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Conventional tissue sampling can lead to misdiagnoses and repeated biopsies. Additionally, tissue processed for histopathology suffers from poor nucleic acid quality and/or quantity for downstream molecular profiling. Targeted micro-sampling of tissue can ensure accurate diagnosis and molecular profiling in the presence of spatial heterogeneity, especially in tumors, and facilitate acquisition of fresh tissue for molecular analysis. In this study, we explored the feasibility of performing 1–2 mm precision biopsies guided by high-resolution reflectance confocal microscopy (RCM) and optical coherence tomography (OCT), and reflective metallic grids for accurate spatial targeting. Accurate sampling was confirmed with either histopathology or molecular profiling through next generation sequencing (NGS) in 9 skin cancers in 7 patients. Imaging-guided 1–2 mm biopsies enabled spatial targeting for in vivo diagnosis, feature correlation and depth assessment, which were confirmed with histopathology. In vivo 1-mm targeted biopsies achieved adequate quantity and high quality of DNA for next-generation sequencing. Subsequent mutational profiling was confirmed on 1 melanoma in situ and 2 invasive melanomas, using a 505-gene mutational panel called Memorial Sloan Kettering-Integrated mutational profiling of actionable cancer targets (MSK-IMPACT). Differential mutational landscapes, in terms of number and types of mutations, were found between invasive and in situ melanomas in a single patient. Our findings demonstrate feasibility of accurate sampling of regions of interest for downstream histopathological diagnoses and molecular pathology in both in vivo and ex vivo settings with broad diagnostic, therapeutic and research potential in cutaneous diseases accessible by RCM-OCT imaging.

To evaluate whether mammographic texture features were associated with second primary contralateral breast cancer (CBC) risk, we created a “texture risk score” using pre-treatment mammograms in a case–control study of 212 women with CBC and 223 controls with unilateral breast cancer. The texture risk score was associated with CBC (odds per adjusted standard deviation = 1.25, 95% CI 1.01–1.56) after adjustment for mammographic percent density and confounders. These results support the potential of texture features for CBC risk assessment of breast cancer survivors.

The penetration of microwave power inside a human head model is improved by employing a dielectric loaded rectangular waveguide as the transmission source. A multi-layer reflection model is investigated to evaluate the combined material characteristics of different lossy human head tissues at 2.45 GHz. A waveguide loaded with a calculated permittivity of 3.62 is shown to maximise the microwave power penetration at the desired frequency. A Quartz (SiO2) loaded rectangular waveguide fed by a microstrip antenna is designed to validate the power penetration improvement inside an inhomogeneous human head phantom. A measured 1.33 dB power penetration increment is observed for the dielectric loaded waveguide over a standard rectangular waveguide at 50 mm inside the head, with an 81.9% reduction in the size of the transmission source.

Enginyeria Biomèdica; Imatge del càncer Biomedical engineering; Cancer imaging Ingeniería Biomédica; Imágenes del cáncer Tumor heterogeneity has been postulated as a hallmark of treatment resistance and a cure constraint in cancer patients. Conventional quantitative medical imaging (radiomics) can be extended to computing voxel-wise features and aggregating tumor subregions with similar radiological phenotypes (imaging habitats) to elucidate the distribution of tumor heterogeneity within and among tumors. Despite the promising applications of imaging habitats, they may be affected by variability of radiomics features, preventing comparison and generalization of imaging habitats techniques. We performed a comprehensive repeatability and reproducibility analysis of voxel-wise radiomics features in more than 500 lung cancer patients with computed tomography (CT) images and demonstrated the effect of voxel-wise radiomics variability on imaging habitats computation in 30 lung cancer patients with test–retest images. Repeatable voxel-wise features characterized texture heterogeneity and were reproducible regardless of the applied feature extraction parameters. Imaging habitats computed using robust radiomics features were more stable than those computed using all features in test–retest CTs from the same patient. Nine voxel-wise radiomics features (joint energy, joint entropy, sum entropy, maximum probability, difference entropy, Imc1, Imc2, Idn and Idmn) were repeatable and reproducible. This supports their application for computing imaging habitats in lung tumors towards the discovery of previously unseen tumor heterogeneity and the development of novel non-invasive imaging biomarkers for precision medicine. This study was supported by the Banco Bilbao Vizcaya Argentaria and Fundacio La Caixa. R.P.L. is supported by the CRIS Foundation Talent Award (TALENT19-05), the Instituto de Salud Carlos III-Investigación en Salud (PI18/01395) and the Prostate Cancer Foundation Young Investigator Award. K.B. is supported by MSCA COFUND Beatriu de Pinós Grant (2019BP/00182). ML is supported by a PERIS Grant-PIS Program. We thank Sarah MacKenzie, PhD for editorial assistance.

Multispectral photoacoustic tomography enables the resolution of spectral components of a tissue or sample at high spatiotemporal resolution. With the availability of commercial instruments, the acquisition of data using this modality has become consistent and standardized. However, the analysis of such data is often hampered by opaque processing algorithms, which are challenging to verify and validate from a user perspective. Furthermore, such tools are inflexible, often locking users into a restricted set of processing motifs, which may not be able to accommodate the demands of diverse experiments. To address these needs, we have developed a Reconstruction, Analysis, and Filtering Toolbox to support the analysis of photoacoustic imaging data. The toolbox includes several algorithms to improve the overall quantification of photoacoustic imaging, including non-negative constraints and multispectral filters. We demonstrate various use cases, including dynamic imaging challenges and quantification of drug effect, and describe the ability of the toolbox to be parallelized on a high performance computing cluster.

To compare the diagnostic performances of physicians and a deep convolutional neural network (CNN) predicting malignancy with ultrasonography images of thyroid nodules with atypia of undetermined significance (AUS)/follicular lesion of undetermined significance (FLUS) results on fine-needle aspiration (FNA). This study included 202 patients with 202 nodules ≥ 1 cm AUS/FLUS on FNA, and underwent surgery in one of 3 different institutions. Diagnostic performances were compared between 8 physicians (4 radiologists, 4 endocrinologists) with varying experience levels and CNN, and AUS/FLUS subgroups were analyzed. Interobserver variability was assessed among the 8 physicians. Of the 202 nodules, 158 were AUS, and 44 were FLUS; 86 were benign, and 116 were malignant. The area under the curves (AUCs) of the 8 physicians and CNN were 0.680–0.722 and 0.666, without significant differences (P > 0.05). In the subgroup analysis, the AUCs for the 8 physicians and CNN were 0.657–0.768 and 0.652 for AUS, 0.469–0.674 and 0.622 for FLUS. Interobserver agreements were moderate (k = 0.543), substantial (k = 0.652), and moderate (k = 0.455) among the 8 physicians, 4 radiologists, and 4 endocrinologists. For thyroid nodules with AUS/FLUS cytology, the diagnostic performance of CNN to differentiate malignancy with US images was comparable to that of physicians with variable experience levels.

The purpose of this study is to investigate imaging characteristics of young age breast cancer (YABC) focusing on correlation with pathologic factors and association with disease recurrence. From January 2017 to December 2019, patients under 40 years old who were diagnosed as breast cancer were enrolled in this study. Morphologic analysis of tumor and multiple quantitative parameters were obtained from pre-treatment dynamic contrast enhanced breast magnetic resonance imaging (DCE-MRI). Tumor-stroma ratio (TSR), microvessel density (MVD) and endothelial Notch 1 (EC Notch 1) were investigated for correlation with imaging parameters. In addition, recurrence associated factors were assessed using both clinico-pathologic factors and imaging parameters. A total of 53 patients were enrolled. Several imaging parameters derived from apparent diffusion coefficient (ADC) histogram showed negative correlation with TSR; and there was negative correlation between MVD and Ve in perfusion analysis. There were nine cases of recurrences with median interval of 16 months. Triple negative subtype and low CD34 MVD positivity in Notch 1 hotspots showed significant association with tumor recurrence. Texture parameters reflecting tumor sphericity and homogeneity were also associated with disease recurrence. In conclusion, several quantitative MRI parameters can be used as imaging biomarkers for tumor microenvironment and can predict disease recurrence in YABC.

Despite follicular lymphoma (FL) is frequently characterized by a moderate increase of glucose metabolism, PET/CT examinations provides valuable information for staging and response assessment of the disease. The aim of the study was to assess and compare the diagnostic performance of PET/ldCT and PET/ceCT, respectively, in evaluating FL patients at the end of treatment. Fifty FL consecutive patients who underwent end-of-therapy PET/CT with both ldCT and ceCT were analyzed. Two blinded observers independently assessed PET/ldCT and PET/ceCT applying the Deauville score (DS) and Lugano classification criteria. PET imaging obtained after the end-of-treatment (EoT) was classified as showing PET and ce-CT matched response (concordant imaging group, CIG) or PET and ce-CT unmatched response (discordant imaging group, DIG). Relapse rate and Event-Free Survival (EFS) were compared between CIG and DIG patients. Overall, no differences in metabolic response classification were observed between PET/ldCT and PET/ceCT. In 13 (26%) patients PET/ceCT identified additional FDG-negative nodal lesions in mesenteric, retroperitoneal and iliac regions. However, in all cases, final DS remained unchanged and the additional results did not modify the following therapeutic decision. Among patients, who obtained complete metabolic response a comparable rate of relapse was registered in DIG 3/13 (23%) and CIG subgroups 5/20 (25%) [p = 0.899]. In all 3 DIG cohort patients who relapsed the recurrent disease involved also, but not exclusively, PET negative lymph nodes detected by ceCT. In overall population metabolic response defined by PET/ldCT predicted EFS [76% (group of patients with metabolic response) vs 35% (group of patients with residual disease), p = 0.0013] significantly better than ceCT-Based response assessment [75% (group of patients with complete response) vs 53% (group of patients with residual disease), p = 0.06]. Our study demonstrates a negligible diagnostic and predictive value of ceCT performed in addition to standard 18FDG PET/ldCT for EoT response evaluation in FLs. PET/ldCT should be performed as first-line imaging procedure, also in patients with prevalent abdominal and pelvic involvement, limiting the acquisition of ceCT in selected cases. This tailored approach would contribute to avoid useless radiation exposure and preserve renal function of patients.

Advances in medical imaging technologies now allow noninvasive image acquisition from individual patients at high spatiotemporal resolutions. A relatively new effort of predictive oncology is to develop a paradigm for forecasting the future status of an individual tumor given initial conditions and an appropriate mathematical model. The objective of this study was to introduce a comprehensive multiscale computational method to predict cancer and microvascular network growth patterns. A rectangular lattice-based model was designed so different evolutionary scenarios could be simulated and for predicting the impact of diffusible factors on tumor morphology and size. Further, the model allows prediction-based simulation of cell and microvascular behavior. Like a single cell, each agent is fully realized within the model and interactions are governed in part by machine learning methods. This multiscale computational model was developed and incorporated input information from in vivo microscale computed tomography (microCT) images acquired from breast cancer-bearing mice. It was found that as the difference between expansion of the cancer cell population and microvascular network increases, cells undergo proliferation and migration with a greater probability compared to other phenotypes. Overall, multiscale computational model agreed with both theoretical expectations and experimental findings (microCT images) not used during model training.

Early prediction of treatment response in nasopharyngeal carcinoma is clinically relevant for optimizing treatment strategies. This meta-analysis was performed to evaluate whether apparent diffusion coefficient (ADC) from diffusion-weighted imaging (DWI) can predict treatment response of patients with nasopharyngeal carcinoma. A systematic search of PubMed-MEDLINE and Embase was performed to identify relevant original articles until July 22, 2021. We included studies which performed DWI for predicting locoregional treatment response in nasopharyngeal carcinoma treated with neoadjuvant chemotherapy, definitive chemoradiation, or radiation therapy. Hazard ratios were meta-analytically pooled using a random-effects model for the pooled estimates of overall survival, local relapse-free survival, distant metastasis-free survival and their 95% CIs. ADC showed a pooled sensitivity of 87% (95% CI 72–94%) and specificity of 70% (95% CI 56–80%) for predicting treatment response. Significant between-study heterogeneity was observed for both pooled sensitivity (I2 = 68.5%) and specificity (I2 = 92.2%) (P

Prostate cancer (PCa) remains the second most diagnosed cancer worldwide. Higher body weight is associated with chronic inflammation, increased angiogenesis, and treatment-resistant tumor phenotypes. Dietary tomato reduces PCa risk, which may be due to tomato inhibition of angiogenesis and disruption of androgen signaling. This pilot study investigated the interplay between tomato powder (TP), incorporated into control (CON) and obesogenic (OB) diets, and PCa tumor growth and blood perfusion over time in a transgenic model of PCa (TRAMP). Ultrasound microvessel imaging (UMI) results showed good agreement with gold-standard immunohistochemistry quantification of endothelial cell density, indicating that this technique can be applied to non-invasively monitor tumor blood perfusion in vivo. Greater body weight was positively associated with tumor growth. We also found that TP significantly inhibited prostate tumor angiogenesis but that this inhibition differentially affected measured outcomes depending on CON or OB diets. TP led to reduced tumor growth, intratumoral inflammation, and intratumoral androgen-regulated gene expression (srd5a1, srd5a2) when incorporated with the CON diet but greater tumor growth and intratumoral gene expression when incorporated with the OB diet. Results from this study show that protective benefits from dietary tomato are lost, or may become deleterious, when combined with a Western-style diet.

To determine whether temporal subtraction (TS) CT obtained with non-rigid image registration improves detection of various bone metastases during serial clinical follow-up examinations by numerous radiologists. Six board-certified radiologists retrospectively scrutinized CT images for patients with history of malignancy sequentially. These radiologists selected 50 positive and 50 negative subjects with and without bone metastases, respectively. Furthermore, for each subject, they selected a pair of previous and current CT images satisfying predefined criteria by consensus. Previous images were non-rigidly transformed to match current images and subtracted from current images to automatically generate TS images. Subsequently, 18 radiologists independently interpreted the 100 CT image pairs to identify bone metastases, both without and with TS images, with each interpretation separated from the other by an interval of at least 30 days. Jackknife free-response receiver operating characteristics (JAFROC) analysis was conducted to assess observer performance. Compared with interpretation without TS images, interpretation with TS images was associated with a significantly higher mean figure of merit (0.710 vs. 0.658; JAFROC analysis, P = 0.0027). Mean sensitivity at lesion-based was significantly higher for interpretation with TS compared with that without TS (46.1% vs. 33.9%; P = 0.003). Mean false positive count per subject was also significantly higher for interpretation with TS than for that without TS (0.28 vs. 0.15; P

We assessed the role of [18F]FDG-PET/CT in identifying and managing cancer of unknown primary site (CUP syndrome). We reviewed [18F]FDG-PET/CT scans of individuals with CUP syndrome recorded in clinical referral letters from 2012 to 2019. We evaluated the identification of primary tumor (PT) by [18F]FDG-PET/CT, according to histological subtype, and the impact on clinical management. The median age was 65 years, 36/64 males (56%). PTs were detected in 28/64 (44%) patients. Detection was significantly lower in patients with squamous cell carcinoma (SCC) than with other histologies combined, p = 0.034. Mean age, mean SUVmax (10.6 ± 6.0) and organ involvement were similar between patients with and without discovered PTs; and between patients with SCC and with other histologies combined. However, those with SCC were less likely than the others to present with multi-lesion involvement, p

Purpose To analyze emergency department (ED) computerized tomography (CT) utilization in cancer patients with coronavirus disease 2019 (COVID-19). Methods A retrospective chart review was performed to identify cancer patients who received COVID-19 diagnosis within the single healthcare system and presented to the ED within 30 days of COVID-19 positive date between May 1 and December 31, 2020. Results In our 61 patients, the mean age was 72.5 years old, with 34% of patients (n = 21) on active cancer therapy and 66% (n = 40) on surveillance only. Most patients (n = 53) received their COVID-19 diagnosis within the ED, with 8 patients diagnosed prior to initial ED visit. The most common CT studies ordered within the ED were CT chest (n = 25), CT abdomen/pelvis (A/P) (n = 20), CT head (n = 8), and CT chest/abdomen/pelvis (C/A/P) (n = 7). COVID-19 findings were present on 33 scans, findings of worsening malignancy on 12 scans, and non-COVID non-cancer findings on 9 scans. Significant differences in CT severity score (p = 0.0001), indication for hospitalization (p = 0.026), length of hospitalization (p = 0.004), interventions (remdesivir, mechanical ventilation, and vasopressor support) while hospitalized (p

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.

Testicular cancer is a rare tumor with a worldwide incidence that has increased over the last few decades. The majority of these tumors are testicular non-germ (TNGCTs) and germ cell tumors (TGCTs); the latter divided into two broad classes - seminomatous (SGCTs) and non-seminomatous germ cell tumors (NSGCTs). Although ultrasonography (US) maintains a primary role in the diagnostic workup of scrotal pathology, magnetic resonance imaging (MRI) has emerged as the imaging modality recommended for challenging cases, providing additional information to clarify inconclusive/equivocal US. In this work we describe and publicly share a collection of 44 images of annotated T2-weighted MRI lesions from 42 patients. Given that testicular cancer is a rare tumor, we are confident that this collection can be used to validate statistical models and to further investigate TNGCT and TGCT peculiarities using medical imaging features., Measurement(s) sex cord-gonadal stromal tumor Technology Type(s) magnetic resonance imaging Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.14511624

Radiomics is a method to mine large numbers of quantitative imaging features and develop predictive models. It has shown exciting promise for improved cancer decision support from early detection to personalized precision treatment, and therefore offers a desirable new direction for pancreatic cancer where the mortality remains high despite the current care and intense research. For radiomics, interobserver segmentation variability and its effect on radiomic feature stability is a crucial consideration. While investigations have been reported for high-contrast cancer sites such as lung cancer, no studies to date have investigated it on CT-based radiomics for pancreatic cancer. With three radiation oncology observers and three radiology observers independently contouring on the contrast CT of 21 pancreatic cancer patients, we conducted the first interobserver segmentation variability study on CT-based radiomics for pancreatic cancer. Moreover, our novel investigation assessed whether there exists an interdisciplinary difference between the two disciplines. For each patient, a consensus tumor volume was generated using the simultaneous truth and performance level expectation algorithm, using the dice similarity coefficient (DSC) to assess each observer’s delineation against the consensus volume. Radiation oncology observers showed a higher average DSC of 0.81 ± 0.06 than the radiology observers at 0.69 ± 0.16 (p = 0.002). On a panel of 1277 radiomic features, the intraclass correlation coefficients (ICC) was calculated for all observers and those of each discipline. Large variations of ICCs were observed for different radiomic features, but ICCs were generally higher for the radiation oncology group than for the radiology group. Applying a threshold of ICC > 0.75 for considering a feature as stable, 448 features (35%) were found stable for the radiation oncology group and 214 features (16%) were stable from the radiology group. Among them, 205 features were found stable for both groups. Our results provide information for interobserver segmentation variability and its effect on CT-based radiomics for pancreatic cancer. An interesting interdisciplinary variability found in this study also introduces new considerations for the deployment of radiomics models.

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.

The Cancer Genome Atlas (TCGA) is one of the largest biorepositories of digital histology. Deep learning (DL) models have been trained on TCGA to predict numerous features directly from histology, including survival, gene expression patterns, and driver mutations. However, we demonstrate that these features vary substantially across tissue submitting sites in TCGA for over 3,000 patients with six cancer subtypes. Additionally, we show that histologic image differences between submitting sites can easily be identified with DL. Site detection remains possible despite commonly used color normalization and augmentation methods, and we quantify the image characteristics constituting this site-specific digital histology signature. We demonstrate that these site-specific signatures lead to biased accuracy for prediction of features including survival, genomic mutations, and tumor stage. Furthermore, ethnicity can also be inferred from site-specific signatures, which must be accounted for to ensure equitable application of DL. These site-specific signatures can lead to overoptimistic estimates of model performance, and we propose a quadratic programming method that abrogates this bias by ensuring models are not trained and validated on samples from the same site., Deep learning models have been trained on The Cancer Genome Atlas to predict numerous features directly from histology, including survival, gene expression patterns, and driver mutations. Here, the authors demonstrate that site-specific histologic signatures can lead to biased estimates of accuracy for such models, and propose a method to minimize such bias.

Although low risk localised prostate cancer has an excellent prognosis owing to effective treatments, such as surgery, radiation, cryosurgery and hormone therapy, metastatic prostate cancer remains incurable. Existing therapeutic regimens prolong life; however, they are beset by problems of resistance, resulting in poor outcomes. Treatment resistance arises primarily from tumour heterogeneity, altered genetic signatures and metabolic reprogramming, all of which enable the tumour to serially adapt to drugs during the course of treatment. In this review, we focus on alterations in the metabolism of prostate cancer, including genetic signatures and molecular pathways associated with metabolic reprogramming. Advances in our understanding of prostate cancer metabolism might help to explain many of the adaptive responses that are induced by therapy, which might, in turn, lead to the attainment of more durable therapeutic responses.

The efficacy of quantitative ultrasound (QUS) multi-parametric imaging in conjunction with unsupervised classification algorithms was investigated for the first time in characterizing intra-tumor regions to predict breast tumor response to chemotherapy before the start of treatment. QUS multi-parametric images of breast tumors were generated using the ultrasound radiofrequency data acquired from 181 patients diagnosed with locally advanced breast cancer and planned for neo-adjuvant chemotherapy followed by surgery. A hidden Markov random field (HMRF) expectation maximization (EM) algorithm was applied to identify distinct intra-tumor regions on QUS multi-parametric images. Several features were extracted from the segmented intra-tumor regions and tumor margin on different parametric images. A multi-step feature selection procedure was applied to construct a QUS biomarker consisting of four features for response prediction. Evaluation results on an independent test set indicated that the developed biomarker coupled with a decision tree model with adaptive boosting (AdaBoost) as the classifier could predict the treatment response of patient at pre-treatment with an accuracy of 85.4% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.89. In comparison, the biomarkers consisted of the features derived from the entire tumor core (without consideration of the intra-tumor regions), and the entire tumor core and the tumor margin could predict the treatment response of patients with an accuracy of 74.5% and 76.4%, and an AUC of 0.79 and 0.76, respectively. Standard clinical features could predict the therapy response with an accuracy of 69.1% and an AUC of 0.6. Long-term survival analyses indicated that the patients predicted by the developed model as responders had a significantly better survival compared to the non-responders. Similar findings were observed for the two response cohorts identified at post-treatment based on standard clinical and pathological criteria. The results obtained in this study demonstrated the potential of QUS multi-parametric imaging integrated with unsupervised learning methods in identifying distinct intra-tumor regions in breast cancer to characterize its responsiveness to chemotherapy prior to the start of treatment.

In 2020, it is estimated that 73,750 kidney cancer cases were diagnosed, and 14,830 people died from cancer in the United States. Preoperative multi-phase abdominal computed tomography (CT) is often used for detecting lesions and classifying histologic subtypes of renal tumor to avoid unnecessary biopsy or surgery. However, there exists inter-observer variability due to subtle differences in the imaging features of tumor subtypes, which makes decisions on treatment challenging. While deep learning has been recently applied to the automated diagnosis of renal tumor, classification of a wide range of subtype classes has not been sufficiently studied yet. In this paper, we propose an end-to-end deep learning model for the differential diagnosis of five major histologic subtypes of renal tumors including both benign and malignant tumors on multi-phase CT. Our model is a unified framework to simultaneously identify lesions and classify subtypes for the diagnosis without manual intervention. We trained and tested the model using CT data from 308 patients who underwent nephrectomy for renal tumors. The model achieved an area under the curve (AUC) of 0.889, and outperformed radiologists for most subtypes. We further validated the model on an independent dataset of 184 patients from The Cancer Imaging Archive (TCIA). The AUC for this dataset was 0.855, and the model performed comparably to the radiologists. These results indicate that our model can achieve similar or better diagnostic performance than radiologists in differentiating a wide range of renal tumors on multi-phase CT.

Fibroblast activation protein inhibitor (FAPI) PET/CT is a new tool in the diagnostic workup of cancer. With a growing volume of applications, pitfalls and common findings need to be considered for (68)Ga-FAPI PET/CT image interpretation. The aim of this study was to summarize common findings and report pitfalls in (68)Ga-FAPI PET/CT. Methods: Ninety-one patients underwent whole-body PET/CT with either FAPI-04 (n = 25) or FAPI-46 (n = 66). Findings were rated in a consensus session of 2 experienced readers. Pitfalls and common findings were defined as focal or localized uptake above the background level and categorized as unspecific or nonmalignant and grouped into degenerative, muscular, scarring/wound-healing, uterine, mammary gland, and head-and-neck findings. The frequency of findings was reported on a per-patient and per-group basis, and SUV(max), SUV(mean), and SUV(peak) were measured. Results: Non–tumor-specific uptake was found in 81.3% of patients. The most frequent finding was uptake in degenerative lesions (51.6%), with a mean SUV(max) of 7.7 ± 2.9, and head-and-neck findings (45.1%). Except for the salivary glands, the uptake values did not differ between 10 and 60 min after injection in most findings. Uterine uptake was found in most women (66.7%), with a mean SUV(max) of 12.2 ± 7.3, and uptake correlated negatively with age (SUV(max), r = −0.6, P < 0.01; SUV(peak), r = −0.57, P < 0.01; SUV(mean), r = −0.58, P < 0.01). Conclusion: Pitfalls include non–tumor-specific (68)Ga-FAPI uptake in degenerative lesions, muscle, the head and neck, scarring, the mammary glands, or the uterus. Here, we summarize the findings to help readers avoid common mistakes at centers introducing (68)Ga-FAPI PET/CT.

The objectives of this study were to assess changes in apparent diffusion coefficient (ADC) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) parameters after radiation therapy (RT) for bone metastases from hepatocellular carcinoma (HCC) and to evaluate their prognostic value. This prospective study was approved by the Institutional Review Board. Fourteen patients with HCC underwent RT (30 Gy in 10 fractions once daily) for bone metastases. The ADC and DCE-MRI parameters and the volume of the target lesions were measured before (baseline) and one month after RT (post-RT). The Wilcoxon signed-rank test was used to compare the parameters between the baseline and post-RT MRI. The parameters were compared between patients with or without disease progression in RT fields using the Mann–Whitney test. Intraclass correlation coefficients were used to evaluate the interobserver agreement. The medians of the ADC, rate constant [kep], and volume fraction of the extravascular extracellular matrix [ve] in the baseline and post-RT MRI were 0.67 (range 0.61–0.72) and 0.75 (range 0.63–1.43) (× 10–3 mm2/s) (P = 0.027), 836.33 (range 301.41–1082.32) and 335.80 (range 21.86–741.87) (× 10–3/min) (P = 0.002), and 161.54 (range 128.38–410.13) and 273.99 (range 181.39–1216.95) (× 10–3) (P = 0.027), respectively. The medians of the percent change in the ADC of post-RT MRI in patients with progressive disease and patients without progressive disease were − 1.35 (range − 6.16 to 6.79) and + 46.71 (range 7.71–112.81) (%) (P = 0.011), respectively. The interobserver agreements for all MRI parameters were excellent (intraclass correlation coefficients > 0.8). In conclusion, the ADC, kep, and ve of bone metastases changed significantly after RT. The percentage change in the ADC was closely related to local tumor progression.

We aimed to investigate the role of the quantitative parameters of dual-energy computed tomography (DECT) in evaluating patients with hepatocellular carcinoma (HCC) treated by transarterial chemoembolization (TACE). We retrospectively identified 80 HCC patients (mean age, 56 years; 61 men) treated by TACE who received contrast-enhanced DECT and were retreated by TACE within 7 days between November 2018 and December 2019. Taking digital subtraction angiography (DSA) and CT images as reference standard, two readers measured and calculated the values of normalized iodine concentration at arterial phase (NICAP), normalized iodine concentration at portal venous phase (NICPP), iodine concentration difference (ICD), arterial iodine fraction (AIF) and slope of the spectral Hounsfield unit curve (λHu) by placing matched regions of interests (ROIs) within the tumor active area (TAA), adjacent normal hepatic parenchyma (ANHP) and tumor necrotic area (TNA). Differences between the parameters were analyzed by the Kruskal–Wallis H test. Receiver operating characteristic analysis of the parameters performance in differentiating the three tissues types was performed. AIF exhibited a good performance in distinguishing TAA (0.93 ± 0.31) and ANHP (0.18 ± 0.14), the areas under the receiver operating characteristic curve (AUC) was 0.989, while the λHu exhibited an excellent performance in distinguishing TAA (3.32 ± 1.24) and TNA (0.29 ± 0.27), with an AUC of 1.000. In conclusion, quantitative DECT can be effectively used to evaluate the tumor viability in HCC patients treated by TACE.

Radiomics studies to predict lymph node (LN) metastasis has only focused on either primary tumor or LN alone. However, combining radiomics features from multiple sources may reflect multiple characteristic of the lesion thereby increasing the discriminative performance of the radiomic model. Therefore, the present study intends to evaluate the efficiency of integrative nomogram, created by combining clinical parameters and radiomics features extracted from gross tumor volume (GTV), peritumoral volume (PTV) and LN, for the preoperative prediction of LN metastasis in clinical cT1N0M0 adenocarcinoma. A primary cohort of 163 patients (training cohort, 113; and internal validation cohort, 50) and an external validation cohort of 53 patients with clinical stage cT1N0M0 were retrospectively included. Features were extracted from three regions of interests (ROIs): GTV; PTV (5.0 mm around the tumor) and LN on pre-operative contrast enhanced computed tomography (CT). LASSO logistic regression method was used to build radiomic signatures. Multivariable regression analysis was used to build a nomogram. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. The discriminative performance of nomogram was validated both internally and externally. The radiomic signatures using the features of GTV, PTV and LN showed a good ability in predicting LN metastasis with an area under the curve (AUC) of 0.74 (95% CI 0.60–0.88), 0.72 (95% CI 0.57–0.87) and 0.64 (95% CI 0.48–0.80) respectively in external validation cohort. The integration of different signature together further increases the discriminatory ability: GTV + PTV (GPTV): AUC 0.75 (95% CI 0.61–0.89) and GPTV + LN: AUC 0.76 (95% CI 0.61–0.91) in external validation cohort. An integrative nomogram of clinical parameters and radiomic features demonstrated further increase in discriminatory ability with AUC of 0.79 (95% CI 0.66–0.93) in external validation cohort. The nomogram showed good calibration. Decision curve analysis demonstrated that the radiomic nomogram was clinically useful. The integration of information from clinical parameters along with CT radiomics information from GTV, PTV and LN was feasible and increases the predictive performance of the nomogram in predicting LN status in cT1N0M0 adenocarcinoma patients suggesting merit of information integration from multiple sources in building prediction model.

Introduction: As a protein-based biomaterial for potential cancer targeting delivery, apoferritin has recently attracted interest.Areas covered: In this review, we discuss the development of this c...

Ovarian cancer is highly metastatic, with a high frequency of relapse, and is the most fatal gynecologic malignancy in women worldwide. It is important to elevate the drug susceptibility and cytotoxicity of ovarian cancer cells, thereby eliminating resident cancer cells for more effective therapeutic efficacy. Here, we developed a bispecific antibody (BsAb; mPEG × HER2) that can easily provide HER2+ tumor tropism to mPEGylated liposomal doxorubicin (PLD) and further increase the drug accumulation in cancer cells via receptor-mediated endocytosis, and improve the cytotoxicity and therapeutic efficacy of HER2+ ovarian tumors. The mPEG × HER2 can simultaneously bind to mPEG molecules on the surface of PLD and HER2 antigen on the surface of ovarian cancer cells. Simply mixing the mPEG × HER2 with PLD was able to confer HER2 specificity of PLD to HER2+ ovarian cancer cells and efficiently trigger endocytosis and enhance cytotoxicity by 5.4-fold as compared to non-targeted PLD. mPEG × HER2-modified PLD was able to significantly increase the targeting and accumulation of HER2+ ovarian tumor by 220% as compared with non-targeted PLD. It could also significantly improve the anti-tumor activity of PLD (P

The purpose of this retrospective study was to investigate the role in staging and prognostic value of pretherapeutic fluorine-18-fluorodeoxyglucose (F-18 FDG) positron emission tomography (PET)/computed tomography (CT) in patients with gastric mucosa-associated lymphoid tissue (MALT) lymphoma without high-grade transformation (HT). We retrospectively reviewed 115 consecutive patients with histopathologically confirmed gastric MALT lymphoma without HT who underwent pretherapeutic F-18 FDG PET/CT. Kaplan–Meier and Cox proportional-hazards regression analyses were used to identify independent prognostic factors for disease free survival (DFS) among 13 clinical parameters and three PET parameters. In two of 115 patients (1.7%), the clinical stage appeared higher according to F-18 FDG PET/CT. In univariate analysis, Helicobacter pylori (HP) infection (P = 0.023), treatment modality (P

This study assessed the possibility of semi-automatic harmonization of standardized uptake values (SUVs) in multicenter studies. Phantom data were acquired using 16 PET/CT scanners (including 3 PET/CT scanners with a silicon photomultiplier detector). PET images obtained using 30-min/bed scans for optimum harmonization filter calculations and using 90–180-s/bed scans for SUV validation under clinical conditions were obtained. Time of flight and a reconstruction method with point-spread function correction were allowed. The optimal full width at half maximum of the 3D-Gaussian filter that minimizes the root mean square error with the median value of the JSNM harmonization range was calculated semi-automatically. The SUVmax and the SUVpeak of the hot spheres were measured, and the inter-scanner coefficient of variation (COV) was calculated before and after harmonization. The harmonization filter was applied to 11 of the 15 PET/CT scanners in which the SUV calibration accuracy had been verified, but not in the remaining 4 scanners. Under noiseless conditions before harmonization, the inter-scanner COVs of the SUVmax and the SUVpeak were as high as 21.57% and 12.20%, respectively, decreasing to 8.79% and 5.73% after harmonization, respectively. Harmonization brought the SUVmax of all the hot spheres to within the harmonization range. Even under clinical conditions affected by image noise, the inter-scanner COVs for the SUVmax and SUVpeak were as high as 8.83% and 5.18% after harmonization, respectively. By applying an optimal harmonization filter that is calculated semi-automatically, the harmonization of SUVs according to the JSNM strategy is possible in multicenter studies, thereby reducing inter-scanner COVs.

Neoadjuvant therapy in breast cancer can downstage axillary lymph nodes and reduce extent of axillary surgery. As such, accurate determination of nodal status after neoadjuvant therapy and before surgery impacts surgical management. There are scarce data on the diagnostic accuracy of breast magnetic resonance imaging (MRI) for nodal evaluation after neoadjuvant therapy in patients with invasive lobular carcinoma (ILC), a diffusely growing tumor type. We retrospectively analyzed patients with stage 1–3 ILC who underwent pre-operative breast MRI after either neoadjuvant chemotherapy or endocrine therapy at our institution between 2006 and 2019. Two breast radiologists reviewed MRIs and evaluated axillary nodes for suspicious features. All patients underwent either sentinel node biopsy or axillary dissection. We evaluated sensitivity, specificity, negative and positive predictive values, and overall accuracy of the post-treatment breast MRI in predicting pathologic nodal status. Of 79 patients, 58.2% received neoadjuvant chemotherapy and 41.8% neoadjuvant endocrine therapy. The sensitivity and negative predictive value of MRI were significantly higher in the neoadjuvant endocrine therapy cohort than in the neoadjuvant chemotherapy cohort (66.7 vs. 37.9%, p = 0.012 and 70.6 vs. 40%, p = 0.007, respectively), while overall accuracy was similar. Upstaging from clinically node negative to pathologically node positive occurred in 28.0 and 41.7%, respectively. In clinically node positive patients, those with an abnormal post-treatment MRI had a significantly higher proportion of patients with ≥4 positive nodes on pathology compared to those with a normal MRI (61.1 versus 16.7%, p = 0.034). Overall, accuracy of breast MRI for predicting nodal status after neoadjuvant therapy in ILC was low in both chemotherapy and endocrine therapy cohorts. However, post-treatment breast MRI may help identify patients with a high burden of nodal disease (≥4 positive nodes), which could impact pre-operative systemic therapy decisions. Further studies are needed to assess other imaging modalities to evaluate for nodal disease following neoadjuvant therapy and to improve clinical staging in patients with ILC.

Encoding the sodium iodide symporter (NIS) by an adenovirus (Ad) is a promising strategy to facilitate non-invasive imaging and radiotherapy of pancreatic cancer. However, insufficient levels of NIS expression in tumor cells have limited its clinical translation. To optimize Ad-based radiotherapy and imaging, we investigated the effect of Ad death protein (ADP) deletion on NIS expression. We cloned two sets of oncolytic NIS-expressing Ads that differed only in the presence or absence of ADP. We found that ADP expression negatively affected NIS membrane localization and inhibited radiotracer uptake. ADP deletion significantly improved NIS-based imaging in pancreatic cancer models including patient-derived xenografts, where effective imaging was possible for up to 6 weeks after a single virus injection. This study demonstrates that improved oncolysis may hinder the therapeutic effect of oncolytic viruses designed to express NIS. In vivo studies in combination with 131I showed potential for effective radiotherapy. This also highlights the need for further investigation into optimal timing of 131I administration and suggests that repeated doses of 131I should be considered to improve efficacy in clinical trials. We conclude that ADP deletion is essential for effective NIS-based theranostics in cancer., Graphical abstract, Sodium iodide symporter (NIS)-expressing oncolytic viruses enable tumor-specific imaging and radiotherapy for infected tumors. We designed a novel NIS-expressing adenoviral vector with deletion of the adenovirus death protein (ADP) gene. ADP deletion improved NIS expression and subsequent imaging capabilities highlighting the role of vector design in effective transgene expression.

Body mass index (BMI) is well known to be associated with poor prognosis in several cancers. The relationship between BMI and the long-term outcomes of patients with intrahepatic cholangiocarcinoma (ICC) is incompletely understood. This study investigated the relationships of BMI with clinicopathological characteristics and patient outcomes, focusing on metabolic activity and immune status. The relationship between BMI and the maximum standardized uptake value (SUVmax) on fluorine-18 fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) was analyzed. In addition, immunohistochemistry was performed for programmed cell death-ligand 1 (PD-L1), cluster of differentiation 8 (CD8), and forkhead box protein P3 (Foxp3). Seventy-four patients with ICC were classified into normal weight (BMI 2, n = 48) and obesity groups (BMI ≥ 25.0 kg/m2, n = 26), respectively. Serum carbohydrate antigen 19–9 levels were higher in the obesity group than in the normal weight group. Tumor size and the intrahepatic metastasis rate were significantly larger in the obesity group. Patients in the obesity group had significantly worse prognoses than those in the normal weight group. Moreover, BMI displayed a positive correlation with SUVmax on 18F-FDG PET/CT (n = 46, r = 0.5152). Patients with high 18F-FDG uptake had a significantly higher rate of PD-L1 expression, lower CD8 + tumor-infiltrating lymphocyte (TIL) counts, and higher Foxp3 + TIL counts. The elevated BMI might predict the outcomes of patients with ICC. Obesity might be associated with ICC progression, possibly through alterations in metabolic activity and the immune status.

Margins of wide local excisions in breast conserving surgery are tested through histology, which can delay results by days and lead to second operations. Detection of margin involvement intraoperatively would allow the removal of additional tissue during the same intervention. X-ray phase contrast imaging (XPCI) provides soft tissue sensitivity superior to conventional X-rays: we propose its use to detect margin involvement intraoperatively. We have developed a system that can perform phase-based computed tomography (CT) scans in minutes, used it to image 101 specimens approximately half of which contained neoplastic lesions, and compared results against those of a commercial system. Histological analysis was carried out on all specimens and used as the gold standard. XPCI-CT showed higher sensitivity (83%, 95% CI 69–92%) than conventional specimen imaging (32%, 95% CI 20–49%) for detection of lesions at margin, and comparable specificity (83%, 95% CI 70–92% vs 86%, 95% CI 73–93%). Within the limits of this study, in particular that specimens obtained from surplus tissue typically contain small lesions which makes detection more difficult for both methods, we believe it likely that the observed increase in sensitivity will lead to a comparable reduction in the number of re-operations.

Meningioma is the most common primary intracranial extra-axial tumor. Total surgical removal is the standard therapeutic method to treat this type of brain tumors. However, the risk of recurrence depends on the tumor grade and the extent of the resection including the infiltrated dura mater and, if necessary, the infiltrated bone. Therefore, proper resection of all invasive tumor borders without touching eloquent areas is of primordial in order to decrease the risk of recurrence. Nowadays, none of the intraoperative used tools is able to provide a precise real-time histopathological information on the tumor surrounding areas to help the surgeon to achieve a gross total removal. To respond to this problem, our team is developing a multimodal two-photon fluorescence endomicroscope, compatible with the surgeon tool, to better delimitate tumor boundaries, relying on the endogenous fluorescence of brain tissues. In this context, we are building a tissue database in order to specify each brain tissue, whether healthy or tumoral, with its specific optical signature. In this study, we present a multimodal and multiscale optical measurements on non-tumoral control brain tissue obtained in epilepsy surgery patients and several meningioma grades. We investigated tissue auto-fluorescence to track the molecular changes associated with the tumor grade from deep ultra-violet (DUV) to near infrared (NIR) excitation. Micro-spectroscopy, fluorescence lifetime imaging, two-photon fluorescence imaging and Second Harmonic Generation (SHG) imaging were performed. Several optically derived parameters such as collagen crosslinks fluorescence in DUV, SHG emission in NIR and long lifetime intensity fraction of Nicotinamide Adenine Dinucleotide and Flavins were correlated to discriminate cancerous tissue from control one. While collagen response managed to discriminate meningioma grades from control samples with a 100% sensitivity and 90% specificity through a 3D discriminative algorithm.

This study aimed to construct convention-radiomics CT nomogram containing conventional CT characteristics and radiomics signature for distinguishing fat-poor angiomyolipoma (fp-AML) from clear-cell renal cell carcinoma (ccRCC). 29 fp-AML and 110 ccRCC patients were enrolled and underwent CT examinations in this study. The radiomics-only logistic model was constructed with selected radiomics features by the analysis of variance (ANOVA)/Mann–Whitney (MW), correlation analysis, and Least Absolute Shrinkage and Selection Operator (LASSO), and the radiomics score (rad-score) was computed. The convention-radiomics logistic model based on independent conventional CT risk factors and rad-score was constructed for differentiating. Then the relevant nomogram was developed. Receiver operation characteristic (ROC) curves were calculated to quantify the accuracy for distinguishing. The rad-score of ccRCC was smaller than that of fp-AML. The convention-radioimics logistic model was constructed containing variables of enhancement pattern, VUP, and rad-score. To the entire cohort, the area under the curve (AUC) of convention-radiomics model (0.968 [95% CI 0.923–0.990]) was higher than that of radiomics-only model (0.958 [95% CI 0.910–0.985]). Our study indicated that convention-radiomics CT nomogram including conventional CT risk factors and radiomics signature exhibited better performance in distinguishing fp-AML from ccRCC.

Sarcomatoid differentiation in RCC (sRCC) is associated with a poor prognosis, necessitating more aggressive management than RCC without sarcomatoid components (nsRCC). Since suspected renal cell carcinoma (RCC) tumors are not routinely biopsied for histologic evaluation, there is a clinical need for a non-invasive method to detect sarcomatoid differentiation pre-operatively. We utilized unsupervised self-organizing map (SOM) and supervised Learning Vector Quantizer (LVQ) machine learning to classify RCC tumors on T2-weighted, non-contrast T1-weighted fat-saturated, contrast-enhanced arterial-phase T1-weighted fat-saturated, and contrast-enhanced venous-phase T1-weighted fat-saturated MRI images. The SOM was trained on 8 nsRCC and 8 sRCC tumors, and used to compute Activation Maps for each training, validation (3 nsRCC and 3 sRCC), and test (5 nsRCC and 5 sRCC) tumor. The LVQ classifier was trained and optimized on Activation Maps from the 22 training and validation cohort tumors, and tested on Activation Maps of the 10 unseen test tumors. In this preliminary study, the SOM-LVQ model achieved a hold-out testing accuracy of 70% in the task of identifying sarcomatoid differentiation in RCC on standard multiparameter MRI (mpMRI) images. We have demonstrated a combined SOM-LVQ machine learning approach that is suitable for analysis of limited mpMRI datasets for the task of differential diagnosis.

How to differentiate with MRI-based techniques testicular germ (TGCTs) and testicular non-germ cell tumors (TNGCTs) is still under debate and Radiomics may be the turning key. Our purpose is to investigate the performance of MRI-based Radiomics signatures for the preoperative prediction of testicular neoplasm histology. The aim is twofold: (i), differentiating TGCTs and TNGCTs status and (ii) differentiating seminomas (SGCTs) from non-seminomatous (NSGCTs). Forty-two patients with pathology-proven testicular neoplasms and referred for pre-treatment MRI, were retrospectively enrolled. Thirty-two out of 44 lesions were TGCTs. Twelve out of 44 were TNGCTs or other histologies. Two radiologists segmented the volume of interest on T2-weighted images. Approximately 500 imaging features were extracted. Least Absolute Shrinkage and Selection Operator (LASSO) was applied as method for variable selection. A linear model and a linear support vector machine (SVM) were trained with selected features to assess discrimination scores for the two endpoints. LASSO identified 3 features that were employed to build fivefold validated linear discriminant and linear SVM classifiers for the TGCT-TNGCT endpoint giving an overall accuracy of 89%. Four features were employed to build another SVM for the SGCT-SNGCT endpoint with an overall accuracy of 86%. The data obtained proved that T2-weighted-based Radiomics is a promising tool in the diagnostic workup of testicular neoplasms by discriminating germ cell from non-gem cell tumors, and seminomas from non-seminomas.

Deep learning for digital pathology is hindered by the extremely high spatial resolution of whole-slide images (WSIs). Most studies have employed patch-based methods, which often require detailed annotation of image patches. This typically involves laborious free-hand contouring on WSIs. To alleviate the burden of such contouring and obtain benefits from scaling up training with numerous WSIs, we develop a method for training neural networks on entire WSIs using only slide-level diagnoses. Our method leverages the unified memory mechanism to overcome the memory constraint of compute accelerators. Experiments conducted on a data set of 9662 lung cancer WSIs reveal that the proposed method achieves areas under the receiver operating characteristic curve of 0.9594 and 0.9414 for adenocarcinoma and squamous cell carcinoma classification on the testing set, respectively. Furthermore, the method demonstrates higher classification performance than multiple-instance learning as well as strong localization results for small lesions through class activation mapping., Deep learning for digital pathology is hindered by the extremely high spatial resolution of whole slide images (WSIs), which requires researchers to adopt patch-based methods and laborious free-hand contouring. Here, the authors develop a whole-slide training method to classify types of lung cancers using slide-level diagnoses with deep learning.

Positron emission tomography using the fluorine-18 (18F) fluciclovine radiotracer has been approved for use in recurrent prostate cancer and is a useful tool for clinical decision making. However, 18F-fluciclovine is not specific for prostate cancer tumor cells, and false-positive results have been reported. In the present study, we have reported our experience with synchronous malignancies identified using 18F-fluciclovine and reviewed other reported cases, with a special emphasis on highlighting the clinical decisions that led to the correct diagnosis.

There is a wide range of equivocal melanocytic lesions that can be clinically and dermoscopically indistinguishable from early melanoma. In the present work, we assessed the possibilities of combined using of multiphoton microscopy (MPM) and optical coherence angiography (OCA) for differential diagnosis of the equivocal melanocytic lesions. Clinical and dermoscopic examinations of 60 melanocytic lesions revealed 10 benign lesions and 32 melanomas, while 18 lesions remained difficult to diagnose. Histopathological analysis of these lesions revealed 4 intradermal, 3 compound and 3 junctional nevi in the “benign” group, 7 superficial spreading, 14 lentigo maligna and 11 nodular melanomas in the “melanoma” group and 2 lentigo simplex, 4 dysplastic nevi, 6 melanomas in situ, 4 invasive lentigo melanomas and 2 invasive superficial spreading melanomas in the “equivocal” group. On the basis of MPM, a multiphoton microscopy score (MPMS) has been developed for quantitative assessment of melanoma features at the cellular level, that showed lower score for benign lesions compare with malignant ones. OCA revealed that the invasive melanoma has a higher vessel density and thicker blood vessels than melanoma in situ and benign lesions. Discriminant functions analysis of MPM and OCA data allowed to differentiate correctly between all equivocal melanocytic lesions. Therefore, we demonstrate, for the first time, that a combined use of MPM and OCA has the potential to improve early diagnosis of melanoma.

Cancer theranostics is a new strategy for combating cancer that integrates cancer imaging and treatment through theranostic agents to provide an efficient and safe way to improve cancer prognosis. Design and synthesis of these cancer theranostic agents are crucial since these agents are required to be biocompatible, tumor-specific, imaging distinguishable and therapeutically efficacious. In this regard, several types of gadolinium (Gd)-based nanomaterials have been introduced to combine different therapeutic agents with Gd to enhance the efficacy of therapeutic agents. At the same time, the entire treatment procedure could be monitored via imaging tools due to incorporation of Gd ions, Gd chelates and Gd/other imaging probes in the theranostic agents. This review aims to overview recent advances in the Gd-based nanomaterials for cancer theranostics and perspectives for Gd nanomaterial-based cancer theranostics are provided.

Immunotherapy has revolutionized the treatment of several malignancies. Notwithstanding the encouraging results, many patients do not respond to treatments. Evaluation of the efficacy of treatments is challenging and robust methods to predict the response to treatment are not yet available. The outcome of immunotherapy results from changes that treatment evokes in the tumor immune landscape. Therefore, a better understanding of the dynamics of immune cells that infiltrate into the tumor microenvironment may fundamentally help in addressing this challenge and provide tools to assess or even predict the response. Noninvasive imaging approaches, such as PET and SPECT that provide whole-body images are currently seen as the most promising tools that can shed light on the events happening in tumors in response to treatment. Such tools can provide critical information that can be used to make informed clinical decisions. Here, we review recent developments in the field of noninvasive cancer imaging with a focus on immunotherapeutics and nuclear imaging technologies and will discuss how the field can move forward to address the challenges that remain unresolved.

Although metabolic intratumoral heterogeneity (ITH) gives important value on treatment responses and prognoses, its association with treatment outcomes have not been reported in gastric cancer (GC). We aimed to evaluate temporal changes in metabolic ITH and the associations with treatment responses, progression-free survival (PFS), and overall survival (OS) in advanced GC patients. Eighty-five patients with unresectable, locally advanced, or metastatic GC were prospectively enrolled before the first-line palliative chemotherapy and underwent [18F]FDG PET at baseline (TP1) and the first response follow-up evaluation (TP2). Standardized uptake values (SUVs), volumetric parameters, and textural features were evaluated in primary gastric tumor at TP1 and TP2. Of 85 patients, 44 had partial response, 33 had stable disease, and 8 progressed. From TP1 to TP2, metabolic ITH was significantly reduced (P P max at TP2, a high kurtosis at TP2 and larger decreases in the coefficient of variance were associated with better PFS. A low SUVmax at TP2, larger decreases in the metabolic tumor volume and larger decreased in the energy were associated with better OS. Age older than 60 years and responders also showed better OS. An early reduction in metabolic ITH is useful to predict treatment outcomes in advanced GC patients.

Oral squamous cell carcinoma (OSCC) accounts for a third of the cancer burden in India, with a correspondingly high cancer-specific mortality. Although treatment of OSCC in India mirrors that of high-income nations, extreme burden of disease, late presentation, and the associated advanced stage of disease pose unique challenges in a resource-constrained environment. Despite a multimodal treatment paradigm, survival rates are low. Often the cause for late presentation is the delayed diagnosis, inappropriate investigation and referral, and compromised or incorrect treatment, leading to poor patient outcomes and costs to the health-care provider. To address these issues, the first UK-India Symposium on Advances in Oral Cancer Imaging Symposium was organized in Bangalore, India, in April 2019; participants included radiologists, imaging scientists, clinicians, and data scientists from the United Kingdom, India, Singapore, and the United States. Following the discussions held during this meeting, in this manuscript, we present evidence-based guidance for the role of imaging in OSCC, recommendations for service development, and details of future potential for evolution in head and neck imaging.

Telomere maintenance is a universal hallmark of cancer. Most tumors including low-grade oligodendrogliomas use telomerase reverse transcriptase (TERT) expression for telomere maintenance while astrocytomas use the alternative lengthening of telomeres (ALT) pathway. Although TERT and ALT are hallmarks of tumor proliferation and attractive therapeutic targets, translational methods of imaging TERT and ALT are lacking. Here we show that TERT and ALT are associated with unique 1H-magnetic resonance spectroscopy (MRS)-detectable metabolic signatures in genetically-engineered and patient-derived glioma models and patient biopsies. Importantly, we have leveraged this information to mechanistically validate hyperpolarized [1-13C]-alanine flux to pyruvate as an imaging biomarker of ALT status and hyperpolarized [1-13C]-alanine flux to lactate as an imaging biomarker of TERT status in low-grade gliomas. Collectively, we have identified metabolic biomarkers of TERT and ALT status that provide a way of integrating critical oncogenic information into non-invasive imaging modalities that can improve tumor diagnosis and treatment response monitoring., Telomerase expression and the alternative lengthening of telomeres pathway are hallmarks of cancer. Here, the authors show that, in primary brain tumors, these features are correlated with metabolic signatures detectable by metabolic imaging, suggesting that they can be used to non-invasively monitor telomere maintenance in brain tumours.

Cachexia is a significant contributor to cancer mortality as it is responsible for up to 30% of cancer deaths. Magnetic resonance imaging offers a noninvasive approach to detect features of cachexia. T1-weighted images of cachectic patients have a “pseudo fat-saturated” appearance secondary to disappearance of subcutaneous and fascial fat throughout the body, as well as fat in the bone marrow. Orbital fat remains preserved until late disease. We present 2 cases with these classic imaging findings of cancer cachexia in the subcutaneous tissues of the head, neck, and spine. This imaging phenomenon is often misinterpreted by radiologists and may lead to delayed diagnosis or unnecessary repeat imaging.