Quantitative and Morphology-Based Deep Convolutional Neural Network Approaches for Osteosarcoma Survival Prediction in the Neoadjuvant and Metastatic Setting
Coudray, Nicolas; Occidental, Michael A; Mantilla, Jose G; Claudio Quiros, Adalberto; Yuan, Ke; Balko, Jan; Tsirigos, Aristotelis; Jour, George
PURPOSE/OBJECTIVE:Necrosis quantification in the neoadjuvant setting using pathology slide review is the most important validated prognostic marker in conventional osteosarcoma. Herein, we explored three deep learning strategies on histology samples to predict outcome for OSA in the neoadjuvant setting. EXPERIMENTAL DESIGN/METHODS:Our study relies on a training cohort from New York University (New York, NY) and an external cohort from Charles university (Prague, Czechia). We trained and validated the performance of a supervised approach that integrates neural network predictions of necrosis/tumor content, and compared predicted overall survival (OS) using Kaplan-Meier curves. Furthermore, we explored morphology-based supervised and self-supervised approaches to determine whether intrinsic histomorphological features could serve as a potential marker for OS in the setting of neoadjuvant. RESULTS:Excellent correlation between the trained network and the pathologists was obtained for the quantification of necrosis content (R2=0.899, r=0.949, p < 0.0001). OS prediction cutoffs were consistent between pathologists and the neural network (22% and 30% of necrosis, respectively). Morphology-based supervised approach predicted OS with p-value=0.0028, HR=2.43 [1.10-5.38]. The self-supervised approach corroborated the findings with clusters enriched in necrosis, fibroblastic stroma, and osteoblastic morphology associating with better OS (lg2HR; -2.366; -1.164; -1.175; 95% CI=[-2.996; -0.514]). Viable/partially viable tumor and fat necrosis were associated with worse OS (lg2HR;1.287;0.822;0.828; 95% CI=[0.38-1.974]). CONCLUSIONS:Neural networks can be used to automatically estimate the necrosis to tumor ratio, a quantitative metric predictive of survival. Furthermore, we identified alternate histomorphological biomarkers specific to the necrotic and tumor regions themselves which can be used as predictors.
PMID: 39561274
ISSN: 1557-3265
CID: 5758442
Multiple levels of Gomori methenamine silver (GMS) stains do not improve diagnostic yield in esophageal biopsies
Occidental, Michael; Dunn, Andrew; Huber, Aaron R
Gomori methenamine silver (GMS) stains are commonly utilized to exclude fungal esophagitis in esophageal biopsies. Our laboratory protocol for special stains, including GMS, stipulates that two levels be performed for each stain. We retrospectively reviewed 127 esophageal biopsies which had GMS stains performed; there were 62 GMS-positive and 65 GMS-negative cases. Sixty-one of the positive cases (98%) had fungal organisms present on all levels. Only one case showed fungal elements on the first, but not the second, level. Given national laboratory staff shortages, performing levels for special stains may be unnecessary, inefficient, and time consuming in the surgical pathology laboratory.
PMID: 39663991
ISSN: 2046-0236
CID: 5762822