Deep Learning of Histopathology Predicts Outcomes After Surgery for Pancreatic Cancer.
Predicting recurrence of pancreatic cancer after surgery could inform clinical decision making, including adjuvant therapies and follow-up. This study aimed to develop and validate a deep learning model using digitized whole-slide images (WSI) of histopathology.
Publicly available WSI of pancreatic ductal adenocarcinoma resections from three cohorts were used for training. The model consisted of a pan-cancer foundation model to generate embeddings, mean-pooling across tissue patches, and then a fully connected neural network. Model predictions were compared with human-labeled histopathologic features and genomic alterations. The model was externally validated in a meta-analysis of a single-center cohort from Princess Margaret Cancer Centre, a multicenter cohort from France, and the PRODIGE 24 trial of adjuvant chemotherapy.
The deep learning model was trained on 12,594 tissue patches from 257 patients. High-risk classifications were associated with squamous morphology, reactive stroma, tumor cellularity, and necrosis, whereas low-risk classifications were associated with tubulopapillary and conventional morphologies, as well as deserted stroma. High-risk cancers were enriched for basal-like gene expression profiles and distinct oncogenic pathways. In a meta-analysis of the external cohorts, the hazard ratio (HR) for death comparing high-versus low-risk cancers was 1.49 (95% CI, 1.25 to 1.79, P < .001), whereas the HR for recurrence or death was 1.41 (95% CI, 1.19 to 1.68, P < .001). The classifications remained prognostic among moderately differentiated cancers.
An open-source deep learning model using WSI from pancreatic cancer resections generated risk classifications that correlated with histopathologic and genomic features. Classifications were externally validated in a meta-analysis of three cohorts. This model could be applied to WSI to provide individualized prognostic information for patients.
Publicly available WSI of pancreatic ductal adenocarcinoma resections from three cohorts were used for training. The model consisted of a pan-cancer foundation model to generate embeddings, mean-pooling across tissue patches, and then a fully connected neural network. Model predictions were compared with human-labeled histopathologic features and genomic alterations. The model was externally validated in a meta-analysis of a single-center cohort from Princess Margaret Cancer Centre, a multicenter cohort from France, and the PRODIGE 24 trial of adjuvant chemotherapy.
The deep learning model was trained on 12,594 tissue patches from 257 patients. High-risk classifications were associated with squamous morphology, reactive stroma, tumor cellularity, and necrosis, whereas low-risk classifications were associated with tubulopapillary and conventional morphologies, as well as deserted stroma. High-risk cancers were enriched for basal-like gene expression profiles and distinct oncogenic pathways. In a meta-analysis of the external cohorts, the hazard ratio (HR) for death comparing high-versus low-risk cancers was 1.49 (95% CI, 1.25 to 1.79, P < .001), whereas the HR for recurrence or death was 1.41 (95% CI, 1.19 to 1.68, P < .001). The classifications remained prognostic among moderately differentiated cancers.
An open-source deep learning model using WSI from pancreatic cancer resections generated risk classifications that correlated with histopathologic and genomic features. Classifications were externally validated in a meta-analysis of three cohorts. This model could be applied to WSI to provide individualized prognostic information for patients.
Authors
Wong Wong, Bourega Bourega, Nicolle Nicolle, Elqaderi Elqaderi, Nowak Nowak, Light Light, Wang Wang, Quan Quan, Ji Ji, Abbas-Aghababazadeh Abbas-Aghababazadeh, Henault Henault, He He, Chen Chen, Hutchinson Hutchinson, Dodd Dodd, Wilson Wilson, Jang Jang, Biankin Biankin, Chang Chang, O'Callaghan O'Callaghan, Biagi Biagi, , Conroy Conroy, Hammel Hammel, , Notta Notta, Knox Knox, Gallinger Gallinger, Krishnan Krishnan, Cros Cros, Grant Grant
View on Pubmed