Abstract 6329: Using low-resolution, low-cost histopathology images to predict esophageal squamous cell carcinoma via deep learning

Abstract

Like many cancers, esophageal squamous cell carcinoma, has a substantially better survival rate if detected in early stages, making early detection a critical element in successful treatment. On the other hand, deep learning tools that employ high-resolution whole slide images and therefore substantially greater compute power and are not easily accessible in low-resource settings. In this study we train and test deep learning models using low-resolution and low-cost digital histopathology images acquired using a 5-megapixel Leica ICC50 camera mounted on a Leica DM500 binocular microscope, at two different magnification levels, Under IRB approvals, we acquired a total of 64 Hematoxylin and Eosin (H&E) stained tissue slides of biopsies taken from patient presenting at Rehman Medical Institute from 2016 to 2020 that were anonymized. A total of 2370 images were captured at two different magnification levels, i.e. 10x and 40x, and were labelled with four classes by at least one expert pathologist (677 well differentiated, 1066 moderately differentiated, 152 poor differentiated and 475 normal) to develop the ESCC image dataset. Preprocessing was performed to clean the dataset, generate patches and resize the images. Deep learning models were trained on the ESCC image dataset for feature extractions. This was followed by machine learning classifier for the diagnosis of ESCC. Deep learning models that were used in this study are Resnet50, VGG16, VGG19, and Inception_Resnet_V2 with Logistic Regression (LR) used as classifier. An ensemble model which stacks up the Inception and Resnet. This model preformed with the highest accuracy at 96% with Precision, Recall, and f1 score values at 96%, 96%, and 96%, respectively. This study reports 1) a local ESCC image repository for research purposes, 2) an ensemble ML/DL model that predicts ESCC with 96% accuracy and 3) also a computer-aided diagnosis (CAD) system for ESCC with a user-interface which will assist histopathologists in diagnosing ESCC with higher speed, efficiency and accuracy. Citation Format: Mansoor Alam, Ibrar Amin, Ayesha Sajjad, Hina Zamir, Saad Zaheer, Maria T. Khattak, Iqbal Muhammad, Faisal F. Khan. Using low-resolution, low-cost histopathology images to predict esophageal squamous cell carcinoma via deep learning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6329.