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An increasing array of tools is being developed using artificial intelligence (AI) and machine learning (ML) for cancer imaging. The development of an optimal tool requires multidisciplinary engagement to ensure that the appropriate use case is met, as well as to undertake robust development and testing prior to its adoption into healthcare systems. This multidisciplinary review highlights key developments in the field. We discuss the challenges and opportunities of AI and ML in cancer imaging; considerations for the development of algorithms into tools that can be widely used and disseminated; and the development of the ecosystem needed to promote growth of AI and ML in cancer imaging.

Molecular imaging using PET plays an important role for the diagnosis of different diseases. Immuno-PET combines the utilization of PET radionuclides and antibodies/proteins/peptides, presenting a popular method for imaging of different diseases, including cancer. 89Zr is an attractive radiometal for immuno-PET but far from ideal due to its high radiation dose for the patients. However, its long half-life is sometimes advantageous and suitable for labelling antibodies, proteins and other biomolecules. This review compiles opportunities and applications for using 89Zr in preclinical and clinical practice. It contains either results from for its production, preclinical and clinical studies.

Cancer is a life-threatening disease and one of the leading causes of death globally. Currently a good number therapeutic modalities including chemotherapy are used to treat cancer. All these treatments have their own advantages and disadvantages, and hence, usually two or more therapies are combined for effective treatment of cancer. However, the inadequacy of treatment is so high that in the past few decades, deaths due to cancer did not change much, even after development of several new treatment modalities and drugs. Early detection and treatment are the most important factors for ensuring success of cancer therapies. So, people are continuously working on an endeavour to develop superior technologies for the early detection and treatment of cancer. Nanotechnology is opening up a window of new possibilities or opportunities to address this menace. Magnetic nanostructures (MNSs) can play an important role in cancer theranostic applications. This review highlights the different applications of MNSs in cancer theranostic applications. Magnetic nanoparticles have been extensively investigated for cancer imaging, drug targeting and hyperthermia applications. This paper reviews the different cancer detection and treatment modalities using magnetic nanostructures, their advantages over the traditional methods, current status, challenges and future prospects with the help of illustrative examples from recent literature.

Purpose (S)-4-(3-18F-Fluoropropyl)-ʟ-Glutamic Acid ([18F]FSPG) is a radiolabeled non-natural amino acid that is used for positron emission tomography (PET) imaging of the glutamate/cystine antiporter, system xC-, whose expression is upregulated in many cancer types. To increase the clinical adoption of this radiotracer, reliable and facile automated procedures for [18F]FSPG production are required. Here, we report a cassette-based method to produce [18F]FSPG at high radioactivity concentrations from low amounts of starting activity. Procedures An automated synthesis and purification of [18F]FSPG was developed using the GE FASTlab. Optimization of the reaction conditions and automated manipulations were performed by measuring the isolated radiochemical yield of [18F]FSPG and by assessing radiochemical purity using radio-HPLC. Purification of [18F]FSPG was conducted by trapping and washing of the radiotracer on Oasis MCX SPE cartridges, followed by a reverse elution of [18F]FSPG in phosphate-buffered saline. Subsequently, the [18F]FSPG obtained from the optimized process was used to image an animal model of non-small cell lung cancer. Results The optimized protocol produced [18F]FSPG in 38.4 ± 2.6 % radiochemical yield and >96 % radiochemical purity with a molar activity of 11.1 ± 7.7 GBq/μmol. Small alterations, including the implementation of a reverse elution and an altered Hypercarb cartridge, led to significant improvements in radiotracer concentration from 100 MBq/ml. The improved radiotracer concentration allowed for the imaging of up to 20 mice, starting with just 1.5 GBq of [18F]Fluoride. Conclusions We have developed a robust and facile method for [18F]FSPG radiosynthesis in high radiotracer concentration, radiochemical yield, and radiochemical purity. This cassette-based method enabled the production of [18F]FSPG at radioactive concentrations sufficient to facilitate large-scale preclinical experiments with a single prep of starting activity. The use of a cassette-based radiosynthesis on an automated synthesis module routinely used for clinical production makes the method amenable to rapid and widespread clinical translation.

Lung cancer is the most dangerous of all forms of cancer and it has the highest occurrence rate, world over. Early detection of lung cancer is a difficult task. Medical images generated by computer tomography (CT) are being used extensively for lung cancer analysis and research. However, it is essential to have a well-organized image database in order to design a reliable computer-aided diagnosis (CAD) tool. Identifying the most appropriate dataset for the research is another big challenge. The objective of this paper is to present a review of literature related to lung CT datasets. The Cancer Imaging Archive (TCIA) consortium collates different types of cancer datasets and permits public access through an integrated search engine. This survey summarizes the research work done using lung CT datasets maintained by TCIA. The motivation to present this survey was to help the research community in selecting the right lung dataset and to provide a comprehensive summary of the research developments in the field.

Medical imaging quantitative features had once disputable usefulness in clinical studies. Nowadays, advancements in analysis techniques, for instance through machine learning, have enabled quantitative features to be progressively useful in diagnosis and research. Tissue characterisation is improved via the "radiomics" features, whose extraction can be automated. Despite the advances, stability of quantitative features remains an important open problem. As features can be highly sensitive to variations of acquisition details, it is not trivial to quantify stability and efficiently select stable features. In this work, we develop and validate a Computed Tomography (CT) simulator environment based on the publicly available ASTRA toolbox ( www.astra-toolbox.com ). We show that the variability, stability and discriminative power of the radiomics features extracted from the virtual phantom images generated by the simulator are similar to those observed in a tandem phantom study. Additionally, we show that the variability is matched between a multi-center phantom study and simulated results. Consequently, we demonstrate that the simulator can be utilised to assess radiomics features' stability and discriminative power., Scientific Reports, 12 (1), ISSN:2045-2322

Human epidermal growth factor receptor 2 (HER2)-amplified breast cancers are treated using targeted antibodies and kinase inhibitors, but resistance to these therapies leads to systemic tumor recurrence of metastatic disease. Herein, we conducted gene expression analyses of HER2 kinase inhibitor-resistant cell lines as compared to their drug-sensitive counterparts. These data demonstrate the induction of epithelial–mesenchymal transition (EMT), which included enhanced expression of fibroblast growth factor receptor 1 (FGFR1) and axonal guidance molecules known as neuropilins (NRPs). Immunoprecipitation of FGFR1 coupled with mass spectroscopy indicated that FGFR1 forms a physical complex with NRPs, which is enhanced upon induction of EMT. Confocal imaging revealed that FGFR1 and NRP1 predominantly interact throughout the cytoplasm. Along these lines, short hairpin RNA-mediated depletion of NRP1, but not the use of NRP1-blocking antibodies, inhibited FGFR signaling and reduced tumor cell growth in vitro and in vivo. Our results further indicate that NRP1 upregulation during EMT is mediated via binding of the chromatin reader protein, bromodomain containing 4 (BRD4) in the NRP1 proximal promoter region. Pharmacological inhibition of BRD4 decreased NRP1 expression and ablated FGF-mediated tumor cell growth. Overall, our studies indicate that NRPs facilitate aberrant growth factor signaling during EMT-associated drug resistance and metastasis. Pharmacological combination of epigenetic modulators with FGFR-targeted kinase inhibitors may provide improved outcomes for breast cancer patients with drug-resistant metastatic disease.

Copper-64 is a useful theranostic radioisotope that is attracting renewed interest from the nuclear medicine community in the recent times. This study aims to demonstrate the utility of research reactors to produce clinical-grade 64Cu via 63Cu(n,γ)64Cu reaction and use it in the form of [64Cu]CuCl2 as a radiopharmaceutical for PET imaging of cancer in human patients. Copper-64 was produced by irradiation of natural CuO target in a medium flux research reactor. The irradiated target was radiochemically processed and detailed quality control analyses were carried out. Sub-acute toxicity studies were carried out with different doses of Cu in Wistar rats. The biological efficacy of the radiopharmaceutical was established in preclinical setting by biodistribution studies in melanoma tumor bearing mice. After getting regulatory approvals, [64Cu]CuCl2 formulation was clinically used for PET imaging of prostate cancer and glioblastoma patients. Large-scale (~ 30 GBq) production of 64Cu could be achieved in a typical batch and it was adequate for formulation of clinical doses for multiple patients. The radiopharmaceutical met all the purity requirements for administration in human subjects. Studies carried out in animal model showed that the toxicity due to “cold” Cu in clinical dose of [64Cu]CuCl2 for PET scans would be negligible. Clinical PET scans showed satisfactory uptake of the radiopharmaceutical in the primary cancer and its metastatic sites. To the best of our knowledge, this is the first study on use of reactor produced [64Cu]CuCl2 for PET imaging of cancer in human patients. It is envisaged that this route of production of 64Cu would aid towards affordable availability of this radioisotope for widespread clinical use in countries with limited cyclotron facilities.

Quantitative mapping of the in vivo dynamics of cellular metabolism via non-invasive imaging contributes to our understanding of the initiation and progression of diseases associated with dysregulated metabolic processes. Current methods for imaging cellular metabolism are limited by low sensitivities, costs or the use of specialized hardware. Here, we introduce a method that captures the turnover of cellular metabolites by quantifying signal reductions in proton magnetic resonance spectroscopy (MRS) resulting from the replacement of 1H with 2H. The method, which we termed quantitative exchanged-label turnover MRS, only requires deuterium-labelled glucose and standard magnetic resonance imaging scanners, and with a single acquisition provides steady-state information and metabolic rates for several metabolites. We used the method to monitor glutamate, glutamine, γ-aminobutyric acid and lactate in the brains of unaffected and glioma-bearing rats following the administration of 2H2-labelled glucose and 2H3-labelled acetate. Quantitative exchanged-label turnover MRS should broaden the applications of routine 1H MRS. A method that quantifies signal reductions in proton magnetic resonance spectroscopy resulting from the replacement of 1H with 2H after the administration of a deuterated substrate can be used to monitor the turnover of cellular metabolites in vivo.

Background Recurrence is the major cause of mortality in patients with resected HCC. However, without a standard approach to evaluate prognosis, it is difficult to select candidates for additional therapy. Methods A total of 201 patients with HCC who were followed up for at least 5 years after curative hepatectomy were enrolled in this retrospective, multicentre study. A total of 3144 radiomics features were extracted from preoperative MRI. The random forest method was used for radiomics signature building, and five-fold cross-validation was applied. A radiomics model incorporating the radiomics signature and clinical risk factors was developed. Results Patients were divided into survivor (n = 97) and non-survivor (n = 104) groups based on the 5-year survival after surgery. The 30 most survival-related radiomics features were selected for the radiomics signature. Preoperative AFP and AST were integrated into the model as independent clinical risk factors. The model demonstrated good calibration and satisfactory discrimination, with a mean AUC of 0.9804 and 0.7578 in the training and validation sets, respectively. Conclusions This radiomics model is a valid method to predict 5-year survival in patients with HCC and may be used to identify patients for clinical trials of perioperative therapies and for additional surveillance.

To evaluate the performance of a deep convolutional neural network (DCNN) in detecting local tumor progression (LTP) after tumor ablation for hepatocellular carcinoma (HCC) on follow-up arterial phase CT images. The DCNN model utilizes three-dimensional (3D) patches extracted from three-channel CT imaging to detect LTP. We built a pipeline to automatically produce a bounding box localization of pathological regions using a 3D-CNN trained for classification. The performance metrics of the 3D-CNN prediction were analyzed in terms of accuracy, sensitivity, specificity, positive predictive value (PPV), area under the receiver operating characteristic curve (AUC), and average precision. We included 34 patients with 49 LTP lesions and randomly selected 40 patients without LTP. A total of 74 patients were randomly divided into three sets: training (n = 48; LTP: no LTP = 21:27), validation (n = 10; 5:5), and test (n = 16; 8:8). When used with the test set (160 LTP positive patches, 640 LTP negative patches), our proposed 3D-CNN classifier demonstrated an accuracy of 97.59%, sensitivity of 96.88%, specificity of 97.65%, and PPV of 91.18%. The AUC and precision–recall curves showed high average precision values of 0.992 and 0.96, respectively. LTP detection on follow-up CT images after tumor ablation for HCC using a DCNN demonstrated high accuracy and incorporated multichannel registration.

A brain tumour is a mass or cluster of abnormal cells in the brain, which has the possibility of becoming life-threatening because of its ability to invade neighbouring tissues and also form metastases. An accurate diagnosis is essential for successful treatment planning and magnetic resonance imaging is the principal imaging modality for diagnostic of brain tumours and their extent. Deep Learning methods in computer vision applications have shown significant improvement in recent years, most of which can be credited to the fact that a sizeable amount of data is available to train models on, and the improvements in the model architectures yielding better approximations in a supervised setting. Classifying tumours using such deep learning methods has made significant progress with the availability of open datasets with reliable annotations. Typically those methods are either 3D models, which use 3D volumetric MRIs or even 2D models considering each slice separately. However, by treating one spatial dimension separately or by considering the slices as a sequence of images over time, spatiotemporal models can be employed as "spatiospatial" models for this task. These models have the capabilities of learning specific spatial and temporal relationship, while reducing computational costs. This paper uses two spatiotemporal models, ResNet (2+1)D and ResNet Mixed Convolution, to classify different types of brain tumours. It was observed that both these models performed superior to the pure 3D convolutional model, ResNet18. Furthermore, it was also observed that pre-training the models on a different, even unrelated dataset before training them for the task of tumour classification improves the performance. Finally, Pre-trained ResNet Mixed Convolution was observed to be the best model in these experiments, achieving a macro F1-score of 0.9345 and a test accuracy of 96.98%, while at the same time being the model with the least computational cost.

This paper proposes a deep learning-based patch label denoising method (LossDiff) for improving the classification of whole-slide images of cancer using a convolutional neural network (CNN). Automated whole-slide image classification is often challenging, requiring a large amount of labeled data. Pathologists annotate the region of interest by marking malignant areas, which pose a high risk of introducing patch-based label noise by involving benign regions that are typically small in size within the malignant annotations, resulting in low classification accuracy with many Type-II errors. To overcome this critical problem, this paper presents a simple yet effective method for noisy patch classification. The proposed method, validated using stomach cancer images, provides a significant improvement compared to other existing methods in patch-based cancer classification, with accuracies of 98.81%, 97.30% and 89.47% for binary, ternary, and quaternary classes, respectively. Moreover, we conduct several experiments at different noise levels using a publicly available dataset to further demonstrate the robustness of the proposed method. Given the high cost of producing explicit annotations for whole-slide images and the unavoidable error-prone nature of the human annotation of medical images, the proposed method has practical implications for whole-slide image annotation and automated cancer diagnosis.

Patients with a biopsy diagnosis of ductal carcinoma in situ (DCIS) may be diagnosed with invasive breast cancer after excision. We evaluated the preoperative clinical and imaging predictors of DCIS that were associated with an upgrade to invasive carcinoma on final pathology and also compared the diagnostic performance of various statistical models. We reviewed the medical records; including mammography, ultrasound (US), and magnetic resonance imaging (MRI) findings; of 644 patients who were preoperatively diagnosed with DCIS and who underwent surgery between January 2012 and September 2018. Logistic regression and three machine learning methods were applied to predict DCIS underestimation. Among 644 DCIS biopsies, 161 (25%) underestimated invasive breast cancers. In multivariable analysis, suspicious axillary lymph nodes (LNs) on US (odds ratio [OR], 12.16; 95% confidence interval [CI], 4.94–29.95; P P = 0.003) were associated with underestimation. Cases with biopsy performed using vacuum-assisted biopsy (VAB) (OR, 0.42; 95% CI, 0.27–0.65; P P = 0.021) and MRI (OR, 0.29; 95% CI, 0.09–0.94; P = 0.037) were less likely to be upgraded. No significant differences in performance were observed between logistic regression and machine learning models. Our results suggest that biopsy device, high nuclear grade, presence of suspicious axillary LN on US, and lesion size on mammography or MRI were independent predictors of DCIS underestimation.

The purpose of this study was to compare the ability of 18F-FDG PET/CT and contrast-enhanced MRI (CEMRI) to detect and grade venous tumour thrombus (VTT) and venous bland thrombus (VBT) in RCC and assess invasion of the venous wall by VTT. The PET/CT and CEMRI data of 41 patients with RCC were retrieved. The difference in maximum standardized uptake value (SUVmax) between VTT and VBT was analysed. According to their pathological diagnosis, the patients were divided into those with and without venous wall invasion. The PET/CT and CEMRI features, including the SUVmax of the primary lesion and VTT, maximum venous diameter, complete occlusion of the vein by VTT, and VTT morphology, were compared between the two groups. All 41 patients had VTT, and eleven of the 41 patients had VBT. The mean SUVmax of the VTT (6.33 ± 4. 68, n = 41) was significantly higher than that of the VBT (1.37 ± 0.26, n = 11; P 18F-FDG PET/CT, and all 11 were diagnosed by CEMRI. Both 18F-FDG PET/CT and CEMRI can effectively detect VTT and distinguish VTT from VBT. 18F-FDG PET/CT is less effective in grading VTT than CEMRI. Complete venous occlusion by VTT indicates venous wall invasion.

Glioma is the most common form of malignant primary brain tumours in adults. Their highly invasive nature makes the disease incurable to date, emphasizing the importance of better understanding the mechanisms driving glioma invasion. Glial fibrillary acidic protein (GFAP) is an intermediate filament protein that is characteristic for astrocyte- and neural stem cell-derived gliomas. Glioma malignancy is associated with changes in GFAP alternative splicing, as the canonical isoform GFAPα is downregulated in higher-grade tumours, leading to increased dominance of the GFAPδ isoform in the network. In this study, we used intravital imaging and anex vivobrain slice invasion model. We show that the GFAPδ and GFAPα isoforms differentially regulate the tumour dynamics of glioma cells. Depletion of either isoform increases the migratory capacity of glioma cells. Remarkably, GFAPδ-depleted cells migrate randomly through the brain tissue, whereas GFAPα-depleted cells show a directionally persistent invasion into the brain parenchyma. This study shows that distinct compositions of the GFAP-network lead to specific migratory dynamics and behaviours of gliomas.

Multiple myeloma (MM) is a cancer of bone marrow (BM) plasma cells, which is increasingly treatable but still incurable. In 90% of MM patients, severe osteolysis results from pathological interactions between MM cells and the bone microenvironment. Delineating specific molecules and pathways for their role in cancer supportive interactions in the BM is vital for developing new therapies. Very Late Antigen 4 (VLA4, integrin α4β1) is a key player in cell–cell adhesion and signaling between MM and BM cells. We evaluated a VLA4 selective near infrared fluorescent probe, LLP2A-Cy5, for in vitro and in vivo optical imaging of VLA4. Furthermore, two VLA4-null murine 5TGM1 MM cell (KO) clones were generated by CRISPR/Cas9 knockout of the Itga4 (α4) subunit, which induced significant alterations in the transcriptome. In contrast to the VLA4+ 5TGM1 parental cells, C57Bl/KaLwRij immunocompetent syngeneic mice inoculated with the VLA4-null clones showed prolonged survival, reduced medullary disease, and increased extramedullary disease burden. The KO tumor foci showed significantly reduced uptake of LLP2A-Cy5, confirming in vivo specificity of this imaging agent. This work provides new insights into the pathogenic role of VLA4 in MM, and evaluates an optical tool to measure its expression in preclinical models.

Background The fatty acid (FA) composition of phosphatidylinositols (PIs) is tightly regulated in mammalian tissue since its disruption impairs normal cellular functions. We previously found its significant alteration in breast cancer by using matrix-assisted laser desorption and ionisation imaging mass spectrometry (MALDI-IMS). Methods We visualised the histological distribution of PIs containing different FAs in 65 primary breast cancer tissues using MALDI-IMS and investigated its association with clinicopathological features and gene expression profiles. Results Normal ductal cells (n = 7) predominantly accumulated a PI containing polyunsaturated FA (PI-PUFA), PI(18:0/20:4). PI(18:0/20:4) was replaced by PIs containing monounsaturated FA (PIs-MUFA) in all non-invasive cancer cells (n = 12). While 54% of invasive cancer cells (n = 27) also accumulated PIs-MUFA, 46% of invasive cancer cells (n = 23) accumulated the PIs-PUFA, PI(18:0/20:3) and PI(18:0/20:4). The accumulation of PI(18:0/20:3) was associated with higher incidence of lymph node metastasis and activation of the PD-1-related immune checkpoint pathway. Fatty acid-binding protein 7 was identified as a putative molecule controlling PI composition. Conclusions MALDI-IMS identified PI composition associated with invasion and nodal metastasis of breast cancer. The accumulation of PI(18:0/20:3) could affect the PD-1-related immune checkpoint pathway, although its precise mechanism should be further validated.

Background Fulfilling the promise of cancer immunotherapy requires novel predictive biomarkers to characterise the host immune microenvironment. Deciphering the complexity of immune cell interactions requires an automated multiplex approach to histological analysis of tumour sections. We tested a new automatic approach to select tissue and quantify the frequencies of cell-cell spatial interactions occurring in the PD1/PD-L1 pathway, hypothesised to reflect immune escape in oropharyngeal squamous cell carcinoma (OPSCC). Methods Single sections of diagnostic biopsies from 72 OPSCC patients were stained using multiplex immunofluorescence (CD8, PD1, PD-L1, CD68). Following multispectral scanning and automated regions-of-interest selection, the Hypothesised Interaction Distribution (HID) method quantified spatial proximity between cells. Method applicability was tested by investigating the prognostic significance of co-localised cells (within 30 μm) in patients stratified by HPV status. Results High frequencies of proximal CD8+ and PD-L1+ (HR 2.95, p = 0.025) and PD1+ and PD-L1+ (HR 2.64, p = 0.042) cells were prognostic for poor overall survival in patients with HPV negative OPSCC (n = 31). Conclusion The HID method can quantify spatial interactions considered to reflect immune escape and generate prognostic information in OPSCC. The new automated approach is ready to test in additional cohorts and its applicability should be explored in research and clinical studies.

Accurate cancer detection and diagnosis is of utmost importance for reliable drug-response prediction. Successful cancer characterization relies on both genetic analysis and histological scans from tumor biopsies. It is known that the cytoskeleton is significantly altered in cancer, as cellular structure dynamically remodels to promote proliferation, migration, and metastasis. We exploited these structural differences with supervised feature extraction methods to introduce an algorithm that could distinguish cancer from non-cancer cells presented in high-resolution, single cell images. In this paper, we successfully identified the features with the most discriminatory power to successfully predict cell type with as few as 100 cells per cell line. This trait overcomes a key barrier of machine learning methodologies: insufficient data. Furthermore, normalizing cell shape via microcontact printing on self-assembled monolayers enabled better discrimination of cell lines with difficult-to-distinguish phenotypes. Classification accuracy remained robust as we tested dissimilar cell lines across various tissue origins, which supports the generalizability of our algorithm.

To investigate the value of the star-VIBE sequence in dynamic contrast-enhanced magnetic resonance imaging of esophageal carcinoma under free breathing conditions. From February 2019 to June 2020, 60 patients with esophageal carcinoma were prospectively enrolled to undergo dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) with the K-space golden-angle radial stack-of-star acquisition scheme (star-VIBE) sequence (Group A) or conventional 3D volumetric-interpolated breath-hold examination (3D-VIBE) sequence (Group B), completely randomized grouping. The image quality of DCE-MRI was subjectively evaluated at five levels and objectively evaluated according to the image signal-to-noise ratio (SNR) and contrast-noise ratio (CNR). The DCE-MRI parameters of volume transfer constant (Ktrans), rate constant (Kep) and vascular extracellular volume fraction (Ve) were calculated using the standard Tofts double-compartment model in the post-perfusion treatment software TISSUE 4D (Siemens). Each group included 30 randomly selected cases. There was a significant difference in subjective classification between the groups (35.90 vs 25.10, p = 0.009). The study showed that both the SNR and CNR of group A were significantly higher than those of group B (p = 0.004 and p > 0.05). The star-VIBE sequence can be applied in DCE-MRI examination of esophageal carcinoma, which can provide higher image quality than the conventional 3D-VIBE sequence in the free breathing state.

Metastasis is the main cause of carcinoma-related death, yet we know little about how it initiates due to our inability to visualize stochastic invasion events. Classical models suggest that cells accumulate mutations that first drive formation of a primary mass, and then downregulate epithelia-specific genes to cause invasion and metastasis. Here, using transparent zebrafish epidermis to model simple epithelia, we can directly image invasion. We find that KRas-transformation, implicated in early carcinogenesis steps, directly drives cell invasion by hijacking a process epithelia normally use to promote death—cell extrusion. Cells invading by basal cell extrusion simultaneously pinch off their apical epithelial determinants, endowing new plasticity. Following invasion, cells divide, enter the bloodstream, and differentiate into stromal, neuronal-like, and other cell types. Yet, only invading KRasV12 cells deficient in p53 survive and form internal masses. Together, we demonstrate that KRas-transformation alone causes cell invasion and partial dedifferentiation, independently of mass formation., The ability to visualise stochastic invasion events is limited in murine models of metastatic cancers. Here the authors use a transparent zebrafish epidermis model to follow the invasion events of K-Ras transformed epithelial cells and show that these cells invade through basal cell extrusion.

Radiotherapy requires the target area and the organs at risk to be contoured on the CT image of the patient. During the process of organs-at-Risk (OAR) of the chest and abdomen, the doctor needs to contour at each CT image. The delineations of large and varied shapes are time-consuming and laborious. This study aims to evaluate the results of two automatic contouring softwares on OARs definition of CT images of lung cancer and rectal cancer patients. The CT images of 15 patients with rectal cancer and 15 patients with lung cancer were selected separately, and the organs at risk were manually contoured by experienced physicians as reference structures. And then the same datasets were automatically contoured based on AiContour (version 3.1.8.0, Manufactured by Linking MED, Beijing, China) and Raystation (version 4.7.5.4, Manufactured by Raysearch, Stockholm, Sweden) respectively. Deep learning auto-segmentations and Atlas were respectively performed with AiContour and Raystation. Overlap index (OI), Dice similarity index (DSC) and Volume difference (Dv) were evaluated based on the auto-contours, and independent-sample t-test analysis is applied to the results. The results of deep learning auto-segmentations on OI and DSC were better than that of Atlas with statistical difference. There was no significant difference in Dv between the results of two software. With deep learning auto-segmentations, auto-contouring results of most organs in the chest and abdomen are good, and with slight modification, it can meet the clinical requirements for planning. With Atlas, auto-contouring results in most OAR is not as good as deep learning auto-segmentations, and only the auto-contouring results of some organs can be used clinically after modification.

Tumor response to radiotherapy or ferroptosis is closely related to hydroxyl radical (•OH) production. Noninvasive imaging of •OH fluctuation in tumors can allow early monitoring of response to therapy, but is challenging. Here, we report the optimization of a diene electrochromic material (1-Br-Et) as a •OH-responsive chromophore, and use it to develop a near-infrared ratiometric fluorescent and photoacoustic (FL/PA) bimodal probe for in vivo imaging of •OH. The probe displays a large FL ratio between 780 and 1113 nm (FL780/FL1113), but a small PA ratio between 755 and 905 nm (PA755/PA905). Oxidation of 1-Br-Et by •OH decreases the FL780/FL1113 while concurrently increasing the PA755/PA905, allowing the reliable monitoring of •OH production in tumors undergoing erastin-induced ferroptosis or radiotherapy., The hydroxyl radical is generated during radiotherapy and ferroptosis and accurate imaging of this reactive oxygen species may permit the monitoring of response to therapy. Here, the authors develop a ratiometric probe for accurate imaging of hydroxyl radical generation in vivo.

SummaryThe National Cancer Imaging Translational Accelerator (NCITA) is creating a UK national coordinated infrastructure for accelerated translation of imaging biomarkers for clinical use. Through the development of standardised protocols, data integration tools and ongoing training programmes, NCITA provides a unique scalable infrastructure for imaging biomarker qualification using multicentre clinical studies.

Currently, intraoperative guidance tools used for brain tumor resection assistance during surgery have several limitations. Hyperspectral (HS) imaging is arising as a novel imaging technique that could offer new capabilities to delineate brain tumor tissue in surgical-time. However, the HS acquisition systems have some limitations regarding spatial and spectral resolution depending on the spectral range to be captured. Image fusion techniques combine information from different sensors to obtain an HS cube with improved spatial and spectral resolution. This paper describes the contributions to HS image fusion using two push-broom HS cameras, covering the visual and near-infrared (VNIR) [400–1000 nm] and near-infrared (NIR) [900–1700 nm] spectral ranges, which are integrated into an intraoperative HS acquisition system developed to delineate brain tumor tissue during neurosurgical procedures. Both HS images were registered using intensity-based and feature-based techniques with different geometric transformations to perform the HS image fusion, obtaining an HS cube with wide spectral range [435–1638 nm]. Four HS datasets were captured to verify the image registration and the fusion process. Moreover, segmentation and classification methods were evaluated to compare the performance results between the use of the VNIR and NIR data, independently, with respect to the fused data. The results reveal that the proposed methodology for fusing VNIR–NIR data improves the classification results up to 21% of accuracy with respect to the use of each data modality independently, depending on the targeted classification problem.

RECIST v1.1 has limitations in evaluating progression. We assessed Dynamic Constrast Enhanced Computed Tomography (DCE-CT) identified Blood Volume (BV) for the evaluation of progressive disease (PD) in patients with metastatic renal cell carcinoma (mRCC). BV was quantified prospectively at baseline, after one month, then every three months until PD. Relative changes (ΔBV) were assessed at each timepoint compared with baseline values. The primary endpoint was Time to PD (TTP), the secondary endpoint was Time to the scan prior to PD (PDminus1). Cox proportional hazard models adjusted ΔBV for treatments and International mRCC Database Consortium factors. A total of 62 patients had analyzable scans at the PD timepoint. Median BV was 23.92 mL × 100 g−1 (range 4.40–399.04) at PD and 26.39 mL × 100 g−1 (range 8.70–77.44) at PDminus1. In the final multivariate analysis higher ΔBV was statistically significantly associated with shorter Time to PD, HR 1.11 (95% CI 1.07–1.15, P P = 0.031). In conclusion, DCE-CT identified BV is a new image-based biomarker of therapy progression in patients with mRCC.

Radiomics is a promising technique for discovering image based biomarkers of therapy response in cancer. Reproducibility of radiomics features is a known issue that is addressed by the image biomarker standardisation initiative (IBSI), but it remains challenging to interpret previously published radiomics signatures. This study investigates the reproducibility of radiomics features calculated with two widely used radiomics software packages (IBEX, MaZda) in comparison to an IBSI compliant software package (PyRadiomics). Intensity histogram, shape and textural features were extracted from 334 diffusion weighted magnetic resonance images of 59 head and neck cancer (HNC) patients from the PREDICT-HN observational radiotherapy study. Based on name and linear correlation, PyRadiomics shares 83 features with IBEX and 49 features with MaZda, a sub-set of well correlated features are considered reproducible (IBEX: 15 features, MaZda: 18 features). We explore the impact of including non-reproducible radiomics features in a HNC radiotherapy response model. It is possible to classify equivalent patient groups using radiomic features from either software, but only when restricting the model to reliable features using a correlation threshold method. This is relevant for clinical biomarker validation trials as it provides a framework to assess the reproducibility of reported radiomic signatures from existing trials.

More widespread use of positron emission tomography (PET) imaging is limited by its high cost and radiation dose. Reductions in PET scan time or radiotracer dosage typically degrade diagnostic image quality (DIQ). Deep-learning-based reconstruction may improve DIQ, but such methods have not been clinically evaluated in a realistic multicenter, multivendor environment. In this study, we evaluated the performance and generalizability of a deep-learning-based image-quality enhancement algorithm applied to fourfold reduced-count whole-body PET in a realistic clinical oncologic imaging environment with multiple blinded readers, institutions, and scanner types. We demonstrate that the low-count-enhanced scans were noninferior to the standard scans in DIQ (p p p-value=0.59) and had high correlation (lowest CCC = 0.94). Thus, we demonstrated that deep learning can be used to restore diagnostic image quality and maintain SUV accuracy for fourfold reduced-count PET scans, with interscan variations in lesion depiction, lower than intra- and interreader variations. This method generalized to an external validation set of clinical patients from multiple institutions and scanner types. Overall, this method may enable either dose or exam-duration reduction, increasing safety and lowering the cost of PET imaging.

Both histologic subtypes and tumor mutation burden (TMB) represent important biomarkers in lung cancer, with implications for patient prognosis and treatment decisions. Typically, TMB is evaluated by comprehensive genomic profiling but this requires use of finite tissue specimens and costly, time-consuming laboratory processes. Histologic subtype classification represents an established component of lung adenocarcinoma histopathology, but can be challenging and is associated with substantial inter-pathologist variability. Here we developed a deep learning system to both classify histologic patterns in lung adenocarcinoma and predict TMB status using de-identified Hematoxylin and Eosin (H&E) stained whole slide images. We first trained a convolutional neural network to map histologic features across whole slide images of lung cancer resection specimens. On evaluation using an external data source, this model achieved patch-level area under the receiver operating characteristic curve (AUC) of 0.78–0.98 across nine histologic features. We then integrated the output of this model with clinico-demographic data to develop an interpretable model for TMB classification. The resulting end-to-end system was evaluated on 172 held out cases from TCGA, achieving an AUC of 0.71 (95% CI 0.63–0.80). The benefit of using histologic features in predicting TMB is highlighted by the significant improvement this approach offers over using the clinical features alone (AUC of 0.63 [95% CI 0.53–0.72], p = 0.002). Furthermore, we found that our histologic subtype-based approach achieved performance similar to that of a weakly supervised approach (AUC of 0.72 [95% CI 0.64–0.80]). Together these results underscore that incorporating histologic patterns in biomarker prediction for lung cancer provides informative signals, and that interpretable approaches utilizing these patterns perform comparably with less interpretable, weakly supervised approaches.

To define an algorithm for differential diagnostics of parotid malignancies and Warthin tumors (WTs) based on dynamic contrast enhanced MRI (DCE-MRI). 55 patients with parotid tumors treated surgically were analyzed. Of which, 19 had parotid malignancy and 36 had WTs confirmed with postoperative histopathological examination. Accuracy of DCE-MRI parameters (Tpeak and WR) was compared with the histopathological diagnosis. ROC analysis was performed to determine sensitivity and specificity of DCE-MRI with various Tpeak and WR cut-off values. WT showed significantly lower median Tpeak and higher median WR than malignant lesions. The cut-off values for Tpeak and WR providing maximum sensitivity (84.2%) and specificity (86.1%) for malignant tumors were Tpeak > 60 s and WR ≤ 30%. Different diagnostic algorithm, i.e., lower cut-off value for Tpeak (Tpeak = 60 s), increases sensitivity of DCE-MRI in differentiating parotid malignancies from WTs. However, WR > 30% seems to be a key diagnostic criterion for benign lesions. Precise and reliable preoperative diagnostics of parotid tumors aids in careful surgical planning, thereby assisting in achieving sufficient surgical resection margins and facial nerve preservation.

Cervical cancer is the second most common cancer in women worldwide with a mortality rate of 60%. Cervical cancer begins with no overt signs and has a long latent period, making early detection through regular checkups vitally immportant. In this study, we compare the performance of two different models, machine learning and deep learning, for the purpose of identifying signs of cervical cancer using cervicography images. Using the deep learning model ResNet-50 and the machine learning models XGB, SVM, and RF, we classified 4119 Cervicography images as positive or negative for cervical cancer using square images in which the vaginal wall regions were removed. The machine learning models extracted 10 major features from a total of 300 features. All tests were validated by fivefold cross-validation and receiver operating characteristics (ROC) analysis yielded the following AUCs: ResNet-50 0.97(CI 95% 0.949–0.976), XGB 0.82(CI 95% 0.797–0.851), SVM 0.84(CI 95% 0.801–0.854), RF 0.79(CI 95% 0.804–0.856). The ResNet-50 model showed a 0.15 point improvement (p

Stereotactic radiosurgery (SRS) is an established, effective therapy against vestibular schwannoma (VS). The mechanisms of tumour response are, however, unknown and in this study we sought to evaluate changes in the irradiated VS tumour microenvironment through a multinuclear MRI approach. Five patients with growing sporadic VS underwent a multi-timepoint comprehensive MRI protocol, which included diffusion tensor imaging (DTI), dynamic contrast-enhanced (DCE) MRI and a spiral 23Na-MRI acquisition for total sodium concentration (TSC) quantification. Post-treatment voxelwise changes in TSC, DTI metrics and DCE-MRI derived microvascular biomarkers (Ktrans, ve and vp) were evaluated and compared against pre-treatment values. Changes in tumour TSC and microvascular parameters were observable as early as 2 weeks post-treatment, preceding changes in structural imaging. At 6 months post-treatment there were significant voxelwise increases in tumour TSC (p p p

Within the tumor microenvironment, specifically aligned collagen has been shown to stimulate tumor progression by directing the migration of metastatic cells along its structural framework. Tumor-associated collagen signatures (TACS) have been linked to breast cancer patient outcome. Robust and affordable methods for assessing biological information contained in collagen architecture need to be developed. We have developed a novel artificial neural network (ANN) based approach for tumor collagen segmentation from bright-field histology images and have tested it on a set of tissue microarray sections from early hormone receptor-positive invasive ductal breast carcinoma stained with Sirius Red (1 core per patient, n = 92). We designed and trained ANNs on sets of differently annotated image patches to segment collagen fibers and extracted 37 features of collagen fiber morphometry, density, orientation, texture, and fractal characteristics in the entire cohort. Independent instances of ANN models trained on highly differing annotations produced reasonably concordant collagen segmentation masks and allowed reliable prognostic Cox regression models (with likelihood ratios 14.11–22.99, at p-value

Colonic crypts are tubular glands that multiply through a symmetric branching process called crypt fission. During the early stages of colorectal cancer, the normal fission process is disturbed, leading to asymmetrical branching or budding. The challenging shapes of the budding crypts make it difficult to prepare paraffin sections for conventional histology, resulting in colonic cross sections with crypts that are only partially visible. To study crypt budding in situ and in three dimensions (3D), we employ X-ray micro-computed tomography to image intact colons, and a new method we developed (3D cyclorama) to digitally unroll them. Here, we present, verify and validate our ‘3D cyclorama’ method that digitally unrolls deformed tubes of non-uniform thickness. It employs principles from electrostatics to reform the tube into a series of onion-like surfaces, which are mapped onto planar panoramic views. This enables the study of features extending over several layers of the tube’s depth, demonstrated here by two case studies: (i) microvilli in the human placenta and (ii) 3D-printed adhesive films for drug delivery. Our 3D cyclorama method can provide novel insights into a wide spectrum of applications where digital unrolling or flattening is necessary, including long bones, teeth roots and ancient scrolls.

We developed a DICOM dataset that can be used to evaluate the performance of de-identification algorithms. DICOM objects (a total of 1,693 CT, MRI, PET, and digital X-ray images) were selected from datasets published in the Cancer Imaging Archive (TCIA). Synthetic Protected Health Information (PHI) was generated and inserted into selected DICOM Attributes to mimic typical clinical imaging exams. The DICOM Standard and TCIA curation audit logs guided the insertion of synthetic PHI into standard and non-standard DICOM data elements. A TCIA curation team tested the utility of the evaluation dataset. With this publication, the evaluation dataset (containing synthetic PHI) and de-identified evaluation dataset (the result of TCIA curation) are released on TCIA in advance of a competition, sponsored by the National Cancer Institute (NCI), for algorithmic de-identification of medical image datasets. The competition will use a much larger evaluation dataset constructed in the same manner. This paper describes the creation of the evaluation datasets and guidelines for their use., Measurement(s) Deidentification • Clinical Data Technology Type(s) data synthesis • digital curation Factor Type(s) imaging type Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.14802774

Endometrial cancer (EC) is the most common gynecological tumor in developed countries, and preoperative risk stratification is essential for personalized medicine. There have been several radiomics studies for noninvasive risk stratification of EC using MRI. Although tumor segmentation is usually necessary for these studies, manual segmentation is not only labor-intensive but may also be subjective. Therefore, our study aimed to perform the automatic segmentation of EC on MRI with a convolutional neural network. The effect of the input image sequence and batch size on the segmentation performance was also investigated. Of 200 patients with EC, 180 patients were used for training the modified U-net model; 20 patients for testing the segmentation performance and the robustness of automatically extracted radiomics features. Using multi-sequence images and larger batch size was effective for improving segmentation accuracy. The mean Dice similarity coefficient, sensitivity, and positive predictive value of our model for the test set were 0.806, 0.816, and 0.834, respectively. The robustness of automatically extracted first-order and shape-based features was high (median ICC = 0.86 and 0.96, respectively). Other high-order features presented moderate-high robustness (median ICC = 0.57–0.93). Our model could automatically segment EC on MRI and extract radiomics features with high reliability.

Accurate identification of axillary lymph node (ALN) involvement in patients with early-stage breast cancer is important for determining appropriate axillary treatment options and therefore avoiding unnecessary axillary surgery and complications. Here, we report deep learning radiomics (DLR) of conventional ultrasound and shear wave elastography of breast cancer for predicting ALN status preoperatively in patients with early-stage breast cancer. Clinical parameter combined DLR yields the best diagnostic performance in predicting ALN status between disease-free axilla and any axillary metastasis with areas under the receiver operating characteristic curve (AUC) of 0.902 (95% confidence interval [CI]: 0.843, 0.961) in the test cohort. This clinical parameter combined DLR can also discriminate between low and heavy metastatic burden of axillary disease with AUC of 0.905 (95% CI: 0.814, 0.996) in the test cohort. Our study offers a noninvasive imaging biomarker to predict the metastatic extent of ALN for patients with early-stage breast cancer.

This study is the first multi-center non-inferiority study that aims to critically evaluate the effectiveness of HHUS/ABUS in China breast cancer detection. This was a multicenter hospital-based study. Five hospitals participated in this study. Women (30–69 years old) with defined criteria were invited for breast examination by HHUS, ABUS or/and mammography. For BI-RADS category 3, an additional magnetic resonance imaging (MRI) test was provided to distinguish the true negative results from false negative results. For women classified as BI-RADS category 4 or 5, either core aspiration biopsy or surgical biopsy was done to confirm the diagnosis. Between February 2016 and March 2017, 2844 women signed the informed consent form, and 1947 of them involved in final analysis (680 were 30 to 39 years old, 1267 were 40 to 69 years old).For all participants, ABUS sensitivity (91.81%) compared with HHUS sensitivity (94.70%) with non-inferior Z tests, P = 0.015. In the 40–69 age group, non-inferior Z tests showed that ABUS sensitivity (93.01%) was non-inferior to MG sensitivity (86.02%) with P P P P P P P P = 0.114 by superior test. The sensitivity of ABUS/HHUS is superior to that of MG. The specificity of ABUS/HHUS is non-inferior to that of MG. In China, for an experienced US radiologist, both HHUS and ABUS have better diagnostic efficacy than MG in symptomatic individuals.

Precise methods for quantifying drug accumulation in brain tissue are currently very limited, challenging the development of new therapeutics for brain disorders. Transcardial perfusion is instrumental for removing the intravascular fraction of an injected compound, thereby allowing for ex vivo assessment of extravasation into the brain. However, pathological remodeling of tissue microenvironment can affect the efficiency of transcardial perfusion, which has been largely overlooked. We show that, in contrast to healthy vasculature, transcardial perfusion cannot remove an injected compound from the tumor vasculature to a sufficient extent leading to considerable overestimation of compound extravasation. We demonstrate that 3D deep imaging of optically cleared tumor samples overcomes this limitation. We developed two machine learning-based semi-automated image analysis workflows, which provide detailed quantitative characterization of compound extravasation patterns as well as tumor angioarchitecture in large three-dimensional datasets from optically cleared samples. This methodology provides a precise and comprehensive analysis of extravasation in brain tumors and allows for correlation of extravasation patterns with specific features of the heterogeneous brain tumor vasculature., Kostrikov et al. report a deficiency of transcardial perfusion in brain tumor vasculature, which leads to exaggeration of drug extravasation measurements. They then demonstrate how optical tissue clearing can help to overcome this limitation and provide two machine learning-based image analysis workflows enabling detailed quantitative characterization of compound extravasation patterns as well as tumor angioarchitecture in large three-dimensional datasets.

Polarization-sensitive optical coherence tomography (PS-OCT) is a high-resolution label-free optical biomedical imaging modality that is sensitive to the microstructural architecture in tissue that gives rise to form birefringence, such as collagen or muscle fibers. To enable polarization sensitivity in an OCT system, however, requires additional hardware and complexity. We developed a deep-learning method to synthesize PS-OCT images by training a generative adversarial network (GAN) on OCT intensity and PS-OCT images. The synthesis accuracy was first evaluated by the structural similarity index (SSIM) between the synthetic and real PS-OCT images. Furthermore, the effectiveness of the computational PS-OCT images was validated by separately training two image classifiers using the real and synthetic PS-OCT images for cancer/normal classification. The similar classification results of the two trained classifiers demonstrate that the predicted PS-OCT images can be potentially used interchangeably in cancer diagnosis applications. In addition, we applied the trained GAN models on OCT images collected from a separate OCT imaging system, and the synthetic PS-OCT images correlate well with the real PS-OCT image collected from the same sample sites using the PS-OCT imaging system. This computational PS-OCT imaging method has the potential to reduce the cost, complexity, and need for hardware-based PS-OCT imaging systems.

Histopathological level imaging in a non-invasive manner is important for clinical diagnosis, which has been a tremendous challenge for current imaging modalities. Recent development of ultra-high-field (UHF) magnetic resonance imaging (MRI) represents a large step toward this goal. Nevertheless, there is a lack of proper contrast agents that can provide superior imaging sensitivity at UHF for disease detection, because conventional contrast agents generally induce T2 decaying effects that are too strong and thus limit the imaging performance. Herein, by rationally engineering the size, spin alignment, and magnetic moment of the nanoparticles, we develop an UHF MRI-tailored ultra-sensitive antiferromagnetic nanoparticle probe (AFNP), which possesses exceptionally small magnetisation to minimize T2 decaying effect. Under the applied magnetic field of 9 T with mice dedicated hardware, the nanoprobe exhibits the ultralow r2/r1 value (~1.93), enabling the sensitive detection of microscopic primary tumours (, Ultra-high-field (UHF) magnetic resonance imaging (MRI) has potential for imaging disease including cancer metastasis. Here, the authors develop an ultra-sensitive antiferromagnetic nanoparticle probe with a small magnetisation for use in UHF MRI and demonstrate the ability to detect small primary tumours and micrometastases in mice.

Nearly half of patients with prostate cancer (PCa) harbour low- or intermediate-risk disease considered suitable for active surveillance (AS). However, up to 44% of patients discontinue AS within the first five years, highlighting the unmet clinical need for robust baseline risk-stratification tools that enable timely and accurate prediction of tumour progression. In this proof-of-concept study, we sought to investigate the added value of MRI-derived radiomic features to standard-of-care clinical parameters for improving baseline prediction of PCa progression in AS patients. Tumour T2-weighted imaging (T2WI) and apparent diffusion coefficient radiomic features were extracted, with rigorous calibration and pre-processing methods applied to select the most robust features for predictive modelling. Following leave-one-out cross-validation, the addition of T2WI-derived radiomic features to clinical variables alone improved the area under the ROC curve for predicting progression from 0.61 (95% confidence interval [CI] 0.481–0.743) to 0.75 (95% CI 0.64–0.86). These exploratory findings demonstrate the potential benefit of MRI-derived radiomics to add incremental benefit to clinical data only models in the baseline prediction of PCa progression on AS, paving the way for future multicentre studies validating the proposed model and evaluating its impact on clinical outcomes.

The Gleason score contributes significantly in predicting prostate cancer outcomes and selecting the appropriate treatment option, which is affected by well-known inter-observer variations. We present a novel deep learning-based automated Gleason grading system that does not require extensive region-level manual annotations by experts and/or complex algorithms for the automatic generation of region-level annotations. A total of 6664 and 936 prostate needle biopsy single-core slides (689 and 99 cases) from two institutions were used for system discovery and validation, respectively. Pathological diagnoses were converted into grade groups and used as the reference standard. The grade group prediction accuracy of the system was 77.5% (95% confidence interval (CI): 72.3–82.7%), the Cohen’s kappa score (κ) was 0.650 (95% CI: 0.570–0.730), and the quadratic-weighted kappa score (κquad) was 0.897 (95% CI: 0.815–0.979). When trained on 621 cases from one institution and validated on 167 cases from the other institution, the system’s accuracy reached 67.4% (95% CI: 63.2–71.6%), κ 0.553 (95% CI: 0.495–0.610), and the κquad 0.880 (95% CI: 0.822–0.938). In order to evaluate the impact of the proposed method, performance comparison with several baseline methods was also performed. While limited by case volume and a few more factors, the results of this study can contribute to the potential development of an artificial intelligence system to diagnose other cancers without extensive region-level annotations.

Serum inflammatory markers are used in the prognostication of colorectal cancer (CRC); however, the corresponding role of positron emission tomography (PET)-derived inflammatory markers remains unclear. This study aimed to investigate the prognostic value of 18F-fluorodeoxyglucose (FDG) uptake in the bone marrow and spleen of patients with CRC and evaluate the relationship between FDG uptake estimates in these organs and serum inflammatory markers. In total, 411 patients who underwent preoperative FDG PET/computed tomography (CT) within 1 month of surgery were enrolled. The mean standardized uptake values of the bone marrow and spleen were normalized to the value of the liver, thereby generating bone marrow-to-liver uptake ratio (BLR) and spleen-to-liver uptake ratio (SLR) estimates. The value of BLR and SLR in predicting overall survival (OS) was assessed using the Cox proportional hazards model. The correlation between BLR or SLR and neutrophil-to-lymphocyte ratio (NLR) was evaluated. The predictive accuracy of BLR alone and in combination with SLR was compared using the integrated area under the receiver operating characteristic curves (iAUC). In the univariate analysis, BLR (> 1.06) and SLR (> 0.93) were significant predictors of OS. In the multivariate analysis, BLR was an independent predictor of OS (hazard ratio = 5.279; p p

Epithelial cancers of the upper intestinal tract in animals, in biopsied human tissue and in a human patient can be detected via a fluorescently labelled inhibitor of the DNA-repair enzyme poly(ADP-ribose) polymerase 1.

Hepatocellular carcinoma (HCC) is one of the most common causes of cancer-related mortality worldwide. Sorafenib is the standard first-line treatment for advanced HCC, but its efficacy is limited. Apatinib is a small-molecule tyrosine kinase inhibitor that has shown promising antitumor effects in gastric and non-small cell lung cancers in clinical trials, but there have been only a few studies reporting its anti-HCC effects in vitro and in HCC xenograft models. Hence, our present study systemically investigated and compared the antitumorigenic and antiangiogenic efficacy of apatinib and sorafenib in HCC in vitro and in vivo using multimodality molecular imaging, including bioluminescence imaging (BLI), bioluminescence tomography (BLT), fluorescence molecular imaging (FMI), and computed tomography angiography (CTA). Moreover, the safety and side effects of the two drugs were systemically evaluated. We found that apatinib showed a comparable therapeutic efficacy to sorafenib for the inhibition of HCC. The drug safety evaluation revealed that both of these drugs caused hypertension and mild liver and kidney damage. Sorafenib caused diarrhea, rash, and weight loss in mice, but these effects were not observed in mice treated with apatinib. In conclusion, apatinib has similar antitumorigenic and antiangiogenic efficacy as sorafenib in HCC with less toxicity. These findings may provide preclinical evidence supporting the potential application of apatinib for the treatment of HCC patients., Atherosclerosis: halting plaque in its tracks Researchers have combined different sophisticated imaging techniques to assess the safety and efficacy of liver cancer therapy in animal models. Many hepatocellular carcinoma (HCC) patients respond to sorafenib, but this drug is expensive and may cause severe side-effects. Qian Liang at China’s Institute of Automation, Beijing, and colleagues have employed cutting-edge imaging technologies to study an alternative drug, apatinib, which has shown promise for stomach and lung cancer and has an excellent safety profile. Using bioluminescence imaging, the researchers could directly visualize apatanib-mediated inhibition of tumor growth in live mice much earlier than would be possible with other methods. The researchers subsequently used additional imaging techniques to demonstrate that apatanib inhibits tumor blood vessel growth. These findings reveal a promising alternative treatment for HCC, as well as a powerful strategy for drug testing in animals.

During the last few decades, nanotechnology has established many essential applications in the biomedical field and in particular for cancer therapy. Not only can nanodelivery systems address the shortcomings of conventional chemotherapy such as limited stability, non-specific biodistribution and targeting, poor water solubility, low therapeutic indices, and severe toxic side effects, but some of them can also provide simultaneous combination of therapies and diagnostics. Among the various therapies, the combination of chemo- and photothermal therapy (CT-PTT) has demonstrated synergistic therapeutic efficacies with minimal side effects in several preclinical studies. In this regard, inorganic nanostructures have been of special interest for CT-PTT, owing to their high thermal conversion efficiency, application in bio-imaging, versatility, and ease of synthesis and surface modification. In addition to being used as the first type of CT-PTT agents, they also include the most novel CT-PTT systems as the potentials of new inorganic nanomaterials are being more and more discovered. Considering the variety of inorganic nanostructures introduced for CT-PTT applications, enormous effort is needed to perform translational research on the most promising nanomaterials and to comprehensively evaluate the potentials of newly introduced ones in preclinical studies. This review provides an overview of most novel strategies used to employ inorganic nanostructures for cancer CT-PTT as well as cancer imaging and discusses current challenges and future perspectives in this area.