Abstract 1393: Automated adeno/squamous-cell NSCLC classification from diagnostic slide images: A deep-learning framework utilizing cell-density maps

Abstract

Abstract Background: The most common form of lung cancer, non-small cell lung cancer (NSCLC), is further categorized into two major histopathological subtypes: ~40% Adenocarcinoma (LUAD), and ~30% squamous cell carcinoma (LUSC). Classifying patients accurately is important for prognosis and therapy decisions, but requires costly pathologist review. Here we present an automated algorithm to differentiate LUAD and LUSC diagnostic whole slide images (WSIs). Methods: 488 subtyped NSCLC high-resolution diagnostic WSIs were obtained from TCGA sources. Adjacent normal regions were identified and excluded from analysis. Cancer cell-density maps were created based on cell counts within discrete patches. These maps were then binned into ten ranges of cell counts (20-30 cells per patch, 30-40, etc. up to >110 cells per patch). 2D color patches were transformed into 1D descriptive vectors using the inception v3 deep learning framework. Samples were randomly split into 70% training and 30% testing sets. Ten LUAD/LUSC linear SVM classifiers (one for each cell-density bin) were trained on such transformed data. Subtype prediction in unseen testing samples was achieved by averaging subtype predictions from the 10 subsequent models. Results: 338 TCGA diagnostic WSIs (164 LUAD and 174 LUSC) were used to train, and 150 (71 LUAD and 79 LUSC) were used to test. The proposed system achieved an AUC of 0.9068 in test samples, corresponding to a classification accuracy of 83.33%. The (heretofore excluded) adjacent normal regions were classified correctly almost as accurately as tumor regions (74.7%). Conclusions: This fully-automated histopathology subtyping method generates maps of regions-of-interest within WSIs, providing novel spatial information on tumoral organization. For example, our results on test data show tumor patches of size 100 square microns with 60 to 100 cells distinguish LUAD from LUSC better than other cell-density ranges. Moreover, adjacent normal tissue may provide additional insights into tumorigenesis mechanisms. Citation Format: Mustafa I. Jaber, Liudmila Beziaeva, Christopher W. Szeto, John Elshimali, Shahrooz Rabizadeh, Bing Song. Automated adeno/squamous-cell NSCLC classification from diagnostic slide images: A deep-learning framework utilizing cell-density maps [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1393.