Search

Simple Summary: The current devices and methods for dosimetry of personalized photodynamic therapy of tumors have several disadvantages. For instance, they are unable to evaluate the entire tumor volume at great depth, and they lack the capacity to provide an accurate estimate of the effective light dose. Furthermore, they are limited by their ability to evaluate either only the photosensitizer concentration or only the hemoglobin oxygenation at a specific point on the tissue surface or the average value based on the finite tissue volume at little depth. Despite the advances in photodynamic therapy, the development of a precise and dependable methodology to ascertain the optimal light dosage for a given tumor remains a significant challenge. This is due to the pivotal role of light dosage in determining the efficacy of treatment outcomes. Significance: Despite the widespread use of photodynamic therapy in clinical practice, there is a lack of personalized methods for assessing the sufficiency of photodynamic exposure on tumors, depending on tissue parameters that change during light irradiation. This can lead to different treatment results. Aim: The objective of this article was to conduct a comprehensive review of devices and methods employed for the implicit dosimetric monitoring of personalized photodynamic therapy for tumors. Methods: The review included 88 peer-reviewed research articles published between January 2010 and April 2024 that employed implicit monitoring methods, such as fluorescence imaging and diffuse reflectance spectroscopy. Additionally, it encompassed computer modeling methods that are most often and successfully used in preclinical and clinical practice to predict treatment outcomes. The Internet search engine Google Scholar and the Scopus database were used to search the literature for relevant articles. Results: The review analyzed and compared the results of 88 peer-reviewed research articles presenting various methods of implicit dosimetry during photodynamic therapy. The most prominent wavelengths for PDT are in the visible and near-infrared spectral range such as 405, 630, 660, and 690 nm. Conclusions: The problem of developing an accurate, reliable, and easily implemented dosimetry method for photodynamic therapy remains a current problem, since determining the effective light dose for a specific tumor is a decisive factor in achieving a positive treatment outcome. [ABSTRACT FROM AUTHOR]

Simple Summary: The paper describes the statistical analysis of the response of gastric cancer cells and normal cells to broadband terahertz radiation up to 4 THz, both with and without the use of nanostructured contrast agents. A statistical analysis was conducted towards multiple pairwise comparisons in order to identify the narrow THz frequency ranges where the contrast is enhanced and allows a clear identification of tumor cells in the presence of contrast agents. The paper describes the statistical analysis of the response of gastric cancer cells and normal cells to broadband terahertz radiation up to 4 THz, both with and without the use of nanostructured contrast agents. The THz spectroscopy analysis was comparatively performed under the ATR procedure and transmission measurement procedure. The statistical analysis was conducted towards multiple pairwise comparisons, including a support medium (without cells) versus a support medium with nanoparticles, normal cells versus normal cells with nanoparticles, and, respectively, tumor cells versus tumor cells with nanoparticles. When generally comparing the ATR procedure and transmission measurement procedure for a broader frequency domain, the differentiation between normal and tumor cells in the presence of contrast agents is superior when using the ATR procedure. THz contrast enhancement by using contrast agents derived from MRI-related contrast agents leads to only limited benefits and only for narrow THz frequency ranges, a disadvantage for THz medical imaging. [ABSTRACT FROM AUTHOR]

Simple Summary: Esophageal carcinoma (EC) is a major cause of cancer deaths since it is first undetectable in its early stages. Narrow-band imaging (NBI) detects EC more accurately, sensitively, and specifically than white light imaging (WLI), according to many studies. This work uses a color space connected to décor to change WLIs into NBIs, improving early EC identification. The YOLOv5 algorithm was utilized to train WLI and NBI images separately, demonstrating the versatility of sophisticated object identification approaches in medical image analysis. Based on the confusion matrix and the trained model's precision, recall, specificity, accuracy, and F1-score, the model's performance was assessed. The model was trained to reliably identify dysplasia, squamous cell carcinoma (SCC), and polyps, demonstrating a detailed and focused examination of EC pathology for a better understanding. Dysplasia cancer, a pre-cancerous stage that may increase five-year survival, was detected with higher recall and accuracy by the NBI model. Although the NBI and WLI models recognized the polyp identically, the SCC category lowered its accuracy and recall rate. The NBI model had an accuracy of 0.60, 0.81, and 0.66 in dysplasia, SCC, and polyp categories, and recall rates of 0.40, 0.73, and 0.76. In dysplasia, SCC, and polyp categories, the WLI model was 0.56, 0.99, and 0.65 accurate. Additionally, it had recall rates of 0.39, 0.86, and 0.78 in the same categories. The NBI model performs poorly due to a small collection of training pictures. Increasing the dataset can improve performance. Esophageal carcinoma (EC) is a prominent contributor to cancer-related mortality since it lacks discernible features in its first phases. Multiple studies have shown that narrow-band imaging (NBI) has superior accuracy, sensitivity, and specificity in detecting EC compared to white light imaging (WLI). Thus, this study innovatively employs a color space linked to décor to transform WLIs into NBIs, offering a novel approach to enhance the detection capabilities of EC in its early stages. In this study a total of 3415 WLI along with the corresponding 3415 simulated NBI images were used for analysis combined with the YOLOv5 algorithm to train the WLI images and the NBI images individually showcasing the adaptability of advanced object detection techniques in the context of medical image analysis. The evaluation of the model's performance was based on the produced confusion matrix and five key metrics: precision, recall, specificity, accuracy, and F1-score of the trained model. The model underwent training to accurately identify three specific manifestations of EC, namely dysplasia, squamous cell carcinoma (SCC), and polyps demonstrates a nuanced and targeted analysis, addressing diverse aspects of EC pathology for a more comprehensive understanding. The NBI model effectively enhanced both its recall and accuracy rates in detecting dysplasia cancer, a pre-cancerous stage that might improve the overall five-year survival rate. Conversely, the SCC category decreased its accuracy and recall rate, although the NBI and WLI models performed similarly in recognizing the polyp. The NBI model demonstrated an accuracy of 0.60, 0.81, and 0.66 in the dysplasia, SCC, and polyp categories, respectively. Additionally, it attained a recall rate of 0.40, 0.73, and 0.76 in the same categories. The WLI model demonstrated an accuracy of 0.56, 0.99, and 0.65 in the dysplasia, SCC, and polyp categories, respectively. Additionally, it obtained a recall rate of 0.39, 0.86, and 0.78 in the same categories, respectively. The limited number of training photos is the reason for the suboptimal performance of the NBI model which can be improved by increasing the dataset. [ABSTRACT FROM AUTHOR]

Simple Summary: This research revolutionizes dermatological diagnostics by integrating artificial intelligence (AI) and hyperspectral imaging (HSI) to identify skin cancer lesions, particularly Mycosis fungoides (MF). It differentiates MF from conditions such as psoriasis and atopic dermatitis using a dataset of 1659 skin images. This study employs a novel AI algorithm alongside advanced techniques for precise lesion segmentation and classification, moving diagnosis from color to spectral analysis. This non-invasive and efficient method marks a significant advancement in the early and accurate detection of skin malignancies. The model's high performance is validated by its sensitivity, specificity, and accuracy, making it a vital tool in dermatology for identifying skin cancers and inflammatory conditions. This study pioneers the application of artificial intelligence (AI) and hyperspectral imaging (HSI) in the diagnosis of skin cancer lesions, particularly focusing on Mycosis fungoides (MF) and its differentiation from psoriasis (PsO) and atopic dermatitis (AD). By utilizing a comprehensive dataset of 1659 skin images, including cases of MF, PsO, AD, and normal skin, a novel multi-frame AI algorithm was used for computer-aided diagnosis. The automatic segmentation and classification of skin lesions were further explored using advanced techniques, such as U-Net Attention models and XGBoost algorithms, transforming images from the color space to the spectral domain. The potential of AI and HSI in dermatological diagnostics was underscored, offering a noninvasive, efficient, and accurate alternative to traditional methods. The findings are particularly crucial for early-stage invasive lesion detection in MF, showcasing the model's robust performance in segmenting and classifying lesions and its superior predictive accuracy validated through k-fold cross-validation. The model attained its optimal performance with a k-fold cross-validation value of 7, achieving a sensitivity of 90.72%, a specificity of 96.76%, an F1-score of 90.08%, and an ROC-AUC of 0.9351. This study marks a substantial advancement in dermatological diagnostics, thereby contributing significantly to the early and precise identification of skin malignancies and inflammatory conditions. [ABSTRACT FROM AUTHOR]

Simple Summary: Recent advancements in this field have shown continuous improvement, with CAD algorithms enhancing diagnosis accuracy. The study systematically analyzes various aspects of HSI research for skin cancer detection, emphasizing global efforts and the need for more collaboration and data sharing. The results suggest promising advancements in skin cancer identification through HSI, offering potential for early diagnosis and tailored treatments. Integrating CAD algorithms improves diagnostic accuracy. Despite challenges such as limited patient involvement and data availability, HSI offers non-invasive, precise lesion characterization, reducing false positives and negatives. It holds the potential to improve early detection and treatment planning. With ongoing research and collaboration, HSI holds the potential to save lives and improve patient care by facilitating early diagnosis and personalized treatment. Skin cancer, a malignant neoplasm originating from skin cell types including keratinocytes, melanocytes, and sweat glands, comprises three primary forms: basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and malignant melanoma (MM). BCC and SCC, while constituting the most prevalent categories of skin cancer, are generally considered less aggressive compared to MM. Notably, MM possesses a greater capacity for invasiveness, enabling infiltration into adjacent tissues and dissemination via both the circulatory and lymphatic systems. Risk factors associated with skin cancer encompass ultraviolet (UV) radiation exposure, fair skin complexion, a history of sunburn incidents, genetic predisposition, immunosuppressive conditions, and exposure to environmental carcinogens. Early detection of skin cancer is of paramount importance to optimize treatment outcomes and preclude the progression of disease, either locally or to distant sites. In pursuit of this objective, numerous computer-aided diagnosis (CAD) systems have been developed. Hyperspectral imaging (HSI), distinguished by its capacity to capture information spanning the electromagnetic spectrum, surpasses conventional RGB imaging, which relies solely on three color channels. Consequently, this study offers a comprehensive exploration of recent CAD investigations pertaining to skin cancer detection and diagnosis utilizing HSI, emphasizing diagnostic performance parameters such as sensitivity and specificity. [ABSTRACT FROM AUTHOR]

Simple Summary: Video capsule endoscopy (VCE) is a small, patient-friendly tool used for medical imaging, but it lacks narrow band imaging (NBI), which is crucial for detecting various cancers like esophageal cancer (EC). EC is hard to detect early since it often shows no symptoms, leading to a low 5-year survival rate. NBI enhances mucosal features for early cancer identification, but adding it to VCE isn't feasible due to size constraints. This study successfully developed a method to convert traditional white light images (WLI) from VCE into NBI-like images for esophageal examination. The method performed well, with high similarity scores and improved image quality, offering a promising solution for better cancer detection. Video capsule endoscopy (VCE) is increasingly used to decrease discomfort among patients owing to its small size. However, VCE has a major drawback of not having narrow band imaging (NBI) functionality. The current VCE has the traditional white light imaging (WLI) only, which has poor performance in the computer-aided detection (CAD) of different types of cancer compared to NBI. Specific cancers, such as esophageal cancer (EC), do not exhibit any early biomarkers, making their early detection difficult. In most cases, the symptoms are unnoticeable, and EC is diagnosed only in later stages, making its 5-year survival rate below 20% on average. NBI filters provide particular wavelengths that increase the contrast and enhance certain features of the mucosa, thereby enabling early identification of EC. However, VCE does not have a slot for NBI functionality because its size cannot be increased. Hence, NBI image conversion from WLI can presently only be achieved in post-processing. In this study, a complete arithmetic assessment of the decorrelated color space was conducted to generate NBI images from WLI images for VCE of the esophagus. Three parameters, structural similarity index metric (SSIM), entropy, and peak-signal-to-noise ratio (PSNR), were used to assess the simulated NBI images. Results show the good performance of the NBI image reproduction method with SSIM, entropy difference, and PSNR values of 93.215%, 4.360, and 28.064 dB, respectively. [ABSTRACT FROM AUTHOR]

Simple Summary: Meningiomas are the most common primary central nervous system tumors, and surgery is their primary treatment of choice. The surgical intervention aims for the maximum extent of tumor resection whilst preserving the brain's neurological functions. The effectiveness of such intervention thus depends on the capability to intraoperatively discriminate between the meningioma tissue and the surrounding brain white matter and gray matter. This study investigated dielectric contrast as a potential physical biomarker for meningioma discrimination. Dielectric contrast is based on the difference in complex permittivity, a physical property of tissues, measurable by dielectric spectroscopy. This study shows that the dielectric contrast is relevant as a potential physical biomarker to discriminate the meningioma tissue from the surrounding brain tissues. Such a biomarker has a meaningful potential to make a positive impact on the management of neurooncological patients in terms of intraoperative tumor discrimination, as well as future diagnostic or therapeutic applications. The effectiveness of surgical resection of meningioma, the most common primary CNS tumor, depends on the capability to intraoperatively discriminate between the meningioma tissue and the surrounding brain white and gray matter tissues. Aiming to find a potential biomarker based on tissue permittivity, dielectric spectroscopy of meningioma, white matter, and gray matter ex vivo tissues was performed using the open-ended coaxial probe method in the microwave frequency range from 0.5 to 18 GHz. The averages and the 95% confidence intervals of the measured permittivity for each tissue were compared. The results showed the absence of overlap between the 95% confidence intervals for meningioma tissue and for brain white and gray matter, indicating a significant difference in average permittivity (p ≤ 0.05) throughout almost the entire measured frequency range, with the most pronounced contrast found between 2 GHz and 5 GHz. The discovered contrast is relevant as a potential physical biomarker to discriminate meningioma tissue from the surrounding brain tissues by means of permittivity measurement, e.g., for intraoperative meningioma margin assessment. The permittivity models for each tissue, developed in this study as its byproducts, will allow more accurate electromagnetic modeling of brain tumor and healthy tissues, facilitating the development of new microwave-based medical devices and tools. [ABSTRACT FROM AUTHOR]

Simple Summary: The non-enhancing peritumoral area (NEPA) is defined as the hyperintense region in T2-weighted and fluid-attenuated inversion recovery (FLAIR) images surrounding a brain tumor. Analyses of the NEPA provide additional information to the magnetic resonance imaging (MRI) evaluation of the enhancing part of brain tumors. NEPA analyses of central nervous system malignancies with conventional and advanced MRI techniques give interesting highlights in the differential diagnosis of solid contrast-enhanced brain tumors regarding the prognostic stratification and treatment response. Considering the rapid technological changes in advanced MRI imaging, we assume that an update on this topic might be of interest. The non-enhancing peritumoral area (NEPA) is defined as the hyperintense region in T2-weighted and fluid-attenuated inversion recovery (FLAIR) images surrounding a brain tumor. The NEPA corresponds to different pathological processes, including vasogenic edema and infiltrative edema. The analysis of the NEPA with conventional and advanced magnetic resonance imaging (MRI) was proposed in the differential diagnosis of solid brain tumors, showing higher accuracy than MRI evaluation of the enhancing part of the tumor. In particular, MRI assessment of the NEPA was demonstrated to be a promising tool for distinguishing high-grade gliomas from primary lymphoma and brain metastases. Additionally, the MRI characteristics of the NEPA were found to correlate with prognosis and treatment response. The purpose of this narrative review was to describe MRI features of the NEPA obtained with conventional and advanced MRI techniques to better understand their potential in identifying the different characteristics of high-grade gliomas, primary lymphoma and brain metastases and in predicting clinical outcome and response to surgery and chemo-irradiation. Diffusion and perfusion techniques, such as diffusion tensor imaging (DTI), diffusional kurtosis imaging (DKI), dynamic susceptibility contrast-enhanced (DSC) perfusion imaging, dynamic contrast-enhanced (DCE) perfusion imaging, arterial spin labeling (ASL), spectroscopy and amide proton transfer (APT), were the advanced MRI procedures we reviewed. [ABSTRACT FROM AUTHOR]

Simple Summary: Breast-conserving surgery (BCS) is an oncological procedure that allows for the excision of breast cancer with a clear margin of healthy tissue whilst optimising the cosmetic appearance. However, BCS is associated with up to a 19% re-excision rate due to incomplete excision ("positive margins") in the United Kingdom. Optical spectroscopy and the optical imaging of BCS specimens could be a potential intraoperative margin assessment tool to help reduce re-excision rates. Hyperspectral sensing is based on the premise that light illuminating biological tissues undergoes several processes that reflect the composition of tissue, thus helping to differentiate between normal and malignant tissues. This review assesses the current literature on the use of hyperspectral sensing in breast cancer. We divide the techniques into either point-based (spectroscopy) or whole field-of-view (imaging) methods. A comparison is made of the effectiveness of these modalities in discriminating between normal and malignant tissue, and we reflect on the usability of these modalities in the intraoperative setting. Up to 19% of patients require re-excision surgery due to positive margins in breast-conserving surgery (BCS). Intraoperative margin assessment tools (IMAs) that incorporate tissue optical measurements could help reduce re-excision rates. This review focuses on methods that use and assess spectrally resolved diffusely reflected light for breast cancer detection in the intraoperative setting. Following PROSPERO registration (CRD42022356216), an electronic search was performed. The modalities searched for were diffuse reflectance spectroscopy (DRS), multispectral imaging (MSI), hyperspectral imaging (HSI), and spatial frequency domain imaging (SFDI). The inclusion criteria encompassed studies of human in vivo or ex vivo breast tissues, which presented data on accuracy. The exclusion criteria were contrast use, frozen samples, and other imaging adjuncts. 19 studies were selected following PRISMA guidelines. Studies were divided into point-based (spectroscopy) or whole field-of-view (imaging) techniques. A fixed-or random-effects model analysis generated pooled sensitivity/specificity for the different modalities, following heterogeneity calculations using the Q statistic. Overall, imaging-based techniques had better pooled sensitivity/specificity (0.90 (CI 0.76–1.03)/0.92 (CI 0.78–1.06)) compared with probe-based techniques (0.84 (CI 0.78–0.89)/0.85 (CI 0.79–0.91)). The use of spectrally resolved diffusely reflected light is a rapid, non-contact technique that confers accuracy in discriminating between normal and malignant breast tissue, and it constitutes a potential IMA tool. [ABSTRACT FROM AUTHOR]

Simple Summary: To minimize the risk of cancer recurrence, it is crucial for surgeons to assess the resection margins (surface) of surgical specimens during breast-conserving surgeries to determine whether the tumor has been removed entirely. However, this is often not easy and also current techniques lack to aid the surgeons. In our study, we used a hyperspectral imaging technique to overcome this challenge. To assess the resection margins with hyperspectral imaging, a classification model should be developed, which requires a dataset with accurate ground-truth labels. Since it is difficult to establish such a dataset, we introduced a novel approach based on hyperspectral unmixing to enable an accurate correlation between hyperspectral measurements and histology ground-truth labels. Subsequently, we developed a classification model for tumor tissue detection on the lumpectomy resection surface of 189 patients. We achieved a sensitivity of 94% and a specificity of 85%, which demonstrated the potential of hyperspectral imaging for breast-conserving surgeries. (1) Background: Assessing the resection margins during breast-conserving surgery is an important clinical need to minimize the risk of recurrent breast cancer. However, currently there is no technique that can provide real-time feedback to aid surgeons in the margin assessment. Hyperspectral imaging has the potential to overcome this problem. To classify resection margins with this technique, a tissue discrimination model should be developed, which requires a dataset with accurate ground-truth labels. However, establishing such a dataset for resection specimens is difficult. (2) Methods: In this study, we therefore propose a novel approach based on hyperspectral unmixing to determine which pixels within hyperspectral images should be assigned to the ground-truth labels from histopathology. Subsequently, we use this hyperspectral-unmixing-based approach to develop a tissue discrimination model on the presence of tumor tissue within the resection margins of ex vivo breast lumpectomy specimens. (3) Results: In total, 372 measured locations were included on the lumpectomy resection surface of 189 patients. We achieved a sensitivity of 0.94, specificity of 0.85, accuracy of 0.87, Matthew's correlation coefficient of 0.71, and area under the curve of 0.92. (4) Conclusion: Using this hyperspectral-unmixing-based approach, we demonstrated that the measured locations with hyperspectral imaging on the resection surface of lumpectomy specimens could be classified with excellent performance. [ABSTRACT FROM AUTHOR]

Simple Summary: In this study, X-ray Photoelectron Spectroscopy (XPS) is used as a fast technique to characterize the composition of nanosized small extracellular vesicles (EVs), with the aim of evaluating its possible application in cancer screening and diagnosis. This spectroscopic technique is an ideal approach to characterize membranes due to its sensitivity and its penetration depth, in the nanometric range, meaning that the results will mainly correspond to the membrane composition, with little interference from the biomolecules in the EV´s lumen. We propose the use of XPS to analyze the membranes of small extracellular vesicles isolated from bodily fluids of cancer patients and compared them to those of healthy cells and donors. Notably, we have focused our analysis on the N chemical environment of EV membranes, as this can be related to previous complex chemical analyses of lipid and protein contents in EVs. We provide preliminary results on a fast and non-invasive novel strategy, as a method or tool for research-based EV investigations with potential application in cancer screening and monitoring. Small extracellular vesicle (EV) membranes display characteristic protein-lipidic composition features that are related to their cell of origin, providing valuable clues regarding their parental cell composition and real-time state. This could be especially interesting in the case of cancer cell-derived EVs, as their membranes could serve as valuable tools in liquid biopsy applications and to detect changes in the tumor malignancy. X-Ray Photoelectron Spectroscopy (XPS) is a powerful surface analysis technique able to detect every chemical element present, being also sensitive to their chemical environment. Here we explore the use of XPS as a fast technique to characterize EV membrane composition, with possible application in cancer research. Notably, we have focused on the nitrogen environment as an indicator of the relative abundance of pyridine-type bonding, primary, secondary and tertiary amines. Specifically, we have analyzed how tumoral and healthy cells have different nitrogen chemical environments that can indicate the presence or absence of malignancy. In addition, a collection of human serum samples from cancer patients and healthy donors was also analyzed. The differential XPS analysis of EVs collected from patients confirmed that the patterns of amine evolution could be related to markers of cancer disease, opening the possibility of their use as a non-invasive blood biomarker. [ABSTRACT FROM AUTHOR]

Simple Summary: No measures of the changes occurring in the liver in the case of outflow deprivation during hepatectomies are available. This study investigated four indexes obtained using hyperspectral imaging in a pig model, measured in different liver lobes, before and after hepatic vein occlusion. The variations of StO2% and deoxy-Hb were considerably good markers of venous liver congestion. Introduction. The changes occurring in the liver in cases of outflow deprivation have rarely been investigated, and no measurements of this phenomenon are available. This investigation explored outflow occlusion in a pig model using a hyperspectral camera. Methods. Six pigs were enrolled. The right hepatic vein was clamped for 30 min. The oxygen saturation (StO2%), deoxygenated hemoglobin level (de-Hb), near-infrared perfusion (NIR), and total hemoglobin index (THI) were investigated at different time points in four perfused lobes using a hyperspectral camera measuring light absorbance between 500 nm and 995 nm. Differences among lobes at different time points were estimated by mixed-effect linear regression. Results. StO2% decreased over time in the right lateral lobe (RLL, totally occluded) when compared to the left lateral (LLL, outflow preserved) and the right medial (RML, partially occluded) lobes (p < 0.05). De-Hb significantly increased after clamping in RLL when compared to RML and LLL (p < 0.05). RML was further analyzed considering the right portion (totally occluded) and the left portion of the lobe (with an autonomous draining vein). StO2% decreased and de-Hb increased more smoothly when compared to the totally occluded RLL (p < 0.05). Conclusions. The variations of StO2% and deoxy-Hb could be considered good markers of venous liver congestion. [ABSTRACT FROM AUTHOR]

Simple Summary: Bladder cancer is the sixth most incident neoplasm, and the average five-year survival rate drops to 5% for patients with the metastatic disease. Raman spectroscopy probes molecular vibrations by inelastic scattering of laser light. In a retrospective study, control and cancer specimens were prepared from ten human bladder resectates and label-free assessed by Raman microspectroscopic imaging. A data analysis workflow without preprocessing identified the locations and signatures of normal bladder, bladder cancer, necrosis, epithelium, and lipid inclusions. Furthermore, microplastic particles, pigments or carotenoids were detected in 13 out of 20 specimens inside tissue and near tissue margins and their identity was confirmed by spectral library surveys. Hypotheses about the origin of these foreign materials are discussed. Our approach with minimal user interference offers prospects for future clinical translation such as intraoperative tumor detection and label-free material identification in complex matrices. Raman spectroscopy offers label-free assessment of bladder tissue for in vivo and ex vivo intraoperative applications. In a retrospective study, control and cancer specimens were prepared from ten human bladder resectates. Raman microspectroscopic images were collected from whole tissue samples in a closed chamber at 785 nm laser excitation using a 20× objective lens and 250 µm step size. Without further preprocessing, Raman images were decomposed by the hyperspectral unmixing algorithm vertex component analysis into endmember spectra and their abundancies. Hierarchical cluster analysis distinguished endmember Raman spectra that were assigned to normal bladder, bladder cancer, necrosis, epithelium and lipid inclusions. Interestingly, Raman spectra of microplastic particles, pigments or carotenoids were detected in 13 out of 20 specimens inside tissue and near tissue margins and their identity was confirmed by spectral library surveys. Hypotheses about the origin of these foreign materials are discussed. In conclusion, our Raman workflow and data processing protocol with minimal user interference offers advantages for future clinical translation such as intraoperative tumor detection and label-free material identification in complex matrices. [ABSTRACT FROM AUTHOR]

Simple Summary: One of the most important and hardest tasks during cancer diagnostics and operations is to differentiate between cancerous and non-malignant tissues. It is important to detect and remove all cancerous cells, but at the same time cut as little as possible non-malignant tissue. That is why there is now a high demand for methods that will help to recognize margins of cancer. One non-invasive, new and promising method is hyperspectral imaging (HSI) with the use of machine learning (ML). However, the output of the ML models is usually fuzzy, especially when pixel-wise classification methods are used. This research shows that, (1) the impact of particular pre-processing techniques on the performance of tissue recognition depends on the ML model used and its architecture and could be explained by the wavelengths emphasized by the models, and (2) that the application of post-processing can strongly improve performance (both sensitivity and specificity) and the ability to easily differentiate between tissue types. Background: Recent studies have shown that hyperspectral imaging (HSI) combined with neural networks can detect colorectal cancer. Usually, different pre-processing techniques (e.g., wavelength selection and scaling, smoothing, denoising) are analyzed in detail to achieve a well-trained network. The impact of post-processing was studied less. Methods: We tested the following methods: (1) Two pre-processing techniques (Standardization and Normalization), with (2) Two 3D-CNN models: Inception-based and RemoteSensing (RS)-based, with (3) Two post-processing algorithms based on median filter: one applies a median filter to a raw predictions map, the other applies the filter to the predictions map after adopting a discrimination threshold. These approaches were evaluated on a dataset that contains ex vivo hyperspectral (HS) colorectal cancer records of 56 patients. Results: (1) Inception-based models perform better than RS-based, with the best results being 92% sensitivity and 94% specificity; (2) Inception-based models perform better with Normalization, RS-based with Standardization; (3) Our outcomes show that the post-processing step improves sensitivity and specificity by 6.6% in total. It was also found that both post-processing algorithms have the same effect, and this behavior was explained. Conclusion: HSI combined with tissue classification algorithms is a promising diagnostic approach whose performance can be additionally improved by the application of the right combination of pre- and post-processing. [ABSTRACT FROM AUTHOR]

Simple Summary: Non-muscle-invasive bladder cancer (NMIBC) is a complicated locally treated disease with high recurrence rates and high risk of progression to muscle invasive disease. Current standard diagnostic approach based on white light cystoscopy (WLC) is characterized by high false negative rate, excessive invasion of the bladder and high economic burden. Hereby we present the fiber-based attenuated total reflection infrared (ATR IR) spectroscopy study of healthy and cancerous tissue samples taken from 54 patients to delineate normal and tumorous human bladder tissues under ex vivo conditions. Investigation of the tissue samples immediately after surgical operation allows to examine samples in their native conditions and establish their initial structure and chemical composition avoiding sample degradation. Our study reveals that fiber-based ATR IR spectroscopy could be an addition to current endoscopic approach with increased diagnostic accuracy and with some potential to be applied in vivo. Surgical treatment is widely applied curative approach for bladder cancer. White light cystoscopy (WLC) is currently used for intraoperative diagnostics of malignant lesions but has relatively high false-negative rate. Here we represent an application of label free fiber-based attenuated total reflection infrared spectroscopy (ATR IR) for freshly resected human bladder tissue examination for 54 patients. Defined molecular spectral markers allow to identify normal and urothelial carcinoma tissues. While methods of statistical analysis (Hierarchical cluster analysis (HCA) and Principal component analysis (PCA)) used for spectral data treatment allow to discriminate tissue types with 91% sensitivity and 96–98% specificity. In the present study the described method was applied for tissue examination under ex vivo conditions. However, after method validation the equipment could be translated from laboratory studies to in situ or even in vivo studies in operating room. [ABSTRACT FROM AUTHOR]

Simple Summary: The complete resection of the malignant tumor during surgery is crucial for the patient's survival. To date, surgeons have been intraoperatively supported by information from a pathologist, who performs a frozen section analysis of resected tissue. This tumor margin evaluation is subjective, methodologically limited and underlies a selection bias. Hyperspectral imaging (HSI) is an established and rapid supporting technique. New artificial-intelligence-based techniques such as machine learning (ML) can harness this complex spectral information for the verification of cancer tissue. We performed HSI on 23 unstained tissue sections from seven patients with oral squamous cell carcinoma and trained the ML model for tumor recognition resulting in an accuracy of 0.76, a specificity of 0.89 and a sensitivity of 0.48. The results were in accordance with the histopathological annotations and do, therefore, enable the delineation of tumor margins with high speed and accuracy during surgery. The intraoperative assessment of tumor margins of head and neck cancer is crucial for complete tumor resection and patient outcome. The current standard is to take tumor biopsies during surgery for frozen section analysis by a pathologist after H&E staining. This evaluation is time-consuming, subjective, methodologically limited and underlies a selection bias. Optical methods such as hyperspectral imaging (HSI) are therefore of high interest to overcome these limitations. We aimed to analyze the feasibility and accuracy of an intraoperative HSI assessment on unstained tissue sections taken from seven patients with oral squamous cell carcinoma. Afterwards, the tissue sections were subjected to standard histopathological processing and evaluation. We trained different machine learning models on the HSI data, including a supervised 3D convolutional neural network to perform tumor detection. The results were congruent with the histopathological annotations. Therefore, this approach enables the delineation of tumor margins with artificial HSI-based histopathological information during surgery with high speed and accuracy on par with traditional intraoperative tumor margin assessment (Accuracy: 0.76, Specificity: 0.89, Sensitivity: 0.48). With this, we introduce HSI in combination with ML hyperspectral imaging as a potential new tool for intraoperative tumor margin assessment. [ABSTRACT FROM AUTHOR]

Simple Summary: At present, quantitative ultrasound (QUS) is increasingly utilized in cancer imaging. Compared to other imaging modalities that take several days to weeks to assess treatment effectiveness, QUS can provide a rapid treatment evaluation. Its implementation has been documented in characterizing benign versus malignant breast lesions, assessing lymph nodes, predicting and monitoring tumor response, etc. Both preclinical and clinical studies have confirmed that changes in QUS parameters are directly correlated with tissue microstructural alterations. QUS parameters and textural analyses have widely been used to predict and monitor neoadjuvant chemotherapy (NAC) response in locally advanced breast cancer (LABC) patients. Thus, QUS methods have emerged as one of the most useful imaging techniques for the management of several tumor types. Quantitative ultrasound (QUS) is a non-invasive novel technique that allows treatment response monitoring. Studies have shown that QUS backscatter variables strongly correlate with changes observed microscopically. Increases in cell death result in significant alterations in ultrasound backscatter parameters. In particular, the parameters related to scatterer size and scatterer concentration tend to increase in relation to cell death. The use of QUS in monitoring tumor response has been discussed in several preclinical and clinical studies. Most of the preclinical studies have utilized QUS for evaluating cell death response by differentiating between viable cells and dead cells. In addition, clinical studies have incorporated QUS mostly for tissue characterization, including classifying benign versus malignant breast lesions, as well as responder versus non-responder patients. In this review, we highlight some of the important findings of previous preclinical and clinical studies and expand the applicability and therapeutic benefits of QUS in clinical settings. We summarized some recent clinical research advances in ultrasound-based radiomics analysis for monitoring and predicting treatment response and characterizing benign and malignant breast lesions. We also discuss current challenges, limitations, and future prospects of QUS-radiomics. [ABSTRACT FROM AUTHOR]

Simple Summary: Radiation-based anticancer therapies are often ineffective as cancer cells may acquire radioresistance. Moreover, such therapies can cause unwanted side effects, affecting normal tissues. This problem may be remedied by the use of nanometric radiosensitizers. These compounds in non-toxic concentrations improve the effectiveness of therapy while reducing the total dose of radiation used. In this work, we checked the radiosensitizing properties of bimetallic palladium-platinum nanoparticles with different nanostructures: nano-alloy and core-shell. It has been found that nano-alloy structures are more promising radiosensitizers in vitro, and their effect is more satisfactory when using X-rays than high-energy protons. Thus, by appropriately designing the microstructure of nanomaterials, it is possible to modulate their radiosensitizing potential and enhance the therapy′s effectiveness. Nano-sized radiosensitizers can be used to increase the effectiveness of radiation-based anticancer therapies. In this study, bimetallic, ~30 nm palladium-platinum nanoparticles (PdPt NPs) with different nanostructures (random nano-alloy NPs and ordered core-shell NPs) were prepared. Scanning transmission electron microscopy (STEM), selected area electron diffraction (SAED), energy-dispersive X-ray spectroscopy (EDS), zeta potential measurements, and nanoparticle tracking analysis (NTA) were used to provide the physicochemical characteristics of PdPt NPs. Then, PdPt NPs were added to the cultures of colon cancer cells and normal colon epithelium cells in individually established non-toxic concentrations and irradiated with the non-harmful dose of X-rays/protons. Cell viability before and after PdPt NPs-(non) assisted X-ray/proton irradiation was evaluated by MTS (3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium) assay. Flow cytometry was used to assess cell apoptosis. The results showed that PdPt NPs significantly enhanced the effect of irradiation on cancer cells. It was noticed that nano-alloy PdPt NPs possess better radiosensitizing properties compared to PtPd core-shell NPs, and the combined effect against cancer cells was c.a. 10% stronger for X-ray than for proton irradiation. Thus, the radio-enhancing features of differently structured PdPt NPs indicate their potential application for the improvement of the effectiveness of radiation-based anticancer therapies. [ABSTRACT FROM AUTHOR]

Simple Summary: Colorectal cancer (CRC) is the third most common and second most deadly type of cancer worldwide. The early detection and accurate characterization of colorectal cancer are associated with improved outcomes. With increasing emphasis on early cancer detection and the use of minimally invasive microsurgical techniques, the development of accurate diagnostic technologies to identify tumors and define their boundaries in real time becomes of paramount importance. The current research identifies potential cancer biomarkers and associated light-based instrument specifications to manufacture next-generation medical devices for CRC detection. These specifications have been chosen so that miniaturized instruments can be integrated into colonoscopes. Cancer biomarkers are listed to enable the use of complementary biochemical methods to analyze biological tissues. The impact of using next-generation colonoscopes in reducing cancer deaths can be assessed once medical devices are manufactured. Colorectal cancer (CRC) is the third most common and second most deadly type of cancer worldwide. Early detection not only reduces mortality but also improves patient prognosis by allowing the use of minimally invasive techniques to remove cancer while avoiding major surgery. Expanding the use of microsurgical techniques requires accurate diagnosis and delineation of the tumor margins in order to allow complete excision of cancer. We have used diffuse reflectance spectroscopy (DRS) to identify the main optical CRC biomarkers and to optimize parameters for the integration of such technologies into medical devices. A total number of 2889 diffuse reflectance spectra were collected in ex vivo specimens from 47 patients. Short source-detector distance (SDD) and long-SDD fiber-optic probes were employed to measure tissue layers from 0.5 to 1 mm and from 0.5 to 1.9 mm deep, respectively. The most important biomolecules contributing to differentiating DRS between tissue types were oxy- and deoxy-hemoglobin (Hb and HbO2), followed by water and lipid. Accurate tissue classification and potential DRS device miniaturization using Hb, HbO2, lipid and water data were achieved particularly well within the wavelength ranges 350–590 nm and 600–1230 nm for the short-SDD probe, and 380–400 nm, 420–610 nm, and 650–950 nm for the long-SDD probe. [ABSTRACT FROM AUTHOR]

Simple Summary: Major hepatic resections are associated with higher risk of post-operative complications and post-hepatectomy liver failure. After hepatic pedicle clamping, ischemia-reperfusion injury may sometimes lead to post-hepatectomy liver failure. Early detection or, ideally, intra-operative prediction of liver dysfunction or failure would be essential for timely treatment. Hyperspectral imaging (HSI) is a non-invasive technology that detects the relative reflectance of light at a wavelength between 500 and 1000 nm, allowing the quantification of relevant organic compounds. This is the first clinical application of HSI in a monocentric major hepatectomy series, and the reported results suggest that HSI values could be associated with short-term post-operative outcomes. Ischemia-reperfusion injury during major hepatic resections is associated with high rates of post-operative complications and liver failure. Real-time intra-operative detection of liver dysfunction could provide great insight into clinical outcomes. In the present study, we demonstrate the intra-operative application of a novel optical technology, hyperspectral imaging (HSI), to predict short-term post-operative outcomes after major hepatectomy. We considered fifteen consecutive patients undergoing major hepatic resection for malignant liver lesions from January 2020 to June 2021. HSI measures included tissue water index (TWI), organ hemoglobin index (OHI), tissue oxygenation (StO2%), and near infrared (NIR). Pre-operative, intra-operative, and post-operative serum and clinical outcomes were collected. NIR values were higher in unhealthy liver tissue (p = 0.003). StO2% negatively correlated with post-operative serum ALT values (r = −0.602), while ΔStO2% positively correlated with ALP (r = 0.594). TWI significantly correlated with post-operative reintervention and OHI with post-operative sepsis and liver failure. In conclusion, the HSI imaging system is accurate and precise in translating from pre-clinical to human studies in this first clinical trial. HSI indices are related to serum and outcome metrics. Further experimental and clinical studies are necessary to determine clinical value of this technology. [ABSTRACT FROM AUTHOR]

Simple Summary: The application of IL-2 for treating cancer is limited owing to its toxicity and short half-life. Its high binding ability to IL-2 receptor α expands immunosuppressive Treg cells, which represents an undesirable toxicity in cancer immunotherapy. Moreover, its small molecular size is the reason for its short half-life. We solved these problems by using a covalent modification strategy of IL-2 variant IL-2K35C with fatty acid by maleimide chemistry, namely, IL-2K35C-moFA. The experiments performed in vitro and in vivo proved that IL-2K35C-moFA is a novel immunotherapeutic agent with the potential to selectively stimulate CD8+ T cells and NK cells. Compared to IL-2WT, IL-2K35C-moFA showed a specifically reduced potency for the stimulation of Treg cells. Our results also showed that fatty acid conjugation appears to be effective in half-life extension. The combination of selective lymphocyte expansion and its long half-life means IL-2K35C-moFA should be evaluated as a potential human immunotherapeutic in the future. Human interleukin 2 (IL-2) has shown impressive results as a therapeutic agent for cancer. However, IL-2-based cancer therapy is limited by strong Treg amplification owing to its high binding affinity to IL-2 receptor α (IL-2Rα) and its short half-life owing to its small molecular size. In this study, we solved these problems using a covalent modification strategy of the IL-2 variant, i.e., substituting cysteine (C) for lysine (K) at position 35, using octadecanedicarboxylic acid through maleimide chemistry, creating IL-2K35C-moFA. IL-2K35C-moFA was equipotent to human IL-2 wild type (IL-2WT) in activating tumor-killing CD8+ memory effector T cells (CD8+ T) and NK cells bearing the intermediate affinity IL-2 receptors, and less potent than IL-2WT on CTLL-2 cells bearing the high-affinity IL-2 receptors. Moreover, it was shown to support the preferential activation of IL-2 receptor β (IL-2Rβ) over IL-2Rα because of the mutation and fatty acid conjugation. In a B16F10 murine tumor model, IL-2K35C-moFA showed efficacy as a single dose and provided durable immunity for 1 week. Our results support the further evaluation of IL-2K35C-moFA as a novel cancer immunotherapy. [ABSTRACT FROM AUTHOR]

Simple Summary: Early esophageal cancer detection is crucial for patient survival; however, even skilled endoscopists find it challenging to identify the cancer cells in the early stages. In order to categorize and identify esophageal cancer using a single shot multi-box detector, this research presents a novel approach integrating hyperspectral imaging by band selection and a deep learning diagnostic model. Based on the pathological characteristics of esophageal cancer, the pictures were categorized into three stages: normal, dysplasia, and squamous cell carcinoma. The findings revealed that mAP in WLIs, NBIs, and HSI pictures each achieved 80%, 85%, and 84%, respectively. The findings of this investigation demonstrated that HSI contains a greater number of spectral characteristics than white-light imaging, which increases accuracy by roughly 5% and complies with NBI predictions. In this study, the combination of hyperspectral imaging (HSI) technology and band selection was coupled with color reproduction. The white-light images (WLIs) were simulated as narrow-band endoscopic images (NBIs). As a result, the blood vessel features in the endoscopic image became more noticeable, and the prediction performance was improved. In addition, a single-shot multi-box detector model for predicting the stage and location of esophageal cancer was developed to evaluate the results. A total of 1780 esophageal cancer images, including 845 WLIs and 935 NBIs, were used in this study. The images were divided into three stages based on the pathological features of esophageal cancer: normal, dysplasia, and squamous cell carcinoma. The results showed that the mean average precision (mAP) reached 80% in WLIs, 85% in NBIs, and 84% in HSI images. This study′s results showed that HSI has more spectral features than white-light imagery, and it improves accuracy by about 5% and matches the results of NBI predictions. [ABSTRACT FROM AUTHOR]

Simple Summary: In order to realize rapid and complete pre-screening in the pathological examination of gastric cancer and assist in quickly determining the boundary between cancerous and healthy cells, we developed a fast-scanning near-field terahertz (THz) imaging system. This imaging system is compact, low-cost, and easy to operate. THz examination does not need any processing after sectioning, and the diagnostic results are directly displayed in images within one minute. Compared with the H&E staining method, THz imaging diagnosis uses a quantitative absorption coefficient to distinguish cancer tissue and healthy tissue, which makes the automation of the tissue sampling pre-screening procedure possible and will save valuable time to help quickly define cancer tissue. At the same time, the spatial resolution of our near-field imaging system reaches λ/17. Using THz imaging to accurately define the margins of cancer can not only conserve healthy tissues but also minimize the number of second surgical procedures, which would save a lot of additional hospital resources. The distinguishable absorption contrast among healthy gastric tissues, carcinoma in situ and cancer tissues in the THz frequency range is one of the keys to realizing gastric cancer diagnosis by THz imaging. Based on microwave devices and a sub-wavelength fiber, we developed a fast-scanning THz imaging system combined with the principle of surface plasmon resonance enhancement. This imaging system has a near-field λ/17 spatial resolution and imaging S/N ratio as high as 108:1, and the image results are directly displayed within 1 min. We also successfully demonstrated the image diagnostic capability on sliced tissues from eight patients with gastric cancer. The results indicate that THz absorption images can not only clearly distinguish cancer tissue from healthy tissues but also accurately define the margins of cancer. Through a medical statistical study of 40 sliced tissues from 40 patients, we prove that THz imaging can be used as a standalone method to diagnose gastric cancer tissues with a diagnostic specificity and sensitivity of 100%. Compared with the H&E staining method, THz imaging diagnosis makes the automation of tissue-sampling pre-screening procedure possible and assists in quickly determining the boundary between cancerous and healthy tissues. [ABSTRACT FROM AUTHOR]

Simple Summary: The complete removal of cancerous tissue is an important predictor of patient outcomes after surgery, in particular for head and neck cancer surgery. Normally, surgical tissues are examined by a pathologist to determine whether a full removal of the cancer was performed. However, these analyses are often imperfect due to time constraints, limitations in frozen sections, and tissue orientation challenges. Newer intraoperative imaging techniques have shown great promise for increasing both the accuracy and efficiency of this process while in the operating room. This review summarizes and analyzes the current state of the literature on intraoperative imaging in determining cancer margins in head and neck cancers. Surgical margin status is one of the strongest prognosticators in predicting patient outcomes in head and neck cancer, yet head and neck surgeons continue to face challenges in the accurate detection of these margins with the current standard of care. Novel intraoperative imaging modalities have demonstrated great promise for potentially increasing the accuracy and efficiency in surgical margin delineation. In this current study, we collated and analyzed various intraoperative imaging modalities utilized in head and neck cancer to evaluate their use in discriminating malignant from healthy tissues. The authors conducted a systematic database search through PubMed/Medline, Web of Science, and EBSCOhost (CINAHL). Study screening and data extraction were performed and verified by the authors, and more studies were added through handsearching. Here, intraoperative imaging modalities are described, including optical coherence tomography, narrow band imaging, autofluorescence, and fluorescent-tagged probe techniques. Available sensitivities and specificities in delineating cancerous from healthy tissues ranged from 83.0% to 100.0% and 79.2% to 100.0%, respectively, across the different imaging modalities. Many of these initial studies are in small sample sizes, with methodological differences that preclude more extensive quantitative comparison. Thus, there is impetus for future larger studies examining and comparing the efficacy of these intraoperative imaging technologies. [ABSTRACT FROM AUTHOR]

Simple Summary: Beckwith–Wiedemann syndrome (BWS, OMIM 130650) is an inborn overgrowth disorder caused by molecular alterations in chromosome 11p15.5. These molecular changes affect so-called imprinted genes, i.e., genes which underlie a complex regulation which is linked to the parental origin of the gene copy. Thus, either the maternal gene copy is expressed or the paternal, but this balanced regulation is prone to disturbances. In fact, different types of molecular variants have been identified in BWS, resulting in a variable phenotype; thus, it was consented that the syndromic entity was extended to the Beckwith–Wiedemann spectrum (BWSp). Some molecular subgroups of BWSp are associated with an increased embryonic tumor risk and have different likelihoods for specific tumors. Therefore, the precise determination of the molecular subgroup is needed for precise monitoring and treatment, but the molecular diagnostic procedure has several limitations and challenges which have to be considered. Beckwith–Wiedemann syndrome (BWS, OMIM 130650) is a congenital imprinting condition with a heterogenous clinical presentation of overgrowth and an increased childhood cancer risk (mainly nephroblastoma, hepatoblastoma or neuroblastoma). Due to the varying clinical presentation encompassing classical, clinical BWS without a molecular diagnosis and BWS-related phenotypes with an 11p15.5 molecular anomaly, the syndromic entity was extended to the Beckwith–Wiedemann spectrum (BWSp). The tumor risk of up to 30% depends on the molecular subtype of BWSp with causative genetic or epigenetic alterations in the chromosomal region 11p15.5. The molecular diagnosis of BWSp can be challenging for several reasons, including the range of causative molecular mechanisms which are frequently mosaic. The molecular basis of tumor formation appears to relate to stalled cellular differentiation in certain organs that predisposes persisting embryonic cells to accumulate additional molecular defects, which then results in a range of embryonal tumors. The molecular subtype of BWSp not only influences the overall risk of neoplasia, but also the likelihood of specific embryonal tumors. [ABSTRACT FROM AUTHOR]

Simple Summary: Hepatocellular carcinoma is the most common primary liver malignancy in the United States. Macrophages are immune cells that play a critical role in the promotion of cancer growth and configuration of the hepatic microenvironment. Studying intrahepatic macrophages is challenging because they are difficult to isolate, they transform their phenotype upon manipulation, and in vivo animal models poorly replicate the liver microenvironment. Understanding the complexity of intrahepatic macrophage populations is crucial because they coordinate antitumoral immunity. Application of novel methods that can detect immune cell phenotypes, along with their spatial co-localization in situ is critical and timely. The role of tumor-associated macrophages (TAMs) in the pathogenesis of hepatocellular carcinoma (HCC) is poorly understood. Most studies rely on platforms that remove intrahepatic macrophages from the microenvironment prior to evaluation. Cell isolation causes activation and phenotypic changes that may not represent their actual biology and function in situ. State-of-the-art methods provides new strategies to study TAMs without losing the context of tissue architecture and spatial relationship with neighboring cells. These technologies, such as multispectral imaging (e.g., Vectra Polaris), mass cytometry by time-of-flight (e.g., Fluidigm CyTOF), cycling of fluorochromes (e.g., Akoya Biosciences CODEX/PhenoCycler-Fusion, Bruker Canopy, Lunaphore Comet, and CyCIF) and digital spatial profiling or transcriptomics (e.g., GeoMx or Visium, Vizgen Merscope) are being utilized to accurately assess the complex cellular network within the tissue microenvironment. In cancer research, these platforms enable characterization of immune cell phenotypes and expression of potential therapeutic targets, such as PDL-1 and CTLA-4. Newer spatial profiling platforms allow for detection of numerous protein targets, in combination with whole transcriptome analysis, in a single liver biopsy tissue section. Macrophages can also be specifically targeted and analyzed, enabling quantification of both protein and gene expression within specific cell phenotypes, including TAMs. This review describes the workflow of each platform, summarizes recent research using these approaches, and explains the advantages and limitations of each. [ABSTRACT FROM AUTHOR]

Simple Summary: Survival of ovarian cancer patients largely relies on the surgical removal of all cancer cells. To achieve this, good vision is crucial. In this study, we evaluate the ability of hyperspectral imaging to detect ovarian cancer. Images of surgically removed tissue samples of 11 patients were taken and compared to histopathology in order to train machine learning software. For training purposes, only healthy tissues and tissues with high tumor cell content (>50%) were included. In total, 26 tissue samples and 26,446 data points of 10 patients were included. Tissue classification as either tumorous or healthy was evaluated by leave-one-out cross-validation. This resulted in a power of 0.83, a sensitivity of 0.81, a specificity of 0.70 and a Matthew's correlation coefficient of 0.41. To conclude, this study shows that hyperspectral imaging can be used to recognize ovarian cancer. In the future, the technique may enable real-time image guidance during surgery. The most important prognostic factor for the survival of advanced-stage epithelial ovarian cancer (EOC) is the completeness of cytoreductive surgery (CRS). Therefore, an intraoperative technique to detect microscopic tumors would be of great value. The aim of this pilot study is to assess the feasibility of near-infrared hyperspectral imaging (HSI) for EOC detection in ex vivo tissue samples. Images were collected during CRS in 11 patients in the wavelength range of 665–975 nm, and processed by calibration, normalization, and noise filtering. A linear support vector machine (SVM) was employed to classify healthy and tumorous tissue (defined as >50% tumor cells). Classifier performance was evaluated using leave-one-out cross-validation. Images of 26 tissue samples from 10 patients were included, containing 26,446 data points that were matched to histopathology. Tumorous tissue could be classified with an area under the curve of 0.83, a sensitivity of 0.81, a specificity of 0.70, and Matthew's correlation coefficient of 0.41. This study paves the way to in vivo and intraoperative use of HSI during CRS. Hyperspectral imaging can scan a whole tissue surface in a fast and non-contact way. Our pilot study demonstrates that HSI and SVM learning can be used to discriminate EOC from surrounding tissue. [ABSTRACT FROM AUTHOR]

Simple Summary: Good oxygenation of both bowel ends is an important prerequisite to promote anastomotic healing after colorectal resections. Bowel oxygenation is usually assessed clinically. Hyperspectral imaging is a contactless and contrast-free tool that allows quantifying tissue oxygen intraoperatively. In this study, the results of 105 colorectal resections with hyperspectral imaging are reported. Background: A perfusion deficit is a well-defined and intraoperatively influenceable cause of anastomotic leak (AL). Current intraoperative perfusion assessment methods do not provide objective and quantitative results. In this study, the ability of hyperspectral imaging (HSI) to quantify tissue oxygenation intraoperatively was assessed. Methods: 115 patients undergoing colorectal resections were included in the final analysis. Before anastomotic formation, the bowel was extracted and the resection line was outlined and imaged using a compact HSI camera, in order to provide instantaneously quantitative perfusion assessment. Results: In 105 patients, a clear demarcation line was visible with HSI one minute after marginal artery transection, reaching a plateau after 3 min. In 58 (55.2%) patients, the clinically determined transection line matched with HSI. In 23 (21.9%) patients, the clinically established resection margin was entirely within the less perfused area. In 24 patients (22.8%), the HSI transection line had an irregular course and crossed the clinically established resection line. In four cases, HSI disclosed a clinically undetected lesion of the marginal artery. Conclusions: Intraoperative HSI is safe, well reproducible, and does not disrupt the surgical workflow. It also quantifies bowel surface perfusion. HSI might become an intraoperative guidance tool, potentially preventing postoperative complications. [ABSTRACT FROM AUTHOR]

Biliary tract cancers (BTCs) are lethal malignancies currently lacking satisfactory methods for early detection and accurate diagnosis. Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) is a promising diagnostic tool for this disease. In this pilot study, sera samples from 50 BTCs and 30 cholelithiasis patients as well as 30 healthy subjects from a population-based case-control study were randomly grouped into training set (30 BTCs, 20 cholelithiasis and 20 controls), duplicate of training set, and blind set (20 BTCs, 10 cholelithiasis and 10 controls); all sets were analyzed on Immobilized Metal Affinity Capture ProteinChips via SELDI-TOF-MS. A decision tree classifier was built using the training set and applied to all test sets. The classification tree constructed with the 3,400, 4,502, 5,680, 7,598, and 11,242 mass-to-charge ratio (m/z) protein peaks had a sensitivity of 96.7% and a specificity of 85.0% when comparing BTCs with non-cancers. When applied to the duplicate set, sensitivity was 66.7% and specificity was 70.0%, while in the blind set, sensitivity was 95.0% and specificity was 75.0%. Positive predictive values of the training, duplicate, and blind sets were 82.9%, 62.5% and 79.2%, respectively. The agreement of the training and duplicate sets was 71.4% (Kappa = 0.43, u = 3.98, P < 0.01). The coefficient of variations based on 10 replicates of one sample for the five differential peaks were 15.8-68.8% for intensity and 0-0.05% for m/z. These pilot results suggest that serum protein profiling by SELDI-TOF-MS may be a promising approach for identifying BTCs but low assay reproducibility may limit its application in clinical practice. [ABSTRACT FROM AUTHOR]

Simple Summary: Oncologic esophagectomy with gastric conduit reconstruction is the gold standard in the curative treatment of localized esophageal cancer. Anastomotic leakage is one of the most significant postoperative complications and a predictor of increased postoperative mortality and deteriorated quality of life. Adequate perfusion is one of the essential prerequisites for anastomotic healing. An objective evaluation of the perfusion of the gastric conduit can be performed by hyperspectral imaging (HSI) and fluorescence imaging (FI) with indocyanine green (ICG) intraoperatively. The aim of this pilot study was to evaluate the feasibility and the potential of improved outcomes by simultaneous HSI and FI-ICG measurements of the gastric tube during esophagectomy. Background: Novel intraoperative imaging techniques, namely, hyperspectral (HSI) and fluorescence imaging (FI), are promising with respect to reducing severe postoperative complications, thus increasing patient safety. Both tools have already been used to evaluate perfusion of the gastric conduit after esophagectomy and before anastomosis. To our knowledge, this is the first study evaluating both modalities simultaneously during esophagectomy. Methods: In our pilot study, 13 patients, who underwent Ivor Lewis esophagectomy and gastric conduit reconstruction, were analyzed prospectively. HSI and FI were recorded before establishing the anastomosis in order to determine its optimum position. Results: No anastomotic leak occurred during this pilot study. In five patients, the imaging methods resulted in a more peripheral adaptation of the anastomosis. There were no significant differences between the two imaging tools, and no adverse events due to the imaging methods or indocyanine green (ICG) injection occurred. Conclusions: Simultaneous intraoperative application of both modalities was feasible and not time consuming. They are complementary with regard to the ideal anastomotic position and may contribute to better surgical outcomes. The impact of their simultaneous application will be proven in consecutive prospective trials with a large patient cohort. [ABSTRACT FROM AUTHOR]

Simple Summary: Cancer is a leading cause of death worldwide, with the detection of the disease in its early stages, as well as a correct assessment of the tumour margins, being paramount for a successful recovery. While breast cancer is one of most common types of cancer, head and neck cancer is one of the types of cancer with a lower prognosis and poor aesthetic results. Vibrational spectroscopy detects molecular vibrations, being sensitive to different sample compositions, even when the difference was slight. The use of spectroscopy in biomedicine has been extensively explored, since it allows a broader assessment of the biochemical fingerprint of several diseases. This literature review covers the most recent advances in breast and head and neck cancer early diagnosis and intraoperative margin assessment, through Raman and Fourier transform infrared spectroscopies. The rising field of spectral histopathology was also approached. The authors aimed at expounding in a more concise and simple way the challenges faced by clinicians and how vibrational spectroscopy has evolved to respond to those needs for the two types of cancer with the highest potential for improvement regarding an early diagnosis, surgical margin assessment and histopathology. In 2020, approximately 10 million people died of cancer, rendering this disease the second leading cause of death worldwide. Detecting cancer in its early stages is paramount for patients' prognosis and survival. Hence, the scientific and medical communities are engaged in improving both therapeutic strategies and diagnostic methodologies, beyond prevention. Optical vibrational spectroscopy has been shown to be an ideal diagnostic method for early cancer diagnosis and surgical margins assessment, as a complement to histopathological analysis. Being highly sensitive, non-invasive and capable of real-time molecular imaging, Raman and Fourier transform infrared (FTIR) spectroscopies give information on the biochemical profile of the tissue under analysis, detecting the metabolic differences between healthy and cancerous portions of the same sample. This constitutes tremendous progress in the field, since the cancer-prompted morphological alterations often occur after the biochemical imbalances in the oncogenic process. Therefore, the early cancer-associated metabolic changes are unnoticed by the histopathologist. Additionally, Raman and FTIR spectroscopies significantly reduce the subjectivity linked to cancer diagnosis. This review focuses on breast and head and neck cancers, their clinical needs and the progress made to date using vibrational spectroscopy as a diagnostic technique prior to surgical intervention and intraoperative margin assessment. [ABSTRACT FROM AUTHOR]

Simple Summary: Detection of early esophageal cancer is important to improve patient survival, however, early diagnosis of the cancer cells is difficult, even for experienced endoscopists. This article provides a new method by using hyperspectral imaging and a deep learning diagnosis model to classify and diagnose esophageal cancer using a single-shot multibox detector. The accuracy of the results when using an RGB image in WLI was 83% and while using the spectrum data the accuracy was increased to 88%. There was an increase of 5% in WLI. The accuracy of the results when using an RGB image in NBI was 86% and while using the spectrum data the accuracy was increased to 91%. There was an increase of 5% in NBI. This study proves that the accuracy of prediction when using the spectrum data has been significantly improved and the diagnosis of narrow-band endoscopy data is more sensitive than that of white-light endoscopy. This study uses hyperspectral imaging (HSI) and a deep learning diagnosis model that can identify the stage of esophageal cancer and mark the locations. This model simulates the spectrum data from the image using an algorithm developed in this study which is combined with deep learning for the classification and diagnosis of esophageal cancer using a single-shot multibox detector (SSD)-based identification system. Some 155 white-light endoscopic images and 153 narrow-band endoscopic images of esophageal cancer were used to evaluate the prediction model. The algorithm took 19 s to predict the results of 308 test images and the accuracy of the test results of the WLI and NBI esophageal cancer was 88 and 91%, respectively, when using the spectral data. Compared with RGB images, the accuracy of the WLI was 83% and the NBI was 86%. In this study, the accuracy of the WLI and NBI was increased by 5%, confirming that the prediction accuracy of the HSI detection method is significantly improved. [ABSTRACT FROM AUTHOR]

Simple Summary: Non-melanoma skin cancers (NMSC) are the most prevalent neoplasms worldwide, especially in the Caucasian population. Radical surgical excision is considered the therapeutic gold standard, while incomplete tumor removal is invariably associated with recurrence and the need for reintervention. Autofluorescence (AF) spectroscopy has recently been investigated for early diagnosis of NMSC and assessment of tumor margins. Understanding changes in AF intensity in association with peculiar histological features could improve the diagnostic accuracy of skin fluorescence spectroscopy. The main goal of our work was to investigate the correlation between the intensity of cutaneous AF and the histopathological features of NMSC. The intensity of fluorescence emission in tissues obtained from NMSC samples was approximately 4 times lower than that in healthy conditions. In fact, mean AF intensity for BCC group was 4.5 and 4.4 for SCCs, with further variability being recorded according to histopathologic subtypes. Histopathological features such as hyperkeratosis, neoangiogenesis, fibrosis and elastosis are statistically associated with a decrease in AF intensity. Our data suggest that such tissue alterations could be responsible for the difference in AF emission between neoplastic and healthy tissue. These results support the potential application of AF as a useful non-invasive diagnostic tool for NMSCs. Non-melanoma skin cancer (NMSC) is the most common malignant tumor affecting fair-skinned people. Increasing incidence rates of NMSC have been reported worldwide, which is an important challenge in terms of public health management. Surgical excision with pre-operatively identified margins is one of the most common and effective treatment strategies. Incomplete tumor removal is associated with a very high risk of recurrence and re-excision. Biological tissues can absorb and re-emit specific light wave-lengths, detectable through spectrophotometric devices. Such a phenomenon is known as autofluorescence (AF). AF spectroscopy has been widely explored for non-invasive, early detection of NMSC as well as for evaluation of surgical margins before excision. Fluorescence-aided diagnosis is based on differences in spectral characteristics between healthy and neoplastic skin. Understanding the biological basis of such differences and correlating AF intensity to histological features could improve the diagnostic accuracy of skin fluorescence spectroscopy. The primary objective of the present pre-clinical ex vivo study is to investigate the correlation between the intensity of cutaneous AF and the histopathological features of NMSC. Ninety-eight lesions suggestive for NMSCs were radically excised from 75 patients (46 M; 29 F; mean age: 79 years). After removal, 115 specific reference points on lesions ("cases"; 59 on BBC, 53 on SCC and 3 on other lesions) and on peri-lesional healthy skin (controls; 115 healthy skin) were identified and marked through suture stitches. Such reference points were irradiated at 400–430 nm wavelength, and resulting emission AF spectra were acquired through spectrophotometry. For each case, AFIR (autofluorescence intensity ratio) was measured as the ratio between the number of photons emitted at a wavelength ranging between 450 and 700 nm (peak: 500 nm) in the healthy skin and that was captured in the pathological tissue. At the histological level, hyperkeratosis, neoangiogenesis, cellular atypia, epithelial thickening, fibrosis and elastosis were quantified by light microscopy and were assessed through a previously validated grading system. Statistical correlation between histologic variables and AFIR was calculated through linear regression. Spectrometric evaluation was performed on 230 (115 cases + 115 controls) reference points. The mean AFIR for BCC group was 4.5, while the mean AFIR for SCC group was 4.4 and the fluorescence peaks at 500 nm were approximately 4 times lower (hypo-fluorescent) in BCCs and in SCCs than in healthy skin. Histological variables significantly associated with alteration of AFIR were fibrosis and elastosis (p < 0.05), neoangiogenesis, hyperkeratosis and epithelial thickening. Cellular atypia was not significantly associated with alteration of AFIR. The intensity of fluorescence emission in neoplastic tissues was approximately 4 times lower than that in healthy tissues. Histopathological features such as hyperkeratosis, neoangiogenesis, fibrosis and elastosis are statistically associated with the decrease in AFIR. We hypothesize that such tissue alterations are among the possible biophysical and biochemical bases of difference in emission AF between neoplastic and healthy tissue. The results of the present evaluation highlighted the possible usefulness of autofluorescence as diagnostic, non-invasive and real-time tool for NMSCs. [ABSTRACT FROM AUTHOR]

Simple Summary: Due to the non-specific symptoms of brain cancer (e.g., headaches or memory changes), gliomas will often remain undetected until they are larger or at a higher grade, reducing the patient's likelihood of a good clinical outcome. Earlier detection and diagnosis of brain tumours is vital to improve patient outcomes, leading to safer surgeries and earlier treatments. A liquid biopsy for brain tumour would prove revolutionary however in order to detect disease earlier the liquid biopsy needs to be able to detect smaller tumours; and current liquid biopsies perform worse when detecting smaller or earlier stage tumours. Here, for the first time, we confirm the applicability of a validated spectroscopic liquid biopsy approach to detect both small and low-grade gliomas proving that the spectroscopic liquid biopsy approach is insensitive to tumour volume unlike other liquid biopsies. Background: To support the early detection and diagnosis of brain tumours we have developed a rapid, cost-effective and easy to use spectroscopic liquid biopsy based on the absorbance of infrared radiation. We have previously reported highly sensitive results of our approach which can discriminate patients with a recent brain tumour diagnosis and asymptomatic controls. Other liquid biopsy approaches (e.g., based on tumour genetic material) report a lower classification accuracy for early-stage tumours. In this manuscript we present an investigation into the link between brain tumour volume and liquid biopsy test performance. Methods: In a cohort of 177 patients (90 patients with high-grade glioma (glioblastoma (GBM) or anaplastic astrocytoma), or low-grade glioma (astrocytoma, oligoastrocytoma and oligodendroglioma)) tumour volumes were calculated from magnetic resonance imaging (MRI) investigations and patients were split into two groups depending on MRI parameters (T1 with contrast enhancement or T2/FLAIR (fluid-attenuated inversion recovery)). Using attenuated total reflection (ATR)-Fourier transform infrared (FTIR) spectroscopy coupled with supervised learning methods and machine learning algorithms, 90 tumour patients were stratified against 87 control patients who displayed no symptomatic indications of cancer, and were classified as either glioma or non-glioma. Results: Sensitivities, specificities and balanced accuracies were all greater than 88%, the area under the curve (AUC) was 0.98, and cancer patients with tumour volumes as small as 0.2 cm3 were correctly identified. Conclusions: Our spectroscopic liquid biopsy approach can identify gliomas that are both small and low-grade showing great promise for deployment of this technique for early detection and diagnosis. [ABSTRACT FROM AUTHOR]

Simple Summary: Cellulitis is a common complication in Breast Cancer-Related Lymphedema (BCRL); however, it is not known whether cellulitis is associated with the severity and biocompositon of BCRL. This study showed that cellulitis was associated with more excess volume, fat, and lean arm mass. Treatments should aim to prevent cellulitis in BCRL to possibly avoid the condition exacerbating. Cellulitis is a common complication in Breast Cancer-Related Lymphedema (BCRL). The excess amount of fat and lean mass in BCRL is a vital factor in patient stratification, prognosis, and treatments. However, it is not known whether cellulitis is associated with the excess fat and lean mass in BCRL. Therefore, this prospective observational study was designed to fundamentally understand the heterogonous biocomposition of BCRL. For this study, we consecutively enrolled 206 patients with unilateral BCRL between January 2019 and February 2020. All patients underwent Dual-Energy X-Ray Absorptiometry scans, bioimpedance spectroscopy, indocyanine green lymphangiography comprehensive history of potential risk factors, and a clinical exam. Multivariate linear and beta regression models were used to determine the strength of association and margins effect. Sixty-nine patients (33%) had at least one previous episode of cellulitis. Notably, a previous episode of cellulitis was associated with 20 percentage points more excess fat and 10 percentage points more excess lean mass compared to patients without cellulitis (p < 0.05). Moreover, each 1 increase in the patients BMI was associated with a 0.03 unit increase in the fat mass proportion of the lymphedema arm. Cellulitis was associated with more excess fat and lean arm mass in BCRL. In addition, patients BMI affect the proportion of fat mass in the arm. [ABSTRACT FROM AUTHOR]

Simple Summary: Novel intraoperative imaging systems may have a critical impact on intraoperative decision-making. Hyperspectral imaging (HSI) is one of the leading new imaging systems, providing color pictures of tissue characterization, such as oxygen saturation (StO2) and Near-Infrared Perfusion Index (NIR-PI). Several surgical disciplines have already used HSI in detecting tissue perfusion with a proven record of success. To the best of our knowledge, HSI has not been used in the field of pancreatic surgery yet. Hyperspectral imaging (HSI) in abdominal surgery is a new non-invasive tool for the assessment of the perfusion and oxygenation of various tissues and organs. Its benefit in pancreatic surgery is still unknown. The aim of this study was to evaluate the key impact of using HSI during pancreatoduodenectomy (PD). In total, 20 consecutive patients were included. HSI was recorded during surgery as part of a pilot study approved by the local Ethics Committee. Data were collected prospectively with the TIVITA® Tissue System. Intraoperative HS images were recorded before and after gastroduodenal artery (GDA) clamping. We detected four patients with celiac artery stenosis (CAS) caused by a median arcuate ligament (MAL). In two of these patients, a reduction in liver oxygenation (StO2) was discovered 15 and 30 min after GDA clamping. The MAL was divided in these patients. HSI showed an improvement of liver StO2 after MAL division (from 61% to 73%) in one of these two patients. There was no obvious decrease in liver StO2 in the other two patients with CAS. HSI, as a non-invasive procedure, could be helpful in evaluating liver and gastric perfusion during PD, which might assist surgeons in choosing the best surgical approach and in improving patients' outcomes. [ABSTRACT FROM AUTHOR]

Simple Summary: Detection of colorectal carcinoma is performed visually by investigators and is confirmed pathologically. With hyperspectral imaging, an expanded spectral range of optical information is now available for analysis. The acquired recordings were analyzed with a neural network, and it was possible to differentiate tumor from healthy mucosa in colorectal carcinoma by automatic classification with high reliability. Classification and visualization were performed based on a four-layer perceptron neural network. Based on a neural network, the classification of CA or AD resulted in a sensitivity of 86% and a specificity of 95%, by means of leave-one-patient-out cross-validation. Additionally, significant differences in terms of perfusion parameters (e.g., oxygen saturation) related to tumor staging and neoadjuvant therapy were observed. This is a step towards optical biopsy. Currently, colorectal cancer (CRC) is mainly identified via a visual assessment during colonoscopy, increasingly used artificial intelligence algorithms, or surgery. Subsequently, CRC is confirmed through a histopathological examination by a pathologist. Hyperspectral imaging (HSI), a non-invasive optical imaging technology, has shown promising results in the medical field. In the current study, we combined HSI with several artificial intelligence algorithms to discriminate CRC. Between July 2019 and May 2020, 54 consecutive patients undergoing colorectal resections for CRC were included. The tumor was imaged from the mucosal side with a hyperspectral camera. The image annotations were classified into three groups (cancer, CA; adenomatous margin around the central tumor, AD; and healthy mucosa, HM). Classification and visualization were performed based on a four-layer perceptron neural network. Based on a neural network, the classification of CA or AD resulted in a sensitivity of 86% and a specificity of 95%, by means of leave-one-patient-out cross-validation. Additionally, significant differences in terms of perfusion parameters (e.g., oxygen saturation) related to tumor staging and neoadjuvant therapy were observed. Hyperspectral imaging combined with automatic classification can be used to differentiate between CRC and healthy mucosa. Additionally, the biological changes induced by chemotherapy to the tissue are detectable with HSI. [ABSTRACT FROM AUTHOR]

Simple Summary: The glioblastoma is a highly malignant brain tumor with very limited treatment options up to date. Metabolism of this tumor is highly dependent on glucose uptake. It is believed that glioblastoma cells cannot metabolize ketone bodies, which are found in the blood during periods of fasting or ketogenic dieting. According to this hypothesis, dieting could lead to cancer cell starvation. The ERGO2 (Ernaehrungsumstellung bei Patienten mit Rezidiv eines Glioblastoms) MR-spectroscopic imaging subtrial was designed to investigate tumor metabolism in patients randomized to calorically restricted ketogenic diet/intermittent fasting versus standard diet. The non-invasive investigation of tumor metabolism is of high clinical interest. Background: The ERGO2 (Ernaehrungsumstellung bei Patienten mit Rezidiv eines Glioblastoms) MR-spectroscopic imaging (MRSI) subtrial investigated metabolism in patients randomized to calorically restricted ketogenic diet/intermittent fasting (crKD-IF) versus standard diet (SD) in addition to re-irradiation (RT) for recurrent malignant glioma. Intracerebral concentrations of ketone bodies (KB), intracellular pH (pHi), and adenosine triphosphate (ATP) were non-invasively determined. Methods: 50 patients were randomized (1:1): Group A keeping a crKD-IF for nine days, and Group B a SD. RT was performed on day 4–8. Twenty-three patients received an extended MRSI-protocol (1H decoupled 31P MRSI with 3D chemical shift imaging (CSI) and 2D 1H point-resolved spectroscopy (PRESS)) at a 3T scanner at baseline and on day 6. Voxels were selected from the area of recurrent tumor and contralateral hemisphere. Spectra were analyzed with LCModel, adding simulated signals of 3-hydroxybutyrate (βOHB), acetone (Acn) and acetoacetate (AcAc) to the standard basis set. Results: Acn was the only reliably MRSI-detectable KB within tumor tissue and/or normal appearing white matter (NAWM). It was detected in 4/11 patients in Group A and in 0/8 patients in Group B. MRSI results showed no significant depletion of ATP in tumor tissue of patients at day 6 during crKD-IF, even though there were a significant difference in ketone serum levels between Group A and B at day 6 and a decline in fasting glucose in Group A from baseline to day 6. The tumor specific alkaline pHi was maintained. Conclusions: Our metabolic findings suggest that tumor cells maintain energy homeostasis even with reduced serum glucose levels and may generate additional ATP through other sources. [ABSTRACT FROM AUTHOR]

Simple Summary: Targeted therapies are of growing interest to physicians in cancer treatment. These drugs target specific genes and proteins involved in the growth and survival of cancer cells. Brain tumor therapy is complicated by the fact that not all drugs can penetrate the blood brain barrier and reach their target. We explored the non-invasive method, Magnetic Resonance Spectroscopy, for monitoring drug penetration and its effects in live animals bearing brain tumors. We were able to show the presence of the investigated drug in mouse brains and its on-target activity. Background: BAY1436032 is a fluorine-containing inhibitor of the R132X-mutant isocitrate dehydrogenase (mIDH1). It inhibits the mIDH1-mediated production of 2-hydroxyglutarate (2-HG) in glioma cells. We investigated brain penetration of BAY1436032 and its effects using 1H/19F-Magnetic Resonance Spectroscopy (MRS). Methods: 19F-Nuclear Magnetic Resonance (NMR) Spectroscopy was conducted on serum samples from patients treated with BAY1436032 (NCT02746081 trial) in order to analyze 19F spectroscopic signal patterns and concentration-time dynamics of protein-bound inhibitor to facilitate their identification in vivo MRS experiments. Hereafter, 30 mice were implanted with three glioma cell lines (LNT-229, LNT-229 IDH1-R132H, GL261). Mice bearing the IDH-mutated glioma cells received 5 days of treatment with BAY1436032 between baseline and follow-up 1H/19F-MRS scan. All other animals underwent a single scan after BAY1436032 administration. Mouse brains were analyzed by liquid chromatography-mass spectrometry (LC-MS/MS). Results: Evaluation of 1H-MRS data showed a decrease in 2-HG/total creatinine (tCr) ratios from the baseline to post-treatment scans in the mIDH1 murine model. Whole brain concentration of BAY1436032, as determined by 19F-MRS, was similar to total brain tissue concentration determined by Liquid Chromatography with tandem mass spectrometry (LC-MS/MS), with a signal loss due to protein binding. Intratumoral drug concentration, as determined by LC-MS/MS, was not statistically different in models with or without R132X-mutant IDH1 expression. Conclusions: Non-invasive monitoring of mIDH1 inhibition by BAY1436032 in mIDH1 gliomas is feasible. [ABSTRACT FROM AUTHOR]

Early onset breast cancer (EOBC), diagnosed at age ~40 or younger, is associated with a poorer prognosis and higher mortality rate compared to breast cancer diagnosed at age 50 or older. EOBC poses a serious threat to public health and requires in-depth investigation. We studied a cohort comprising 90 Taiwanese female patients, aiming to unravel the underlying mechanisms of EOBC etiopathogenesis. Sequence data generated by whole-exome sequencing (WES) and whole-genome sequencing (WGS) from white blood cell (WBC)–tumor pairs were analyzed to identify somatic missense mutations, copy number variations (CNVs) and germline missense mutations. Similar to regular breast cancer, the key somatic mutation-susceptibility genes of EOBC include TP53 (40% prevalence), PIK3CA (37%), GATA3 (17%) and KMT2C (17%), which are frequently reported in breast cancer; however, the structural protein-coding genes MUC17 (19%), FLG (16%) and NEBL (11%) show a significantly higher prevalence in EOBC. Furthermore, the top 2 genes harboring EOBC germline mutations, MUC16 (19%) and KRT18 (19%), encode structural proteins. Compared to conventional breast cancer, an unexpectedly higher number of EOBC susceptibility genes encode structural proteins. We suspect that mutations in structural proteins may increase physical permeability to environmental hormones and carcinogens and cause breast cancer to occur at a young age. [ABSTRACT FROM AUTHOR]

Oncolytic adenoviruses (Ads) are promising tools for cancer therapeutics. However, most Ad-based therapies utilize Ad type 5 (Ad5), which displays unsatisfying efficiency in clinical trials, partly due to the low expression levels of its primary coxsackievirus and adenovirus receptor (CAR) on tumor cells. Since the efficacy of virotherapy strongly relies on efficient transduction of targeted tumor cells, initial screening of a broad range of viral agents to identify the most effective vehicles is essential. Using a novel Ad library consisting of numerous human Ads representing known Ad species, we evaluated the transduction efficiencies in four breast cancer (BC) cell lines. For each cell line over 20 Ad types were screened in a high-throughput manner based on reporter assays. Ad types featuring high transduction efficiencies were further investigated with respect to the percentage of transgene-positive cells and efficiencies of cellular entry in individual cell lines. Additionally, oncolytic assay was performed to test tumor cell lysis efficacy of selected Ad types. We found that all analyzed BC cell lines show low expression levels of CAR, while alternative receptors such as CD46, DSG-2, and integrins were also detected. We identified Ad3, Ad35, Ad37, and Ad52 as potential candidates for BC virotherapy. [ABSTRACT FROM AUTHOR]

Imaging techniques such as computed tomographies (CT) play a major role in clinical imaging and diagnosis of malignant lesions. In recent years, metal nanoparticle platforms enabled effective payload delivery for several imaging techniques. Due to the possibility of surface modification, metal nanoparticles are predestined to facilitate molecular tumor targeting. In this work, we demonstrate the feasibility of anti-plasma membrane Heat shock protein 70 (Hsp70) antibody functionalized gold nanoparticles (cmHsp70.1-AuNPs) for tumor-specific multimodal imaging. Membrane-associated Hsp70 is exclusively presented on the plasma membrane of malignant cells of multiple tumor entities but not on corresponding normal cells, predestining this target for a tumor-selective in vivo imaging. In vitro microscopic analysis revealed the presence of cmHsp70.1-AuNPs in the cytosol of tumor cell lines after internalization via the endo-lysosomal pathway. In preclinical models, the biodistribution as well as the intratumoral enrichment of AuNPs were examined 24 h after i.v. injection in tumor-bearing mice. In parallel to spectral CT analysis, histological analysis confirmed the presence of AuNPs within tumor cells. In contrast to control AuNPs, a significant enrichment of cmHsp70.1-AuNPs has been detected selectively inside tumor cells in different tumor mouse models. Furthermore, a machine-learning approach was developed to analyze AuNP accumulations in tumor tissues and organs. In summary, utilizing mHsp70 on tumor cells as a target for the guidance of cmHsp70.1-AuNPs facilitates an enrichment and uniform distribution of nanoparticles in mHsp70-expressing tumor cells that enables various microscopic imaging techniques and spectral-CT-based tumor delineation in vivo. [ABSTRACT FROM AUTHOR]

Risk factors including genetic effects are still being investigated in lung adenocarcinoma (LUAD). Mitochondria play an important role in controlling imperative cellular parameters, and anomalies in mitochondrial function might be crucial for cancer development. The mitochondrial genomic aberrations found in lung adenocarcinoma and their associations with cancer development and progression are not yet clearly characterized. Here, we identified a spectrum of mitochondrial genome mutations in early-stage lung adenocarcinoma and explored their association with prognosis and clinical outcomes. Next-generation sequencing was used to reveal the mitochondrial genomes of tumor and conditionally normal adjacent tissues from 61 Stage 1 LUADs. Mitochondrial somatic mutations and clinical outcomes including relapse-free survival (RFS) were analyzed. Patients with somatic mutations in the D-loop region had longer RFS (adjusted hazard ratio, adjHR = 0.18, p = 0.027), whereas somatic mutations in mitochondrial Complex IV and Complex V genes were associated with shorter RFS (adjHR = 3.69, p = 0.012, and adjHR = 6.63, p = 0.002, respectively). The risk scores derived from mitochondrial somatic mutations were predictive of RFS (adjHR = 9.10, 95%CI: 2.93–28.32, p < 0.001). Our findings demonstrated the vulnerability of the mitochondrial genome to mutations and the potential prediction ability of somatic mutations. This research may contribute to improving molecular guidance for patient treatment in precision medicine. [ABSTRACT FROM AUTHOR]

Accumulating evidence suggests that iron homeostasis is disturbed in tumors. We aimed at clarifying the distribution of iron in renal cell carcinoma (RCC). Considering the pivotal role of macrophages for iron homeostasis and their association with poor clinical outcome, we investigated the role of macrophage-secreted iron for tumor progression by applying a novel chelation approach. We applied flow cytometry and multiplex-immunohistochemistry to detect iron-dependent markers and analyzed iron distribution with atomic absorption spectrometry in patients diagnosed with RCC. We further analyzed the functional significance of iron by applying a novel extracellular chelator using RCC cell lines as well as patient-derived primary cells. The expression of iron-regulated genes was significantly elevated in tumors compared to adjacent healthy tissue. Iron retention was detected in tumor cells, whereas tumor-associated macrophages showed an iron-release phenotype accompanied by enhanced expression of ferroportin. We found increased iron amounts in extracellular fluids, which in turn stimulated tumor cell proliferation and migration. In vitro, macrophage-derived iron showed pro-tumor functions, whereas application of an extracellular chelator blocked these effects. Our study provides new insights in iron distribution and iron-handling in RCC. Chelators that specifically scavenge iron in the extracellular space confirmed the importance of macrophage-secreted iron in promoting tumor growth. [ABSTRACT FROM AUTHOR]

Background: Pediatric high-grade gliomas (pHGGs) are facing a very dismal prognosis and representative pre-clinical models are needed for new treatment strategies. Here, we examined the relevance of collecting functional, genomic, and metabolomics data to validate patient-derived models in a hypoxic microenvironment. Methods: From our biobank of pediatric brain tumor-derived models, we selected 11 pHGGs driven by the histone H3.3K28M mutation. We compared the features of four patient tumors to their paired cell lines and mouse xenografts using NGS (next generation sequencing), aCGH (array comparative genomic hybridization), RNA sequencing, WES (whole exome sequencing), immunocytochemistry, and HRMAS (high resolution magic angle spinning) spectroscopy. We developed a multicellular in vitro model of cell migration to mimic the brain hypoxic microenvironment. The live cell technology Incucyte© was used to assess drug responsiveness in variable oxygen conditions. Results: The concurrent 2D and 3D cultures generated from the same tumor sample exhibited divergent but complementary features, recreating the patient intra-tumor complexity. Genomic and metabolomic data described the metabolic changes during pHGG progression and supported hypoxia as an important key to preserve the tumor metabolism in vitro and cell dissemination present in patients. The neurosphere features preserved tumor development and sensitivity to treatment. Conclusion: We proposed a novel multistep work for the development and validation of patient-derived models, considering the immature and differentiated content and the tumor microenvironment of pHGGs. [ABSTRACT FROM AUTHOR]

Diagnosis of oncological diseases remains at the forefront of current medical research. Carbonic Anhydrase IX (CA IX) is a cell surface hypoxia-inducible enzyme functionally involved in adaptation to acidosis that is expressed in aggressive tumors; hence, it can be used as a tumor biomarker. Herein, we propose a nanoscale graphene oxide (GO) platform functionalized with magnetic nanoparticles and a monoclonal antibody specific to the CA IX marker. The GO platforms were prepared by a modified Hummers and Offeman method from exfoliated graphite after several centrifugation and ultrasonication cycles. The magnetic nanoparticles were prepared by a chemical precipitation method and subsequently modified. Basic characterization of GO, such as the degree of oxidation, nanoparticle size and exfoliation, were determined by physical and chemical analysis, including X-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM), energy dispersive X-ray analysis (EDX), and atomic force microscopy (AFM). In addition, the size and properties of the poly-L-lysine-modified magnetic nanoparticles were characterized. The antibody specific to CA IX was linked via an amidic bond to the poly-L-lysine modified magnetic nanoparticles, which were conjugated to GO platform again via an amidic bond. The prepared GO-based platform with magnetic nanoparticles combined with a biosensing antibody element was used for a hypoxic cancer cell targeting study based on immunofluorescence. [ABSTRACT FROM AUTHOR]

Objectives: To conduct a comprehensive glycopeptide spectra analysis of serum between cancer and non-cancer patients to identify early biomarkers of epithelial ovarian cancer (EOC). Methods: Approximately 30,000 glycopeptide peaks were detected from the digested serum glycoproteins of 39 EOC patients (23 early-stage, 16 advanced-stage) and 45 non-cancer patients (27 leiomyoma and ovarian cyst cases, 18 endometrioma cases) by liquid chromatography mass spectrometry (LC–MS). The differential glycopeptide peak spectra were analyzed to distinguish between cancer and non-cancer groups by employing multivariate analysis including principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA) and heat maps. Results: Examined spectral peaks were filtered down to 2281 serum quantitative glycopeptide signatures for differentiation between ovarian cancer and controls using multivariate analysis. The OPLS-DA model using cross-validation parameters R2 and Q2 and score plots of the serum samples significantly differentiated the EOC group from the non-cancer control group. In addition, women with early-stage clear cell carcinoma and endometriomas were clearly distinguished from each other by OPLS-DA as well as by PCA and heat maps. Conclusions: Our study demonstrates the potential of comprehensive serum glycoprotein analysis as a useful tool for ovarian cancer detection. [ABSTRACT FROM AUTHOR]

Constitutional loss-of-function pathogenic variants in the tumor suppressor genes BRCA1 and BRCA2 are widely associated with an elevated risk of ovarian cancer (OC). As only ~15% of OC individuals carry the BRCA1/2 pathogenic variants, the identification of other potential OC-susceptibility genes is of great clinical importance. Here, we established the prevalence and spectrum of the germline pathogenic variants in the BRCA1/2 and 23 other cancer-related genes in a large Polish population of 333 unselected OC cases. Approximately 21% of cases (71/333) carried the BRCA1/2 pathogenic or likely pathogenic variants, with c.5266dup (p.Gln1756Profs*74) and c.3700_3704del (p.Val1234Glnfs*8) being the most prevalent. Additionally, ~6% of women (20/333) were carriers of the pathogenic or likely pathogenic variants in other cancer-related genes, with NBN and CHEK2 reported as the most frequently mutated, accounting for 1.8% (6/333) and 1.2% (4/333) of cases, respectively. We also found ten pathogenic or likely pathogenic variants in other genes: 1/333 in APC, 1/333 in ATM, 2/333 in BLM, 1/333 in BRIP1, 1/333 in MRE11A, 2/333 in PALB2, 1/333 in RAD50, and 1/333 in RAD51C, accounting for 50% of all detected variants in moderate- and low-penetrant genes. Our findings confirmed the presence of the additional OC-associated genes in the Polish population that may improve the personalized risk assessment of these individuals. [ABSTRACT FROM AUTHOR]