Evaluation of a medical student-delivered smoking prevention program utilizing a face-aging mobile app for secondary schools in Germany: The Education Against Tobacco cluster-randomized controlled trial.

BACKGROUND: To reduce smoking uptake in adolescents, the medical students' network Education Against Tobacco (EAT) has developed a school-based intervention involving a face-aging mobile app (Smokerface). METHODS: A two-arm cluster-randomized controlled trial was conducted, evaluating the 2016 EAT intervention, which employed the mobile app Smokerface and which was delivered by medical students. Schools were randomized to intervention or control group. Surveys were conducted at baseline (pre-intervention) and at 9, 16, and 24 months post-intervention via paper & pencil questionnaires. The primary outcome was the difference in within-group changes in smoking prevalence between intervention and control group at 24 months. RESULTS: Overall, 144 German secondary schools comprising 11,286 pupils participated in the baseline survey, of which 100 schools participated in the baseline and at least one of the follow-up surveys, yielding 7437 pupils in the analysis sample. After 24 months, smoking prevalence was numerically lower in the intervention group compared to control group (12.9 % vs. 14.3 %); however, between-group differences in change in smoking prevalence between baseline and 24-months follow-up (OR=0.83, 95 %-CI: 0.64-1.09) were not statistically significant (p = 0.176). Intention to start smoking among baseline non-smokers declined non-significantly in the intervention group (p = 0.064), and remained essentially unchanged in the control group, but between-group differences in changes at the 24-months follow-up (OR=0.88, 0.64-1.21) were not statistically significant (p = 0.417). CONCLUSION: While a trend towards beneficial effects of the intervention regarding smoking prevalence as well as intention to start smoking among baseline non-smokers was observed, our smoking prevention trial demonstrated no significant effect of the intervention.

A 3-dimensional histology computer model of malignant melanoma and its implications for digital pathology.

BACKGROUND: Historically, cancer diagnoses have been made by pathologists using two-dimensional histological slides. However, with the advent of digital pathology and artificial intelligence, slides are being digitised, providing new opportunities to integrate their information. Since nature is 3-dimensional (3D), it seems intuitive to digitally reassemble the 3D structure for diagnosis. OBJECTIVE: To develop the first human-3D-melanoma-histology-model with full data and code availability. Further, to evaluate the 3D-simulation together with experienced pathologists in the field and discuss the implications of digital 3D-models for the future of digital pathology. METHODS: A malignant melanoma of the skin was digitised via 3 µm cuts by a slide scanner; an open-source software was then leveraged to construct the 3D model. A total of nine pathologists from four different countries with at least 10 years of experience in the histologic diagnosis of melanoma tested the model and discussed their experiences as well as implications for future pathology. RESULTS: We successfully constructed and tested the first 3D-model of human melanoma. Based on testing, 88.9% of pathologists believe that the technology is likely to enter routine pathology within the next 10 years; advantages include a better reflectance of anatomy, 3D assessment of symmetry and the opportunity to simultaneously evaluate different tissue levels at the same time; limitations include the high consumption of tissue and a yet inferior resolution due to computational limitations. CONCLUSIONS: 3D-histology-models are promising for digital pathology of cancer and melanoma specifically, however, there are yet limitations which need to be carefully addressed.

A self-supervised vision transformer to predict survival from histopathology in renal cell carcinoma.

PURPOSE: To develop and validate an interpretable deep learning model to predict overall and disease-specific survival (OS/DSS) in clear cell renal cell carcinoma (ccRCC). METHODS: Digitised haematoxylin and eosin-stained slides from The Cancer Genome Atlas were used as a training set for a vision transformer (ViT) to extract image features with a self-supervised model called DINO (self-distillation with no labels). Extracted features were used in Cox regression models to prognosticate OS and DSS. Kaplan-Meier for univariable evaluation and Cox regression analyses for multivariable evaluation of the DINO-ViT risk groups were performed for prediction of OS and DSS. For validation, a cohort from a tertiary care centre was used. RESULTS: A significant risk stratification was achieved in univariable analysis for OS and DSS in the training (n = 443, log rank test, p < 0.01) and validation set (n = 266, p < 0.01). In multivariable analysis, including age, metastatic status, tumour size and grading, the DINO-ViT risk stratification was a significant predictor for OS (hazard ratio [HR] 3.03; 95%-confidence interval [95%-CI] 2.11-4.35; p < 0.01) and DSS (HR 4.90; 95%-CI 2.78-8.64; p < 0.01) in the training set but only for DSS in the validation set (HR 2.31; 95%-CI 1.15-4.65; p = 0.02). DINO-ViT visualisation showed that features were mainly extracted from nuclei, cytoplasm, and peritumoural stroma, demonstrating good interpretability. CONCLUSION: The DINO-ViT can identify high-risk patients using histological images of ccRCC. This model might improve individual risk-adapted renal cancer therapy in the future.

Implementation of Whole-Genome and Transcriptome Sequencing Into Clinical Cancer Care.

PURPOSE: The combination of whole-genome and transcriptome sequencing (WGTS) is expected to transform diagnosis and treatment for patients with cancer. WGTS is a comprehensive precision diagnostic test that is starting to replace the standard of care for oncology molecular testing in health care systems around the world; however, the implementation and widescale adoption of this best-in-class testing is lacking. METHODS: Here, we address the barriers in integrating WGTS for cancer diagnostics and treatment selection and answer questions regarding utility in different cancer types, cost-effectiveness and affordability, and other practical considerations for WGTS implementation. RESULTS: We review the current studies implementing WGTS in health care systems and provide a synopsis of the clinical evidence and insights into practical considerations for WGTS implementation. We reflect on regulatory, costs, reimbursement, and incidental findings aspects of this test. CONCLUSION: WGTS is an appropriate comprehensive clinical test for many tumor types and can replace multiple, cascade testing approaches currently performed. Decreasing sequencing cost, increasing number of clinically relevant aberrations and discovery of more complex biomarkers of treatment response, should pave the way for health care systems and laboratories in implementing WGTS into clinical practice, to transform diagnosis and treatment for patients with cancer.

Deep learning can predict survival directly from histology in clear cell renal cell carcinoma.

For clear cell renal cell carcinoma (ccRCC) risk-dependent diagnostic and therapeutic algorithms are routinely implemented in clinical practice. Artificial intelligence-based image analysis has the potential to improve outcome prediction and thereby risk stratification. Thus, we investigated whether a convolutional neural network (CNN) can extract relevant image features from a representative hematoxylin and eosin-stained slide to predict 5-year overall survival (5y-OS) in ccRCC. The CNN was trained to predict 5y-OS in a binary manner using slides from TCGA and validated using an independent in-house cohort. Multivariable logistic regression was used to combine of the CNNs prediction and clinicopathological parameters. A mean balanced accuracy of 72.0% (standard deviation [SD] = 7.9%), sensitivity of 72.4% (SD = 10.6%), specificity of 71.7% (SD = 11.9%) and area under receiver operating characteristics curve (AUROC) of 0.75 (SD = 0.07) was achieved on the TCGA training set (n = 254 patients / WSIs) using 10-fold cross-validation. On the external validation cohort (n = 99 patients / WSIs), mean accuracy, sensitivity, specificity and AUROC were 65.5% (95%-confidence interval [CI]: 62.9-68.1%), 86.2% (95%-CI: 81.8-90.5%), 44.9% (95%-CI: 40.2-49.6%), and 0.70 (95%-CI: 0.69-0.71). A multivariable model including age, tumor stage and metastasis yielded an AUROC of 0.75 on the TCGA cohort. The inclusion of the CNN-based classification (Odds ratio = 4.86, 95%-CI: 2.70-8.75, p < 0.01) raised the AUROC to 0.81. On the validation cohort, both models showed an AUROC of 0.88. In univariable Cox regression, the CNN showed a hazard ratio of 3.69 (95%-CI: 2.60-5.23, p < 0.01) on TCGA and 2.13 (95%-CI: 0.92-4.94, p = 0.08) on external validation. The results demonstrate that the CNN's image-based prediction of survival is promising and thus this widely applicable technique should be further investigated with the aim of improving existing risk stratification in ccRCC.

Model soups improve performance of dermoscopic skin cancer classifiers.

BACKGROUND: Image-based cancer classifiers suffer from a variety of problems which negatively affect their performance. For example, variation in image brightness or different cameras can already suffice to diminish performance. Ensemble solutions, where multiple model predictions are combined into one, can improve these problems. However, ensembles are computationally intensive and less transparent to practitioners than single model solutions. Constructing model soups, by averaging the weights of multiple models into a single model, could circumvent these limitations while still improving performance. OBJECTIVE: To investigate the performance of model soups for a dermoscopic melanoma-nevus skin cancer classification task with respect to (1) generalisation to images from other clinics, (2) robustness against small image changes and (3) calibration such that the confidences correspond closely to the actual predictive uncertainties. METHODS: We construct model soups by fine-tuning pre-trained models on seven different image resolutions and subsequently averaging their weights. Performance is evaluated on a multi-source dataset including holdout and external components. RESULTS: We find that model soups improve generalisation and calibration on the external component while maintaining performance on the holdout component. For robustness, we observe performance improvements for pertubated test images, while the performance on corrupted test images remains on par. CONCLUSIONS: Overall, souping for skin cancer classifiers has a positive effect on generalisation, robustness and calibration. It is easy for practitioners to implement and by combining multiple models into a single model, complexity is reduced. This could be an important factor in achieving clinical applicability, as less complexity generally means more transparency.

Explainable artificial intelligence in skin cancer recognition: A systematic review.

BACKGROUND: Due to their ability to solve complex problems, deep neural networks (DNNs) are becoming increasingly popular in medical applications. However, decision-making by such algorithms is essentially a black-box process that renders it difficult for physicians to judge whether the decisions are reliable. The use of explainable artificial intelligence (XAI) is often suggested as a solution to this problem. We investigate how XAI is used for skin cancer detection: how is it used during the development of new DNNs? What kinds of visualisations are commonly used? Are there systematic evaluations of XAI with dermatologists or dermatopathologists? METHODS: Google Scholar, PubMed, IEEE Explore, Science Direct and Scopus were searched for peer-reviewed studies published between January 2017 and October 2021 applying XAI to dermatological images: the search terms histopathological image, whole-slide image, clinical image, dermoscopic image, skin, dermatology, explainable, interpretable and XAI were used in various combinations. Only studies concerned with skin cancer were included. RESULTS: 37 publications fulfilled our inclusion criteria. Most studies (19/37) simply applied existing XAI methods to their classifier to interpret its decision-making. Some studies (4/37) proposed new XAI methods or improved upon existing techniques. 14/37 studies addressed specific questions such as bias detection and impact of XAI on man-machine-interactions. However, only three of them evaluated the performance and confidence of humans using CAD systems with XAI. CONCLUSION: XAI is commonly applied during the development of DNNs for skin cancer detection. However, a systematic and rigorous evaluation of its usefulness in this scenario is lacking.

Deep learning can predict lymph node status directly from histology in colorectal cancer.

BACKGROUND: Lymph node status is a prognostic marker and strongly influences therapeutic decisions in colorectal cancer (CRC). OBJECTIVES: The objective of the study is to investigate whether image features extracted by a deep learning model from routine histological slides and/or clinical data can be used to predict CRC lymph node metastasis (LNM). METHODS: Using histological whole slide images (WSIs) of primary tumours of 2431 patients in the DACHS cohort, we trained a convolutional neural network to predict LNM. In parallel, we used clinical data derived from the same cases in logistic regression analyses. Subsequently, the slide-based artificial intelligence predictor (SBAIP) score was included in the regression. WSIs and data from 582 patients of the TCGA cohort were used as the external test set. RESULTS: On the internal test set, the SBAIP achieved an area under receiver operating characteristic (AUROC) of 71.0%, the clinical classifier achieved an AUROC of 67.0% and a combination of the two classifiers yielded an improvement to 74.1%. Whereas the clinical classifier's performance remained stable on the TCGA set, performance of the SBAIP dropped to an AUROC of 61.2%. Performance of the clinical classifier depended strongly on the T stage. CONCLUSION: Deep learning-based image analysis may help predict LNM of patients with CRC using routine histological slides. Combination with clinical data such as T stage might be useful. Strategies to increase performance of the SBAIP on external images should be investigated.

Gastrointestinal cancer classification and prognostication from histology using deep learning: Systematic review.

BACKGROUND: Gastrointestinal cancers account for approximately 20% of all cancer diagnoses and are responsible for 22.5% of cancer deaths worldwide. Artificial intelligence-based diagnostic support systems, in particular convolutional neural network (CNN)-based image analysis tools, have shown great potential in medical computer vision. In this systematic review, we summarise recent studies reporting CNN-based approaches for digital biomarkers for characterization and prognostication of gastrointestinal cancer pathology. METHODS: Pubmed and Medline were screened for peer-reviewed papers dealing with CNN-based gastrointestinal cancer analyses from histological slides, published between 2015 and 2020.Seven hundred and ninety titles and abstracts were screened, and 58 full-text articles were assessed for eligibility. RESULTS: Sixteen publications fulfilled our inclusion criteria dealing with tumor or precursor lesion characterization or prognostic and predictive biomarkers: 14 studies on colorectal or rectal cancer, three studies on gastric cancer and none on esophageal cancer. These studies were categorised according to their end-points: polyp characterization, tumor characterization and patient outcome. Regarding the translation into clinical practice, we identified several studies demonstrating generalization of the classifier with external tests and comparisons with pathologists, but none presenting clinical implementation. CONCLUSIONS: Results of recent studies on CNN-based image analysis in gastrointestinal cancer pathology are promising, but studies were conducted in observational and retrospective settings. Large-scale trials are needed to assess performance and predict clinical usefulness. Furthermore, large-scale trials are required for approval of CNN-based prediction models as medical devices.

A benchmark for neural network robustness in skin cancer classification.

BACKGROUND: One prominent application for deep learning-based classifiers is skin cancer classification on dermoscopic images. However, classifier evaluation is often limited to holdout data which can mask common shortcomings such as susceptibility to confounding factors. To increase clinical applicability, it is necessary to thoroughly evaluate such classifiers on out-of-distribution (OOD) data. OBJECTIVE: The objective of the study was to establish a dermoscopic skin cancer benchmark in which classifier robustness to OOD data can be measured. METHODS: Using a proprietary dermoscopic image database and a set of image transformations, we create an OOD robustness benchmark and evaluate the robustness of four different convolutional neural network (CNN) architectures on it. RESULTS: The benchmark contains three data sets-Skin Archive Munich (SAM), SAM-corrupted (SAM-C) and SAM-perturbed (SAM-P)-and is publicly available for download. To maintain the benchmark's OOD status, ground truth labels are not provided and test results should be sent to us for assessment. The SAM data set contains 319 unmodified and biopsy-verified dermoscopic melanoma (n = 194) and nevus (n = 125) images. SAM-C and SAM-P contain images from SAM which were artificially modified to test a classifier against low-quality inputs and to measure its prediction stability over small image changes, respectively. All four CNNs showed susceptibility to corruptions and perturbations. CONCLUSIONS: This benchmark provides three data sets which allow for OOD testing of binary skin cancer classifiers. Our classifier performance confirms the shortcomings of CNNs and provides a frame of reference. Altogether, this benchmark should facilitate a more thorough evaluation process and thereby enable the development of more robust skin cancer classifiers.

Deep learning approach to predict sentinel lymph node status directly from routine histology of primary melanoma tumours.

AIM: Sentinel lymph node status is a central prognostic factor for melanomas. However, the surgical excision involves some risks for affected patients. In this study, we therefore aimed to develop a digital biomarker that can predict lymph node metastasis non-invasively from digitised H&E slides of primary melanoma tumours. METHODS: A total of 415 H&E slides from primary melanoma tumours with known sentinel node (SN) status from three German university hospitals and one private pathological practice were digitised (150 SN positive/265 SN negative). Two hundred ninety-one slides were used to train artificial neural networks (ANNs). The remaining 124 slides were used to test the ability of the ANNs to predict sentinel status. ANNs were trained and/or tested on data sets that were matched or not matched between SN-positive and SN-negative cases for patient age, ulceration, and tumour thickness, factors that are known to correlate with lymph node status. RESULTS: The best accuracy was achieved by an ANN that was trained and tested on unmatched cases (61.8% ± 0.2%) area under the receiver operating characteristic (AUROC). In contrast, ANNs that were trained and/or tested on matched cases achieved (55.0% ± 3.5%) AUROC or less. CONCLUSION: Our results indicate that the image classifier can predict lymph node status to some, albeit so far not clinically relevant, extent. It may do so by mostly detecting equivalents of factors on histological slides that are already known to correlate with lymph node status. Our results provide a basis for future research with larger data cohorts.

MicroRNA-sensitive oncolytic measles virus for chemovirotherapy of pancreatic cancer.

Advanced pancreatic cancer is characterized by few treatment options and poor outcomes. Oncolytic virotherapy and chemotherapy involve complementary pharmacodynamics and could synergize to improve therapeutic efficacy. Likewise, multimodality treatment may cause additional toxicity, and new agents have to be safe. Balancing both aims, we generated an oncolytic measles virus for 5-fluorouracil-based chemovirotherapy of pancreatic cancer with enhanced tumor specificity through microRNA-regulated vector tropism. The resulting vector encodes a bacterial prodrug convertase, cytosine deaminase-uracil phosphoribosyl transferase, and carries synthetic miR-148a target sites in the viral F gene. Combination of the armed and targeted virus with 5-fluorocytosine, a prodrug of 5-fluorouracil, resulted in cytotoxicity toward both infected and bystander pancreatic cancer cells. In pancreatic cancer xenografts, a single intratumoral injection of the virus induced robust in vivo expression of prodrug convertase. Based on intratumoral transgene expression kinetics, we devised a chemovirotherapy regimen to assess treatment efficacy. Concerted multimodality treatment with intratumoral virus and systemic prodrug administration delayed tumor growth and prolonged survival of xenograft-bearing mice. Our results demonstrate that 5-fluorouracil-based chemovirotherapy with microRNA-sensitive measles virus is an effective strategy against pancreatic cancer at a favorable therapeutic index that warrants future clinical translation.

The balance between the intronic miR-342 and its host gene Evl determines hematopoietic cell fate decision.

Protein-coding and non-coding genes like miRNAs tightly control hematopoietic differentiation programs. Although miRNAs are frequently located within introns of protein-coding genes, the molecular interplay between intronic miRNAs and their host genes is unclear. By genomic integration site mapping of gamma-retroviral vectors in genetically corrected peripheral blood from gene therapy patients, we identified the EVL/MIR342 gene locus as a hotspot for therapeutic vector insertions indicating its accessibility and expression in human hematopoietic stem and progenitor cells. We therefore asked if and how EVL and its intronic miRNA-342 regulate hematopoiesis. Here we demonstrate that overexpression (OE) of Evl in murine primary Lin- Sca1+ cKit+ cells drives lymphopoiesis whereas miR-342 OE increases myeloid colony formation in vitro and in vivo, going along with a profound upregulation of canonical pathways essential for B-cell development or myelopoietic functions upon Evl or miR-342 OE, respectively. Strikingly, miR-342 counteracts its host gene by targeting lymphoid signaling pathways, resulting in reduced pre-B-cell output. Moreover, EVL overexpression is associated with lymphoid leukemia in patients. In summary, our data show that one common gene locus regulates distinct hematopoietic differentiation programs depending on the gene product expressed, and that the balance between both may determine hematopoietic cell fate decision.

Combining CNN-based histologic whole slide image analysis and patient data to improve skin cancer classification.

BACKGROUND: Clinicians and pathologists traditionally use patient data in addition to clinical examination to support their diagnoses. OBJECTIVES: We investigated whether a combination of histologic whole slides image (WSI) analysis based on convolutional neural networks (CNNs) and commonly available patient data (age, sex and anatomical site of the lesion) in a binary melanoma/nevus classification task could increase the performance compared with CNNs alone. METHODS: We used 431 WSIs from two different laboratories and analysed the performance of classifiers that used the image or patient data individually or three common fusion techniques. Furthermore, we tested a naive combination of patient data and an image classifier: for cases interpreted as 'uncertain' (CNN output score <0.7), the decision of the CNN was replaced by the decision of the patient data classifier. RESULTS: The CNN on its own achieved the best performance (mean ± standard deviation of five individual runs) with AUROC of 92.30% ± 0.23% and balanced accuracy of 83.17% ± 0.38%. While the classification performance was not significantly improved in general by any of the tested fusions, naive strategy of replacing the image classifier with the patient data classifier on slides with low output scores improved balanced accuracy to 86.72% ± 0.36%. CONCLUSION: In most cases, the CNN on its own was so accurate that patient data integration did not provide any benefit. However, incorporating patient data for lesions that were classified by the CNN with low 'confidence' improved balanced accuracy.

Deep learning approach to predict lymph node metastasis directly from primary tumour histology in prostate cancer.

OBJECTIVE: To develop a new digital biomarker based on the analysis of primary tumour tissue by a convolutional neural network (CNN) to predict lymph node metastasis (LNM) in a cohort matched for already established risk factors. PATIENTS AND METHODS: Haematoxylin and eosin (H&E) stained primary tumour slides from 218 patients (102 N+; 116 N0), matched for Gleason score, tumour size, venous invasion, perineural invasion and age, who underwent radical prostatectomy were selected to train a CNN and evaluate its ability to predict LN status. RESULTS: With 10 models trained with the same data, a mean area under the receiver operating characteristic curve (AUROC) of 0.68 (95% confidence interval [CI] 0.678-0.682) and a mean balanced accuracy of 61.37% (95% CI 60.05-62.69%) was achieved. The mean sensitivity and specificity was 53.09% (95% CI 49.77-56.41%) and 69.65% (95% CI 68.21-71.1%), respectively. These results were confirmed via cross-validation. The probability score for LNM prediction was significantly higher on image sections from N+ samples (mean [SD] N+ probability score 0.58 [0.17] vs 0.47 [0.15] N0 probability score, P = 0.002). In multivariable analysis, the probability score of the CNN (odds ratio [OR] 1.04 per percentage probability, 95% CI 1.02-1.08; P = 0.04) and lymphovascular invasion (OR 11.73, 95% CI 3.96-35.7; P < 0.001) proved to be independent predictors for LNM. CONCLUSION: In our present study, CNN-based image analyses showed promising results as a potential novel low-cost method to extract relevant prognostic information directly from H&E histology to predict the LN status of patients with prostate cancer. Our ubiquitously available technique might contribute to an improved LN status prediction.

Hidden Variables in Deep Learning Digital Pathology and Their Potential to Cause Batch Effects: Prediction Model Study.

BACKGROUND: An increasing number of studies within digital pathology show the potential of artificial intelligence (AI) to diagnose cancer using histological whole slide images, which requires large and diverse data sets. While diversification may result in more generalizable AI-based systems, it can also introduce hidden variables. If neural networks are able to distinguish/learn hidden variables, these variables can introduce batch effects that compromise the accuracy of classification systems. OBJECTIVE: The objective of the study was to analyze the learnability of an exemplary selection of hidden variables (patient age, slide preparation date, slide origin, and scanner type) that are commonly found in whole slide image data sets in digital pathology and could create batch effects. METHODS: We trained four separate convolutional neural networks (CNNs) to learn four variables using a data set of digitized whole slide melanoma images from five different institutes. For robustness, each CNN training and evaluation run was repeated multiple times, and a variable was only considered learnable if the lower bound of the 95% confidence interval of its mean balanced accuracy was above 50.0%. RESULTS: A mean balanced accuracy above 50.0% was achieved for all four tasks, even when considering the lower bound of the 95% confidence interval. Performance between tasks showed wide variation, ranging from 56.1% (slide preparation date) to 100% (slide origin). CONCLUSIONS: Because all of the analyzed hidden variables are learnable, they have the potential to create batch effects in dermatopathology data sets, which negatively affect AI-based classification systems. Practitioners should be aware of these and similar pitfalls when developing and evaluating such systems and address these and potentially other batch effect variables in their data sets through sufficient data set stratification.

Common clonal origin of conventional T cells and induced regulatory T cells in breast cancer patients.

Regulatory CD4+ T cells (Treg) prevent tumor clearance by conventional T cells (Tconv) comprising a major obstacle of cancer immune-surveillance. Hitherto, the mechanisms of Treg repertoire formation in human cancers remain largely unclear. Here, we analyze Treg clonal origin in breast cancer patients using T-Cell Receptor and single-cell transcriptome sequencing. While Treg in peripheral blood and breast tumors are clonally distinct, Tconv clones, including tumor-antigen reactive effectors (Teff), are detected in both compartments. Tumor-infiltrating CD4+ cells accumulate into distinct transcriptome clusters, including early activated Tconv, uncommitted Teff, Th1 Teff, suppressive Treg and pro-tumorigenic Treg. Trajectory analysis suggests early activated Tconv differentiation either into Th1 Teff or into suppressive and pro-tumorigenic Treg. Importantly, Tconv, activated Tconv and Treg share highly-expanded clones contributing up to 65% of intratumoral Treg. Here we show that Treg in human breast cancer may considerably stem from antigen-experienced Tconv converting into secondary induced Treg through intratumoral activation.

Robustness of convolutional neural networks in recognition of pigmented skin lesions.

BACKGROUND: A basic requirement for artificial intelligence (AI)-based image analysis systems, which are to be integrated into clinical practice, is a high robustness. Minor changes in how those images are acquired, for example, during routine skin cancer screening, should not change the diagnosis of such assistance systems. OBJECTIVE: To quantify to what extent minor image perturbations affect the convolutional neural network (CNN)-mediated skin lesion classification and to evaluate three possible solutions for this problem (additional data augmentation, test-time augmentation, anti-aliasing). METHODS: We trained three commonly used CNN architectures to differentiate between dermoscopic melanoma and nevus images. Subsequently, their performance and susceptibility to minor changes ('brittleness') was tested on two distinct test sets with multiple images per lesion. For the first set, image changes, such as rotations or zooms, were generated artificially. The second set contained natural changes that stemmed from multiple photographs taken of the same lesions. RESULTS: All architectures exhibited brittleness on the artificial and natural test set. The three reviewed methods were able to decrease brittleness to varying degrees while still maintaining performance. The observed improvement was greater for the artificial than for the natural test set, where enhancements were minor. CONCLUSIONS: Minor image changes, relatively inconspicuous for humans, can have an effect on the robustness of CNNs differentiating skin lesions. By the methods tested here, this effect can be reduced, but not fully eliminated. Thus, further research to sustain the performance of AI classifiers is needed to facilitate the translation of such systems into the clinic.

Integrating proteomics into precision oncology.

DNA sequencing and RNA sequencing are increasingly applied in precision oncology, where molecular tumor boards evaluate the actionability of genetic events in individual tumors to guide targeted treatment. To work toward an additional level of patient characterization, we assessed the abundance and activity of 27 proteins in 134 patients whose tumors had previously undergone whole-exome and RNA sequencing within the Molecularly Aided Stratification for Tumor Eradication Research (MASTER) program of National Center for Tumor Diseases, Heidelberg. Proteomic and phosphoproteomic targets were selected to reflect the most relevant therapeutic baskets in MASTER. Among six different therapeutic baskets, the proteomic data supported treatment recommendations that were based on DNA and RNA analyses in 10% to 57% and frequently suggested alternative treatment options. In several cases, protein activities explained the patients' clinical course and provided potential explanations for treatment failure. Our study indicates that the integrative analysis of DNA, RNA and protein data may refine therapeutic stratification of individual patients and, thus, holds potential to increase the success rate of precision cancer therapy. Prospective validation studies are needed to advance the integration of proteomic analysis into precision oncology.