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Context.—The diagnosis and clinical management of patients with diffuse gliomas (DGs) have evolved rapidly over the past decade with the emergence of molecular biomarkers that are used to classify, stratify risk, and predict treatment response for optimal clinical care.Objective.—To develop evidence-based recommendations for informing molecular biomarker testing for pediatric and adult patients with DGs and provide guidance for appropriate laboratory test and biomarker selection for optimal diagnosis, risk stratification, and prediction.Design.—The College of American Pathologists convened an expert panel to perform a systematic review of the literature and develop recommendations. A systematic review of literature was conducted to address the overarching question, "What ancillary tests are needed to classify DGs and sufficiently inform the clinical management of patients?" Recommendations were derived from quality of evidence, open comment feedback, and expert panel consensus.Results.—Thirteen recommendations and 3 good practice statements were established to guide pathologists and treating physicians on the most appropriate methods and molecular biomarkers to include in laboratory testing to inform clinical management of patients with DGs.Conclusions.—Evidence-based incorporation of laboratory results from molecular biomarker testing into integrated diagnoses of DGs provides reproducible and clinically meaningful information for patient management.

Context.—The diagnosis and clinical management of patients with diffuse gliomas (DGs) have evolved rapidly over the past decade with the emergence of molecular biomarkers that are used to classify, stratify risk, and predict treatment response for optimal clinical care. Objective.—To develop evidence-based recommendations for informing molecular biomarker testing for pediatric and adult patients with DGs and provide guidance for appropriate laboratory test and biomarker selection for optimal diagnosis, risk stratification, and prediction. Design.—The College of American Pathologists convened an expert panel to perform a systematic review of the literature and develop recommendations. A systematic review of literature was conducted to address the overarching question, ‘‘What ancillary tests are needed to classify DGs and sufficiently inform the clinical management of patients?’’ Recommendations were derived from quality of evidence, open comment feedback, and expert panel consensus. Results.—Thirteen recommendations and 3 good practice statements were established to guide pathologists and treating physicians on the most appropriate methods and molecular biomarkers to include in laboratory testing to inform clinical management of patients with DGs. Conclusions.—Evidence-based incorporation of laboratory results from molecular biomarker testing into integrated diagnoses of DGs provides reproducible and clinically meaningful information for patient management.

Osteosarcoma is the most common primary bone cancer whose standard treatment includes pre-operative chemotherapy followed by resection. Chemotherapy response is used for predicting prognosis and further management of patients. Necrosis is routinely assessed post-chemotherapy from histology slides on resection specimens where necrosis ratio is defined as the ratio of necrotic tumor to overall tumor. Patients with necrosis ratio >=90% are known to have better outcome. Manual microscopic review of necrosis ratio from multiple glass slides is semi-quantitative and can have intra- and inter-observer variability. We propose an objective and reproducible deep learning-based approach to estimate necrosis ratio with outcome prediction from scanned hematoxylin and eosin whole slide images. We collected 103 osteosarcoma cases with 3134 WSIs to train our deep learning model, to validate necrosis ratio assessment, and to evaluate outcome prediction. We trained Deep Multi-Magnification Network to segment multiple tissue subtypes including viable tumor and necrotic tumor in pixel-level and to calculate case-level necrosis ratio from multiple WSIs. We showed necrosis ratio estimated by our segmentation model highly correlates with necrosis ratio from pathology reports manually assessed by experts where mean absolute differences for Grades IV (100%), III (>=90%), and II (>=50% and <90%) necrosis response are 4.4%, 4.5%, and 17.8%, respectively. We successfully stratified patients to predict overall survival with p=10^-6 and progression-free survival with p=0.012. Our reproducible approach without variability enabled us to tune cutoff thresholds, specifically for our model and our data set, to 80% for OS and 60% for PFS. Our study indicates deep learning can support pathologists as an objective tool to analyze osteosarcoma from histology for assessing treatment response and predicting patient outcome.

Osteosarcoma is the most common malignant primary bone tumor. Standard treatment includes pre-operative chemotherapy followed by surgical resection. The response to treatment as measured by ratio of necrotic tumor area to overall tumor area is a known prognostic factor for overall survival. This assessment is currently done manually by pathologists by looking at glass slides under the microscope which may not be reproducible due to its subjective nature. Convolutional neural networks (CNNs) can be used for automated segmentation of viable and necrotic tumor on osteosarcoma whole slide images. One bottleneck for supervised learning is that large amounts of accurate annotations are required for training which is a time-consuming and expensive process. In this paper, we describe Deep Interactive Learning (DIaL) as an efficient labeling approach for training CNNs. After an initial labeling step is done, annotators only need to correct mislabeled regions from previous segmentation predictions to improve the CNN model until the satisfactory predictions are achieved. Our experiments show that our CNN model trained by only 7 hours of annotation using DIaL can successfully estimate ratios of necrosis within expected inter-observer variation rate for non-standardized manual surgical pathology task., Comment: Accepted at MICCAI 2020

Background: The establishment of whole-slide imaging (WSI) as a medical diagnostic device allows that pathologists may evaluate mitotic activity with this new technology. Furthermore, the image digitalization provides an opportunity to develop algorithms for automatic quantifications, ideally leading to improved reproducibility as compared to the naked eye examination by pathologists. In order to implement them effectively, accuracy of mitotic figure detection using WSI should be investigated. In this study, we aimed to measure pathologist performance in detecting mitotic figures (MFs) using multiple platforms (multiple scanners) and compare the results with those obtained using a brightfield microscope. Methods: Four slides of canine oral melanoma were prepared and digitized using 4 WSI scanners. In these slides, 40 regions of interest (ROIs) were demarcated, and five observers identified the MFs using different viewing modes: microscopy and WSI. We evaluated the inter- and intra-observer agreements between modes with Cohen’s Kappa and determined “true” MFs with a consensus panel. We then assessed the accuracy (agreement with truth) using the average of sensitivity and specificity. Results: In the 40 ROIs, 155 candidate MFs were detected by five pathologists; 74 of them were determined to be true MFs. Inter- and intra-observer agreement was mostly “substantial” or greater (Kappa?=?0.594?0.939). Accuracy was between 0.632 and 0.843 across all readers and modes. After averaging over readers for each modality, we found that mitosis detection accuracy for 3 of the 4 WSI scanners was significantly less than that of the microscope (p =?0.002, 0.012, and 0.001). Conclusions: This study is the first to compare WSIs and microscopy in detecting MFs at the level of individual cells. Our results suggest that WSI can be used for mitotic cell detection and offers similar reproducibility to the microscope, with slightly less accuracy., Diagnostic Pathology, 14(1), art.no.65; 2019