Deep learning-based identification of esophageal cancer subtypes through analysis of high-resolution histopathology images

Syed Wajid Aalam; Abdul Basit Ahanger; Tariq A Masoodi; Ajaz A Bhat; Ammira S Al-Shabeeb Akil; Meraj Alam Khan; Assif Assad; Muzafar A Macha; Muzafar Rasool Bhat

doi:10.3389/fmolb.2024.1346242

Deep learning-based identification of esophageal cancer subtypes through analysis of high-resolution histopathology images

Front Mol Biosci. 2024 Mar 19:11:1346242. doi: 10.3389/fmolb.2024.1346242. eCollection 2024.

Authors

Syed Wajid Aalam¹, Abdul Basit Ahanger¹, Tariq A Masoodi², Ajaz A Bhat³, Ammira S Al-Shabeeb Akil³, Meraj Alam Khan⁴, Assif Assad⁵, Muzafar A Macha⁶, Muzafar Rasool Bhat¹

Affiliations

¹ Department of Computer Science, Islamic University of Science and Technology, Awantipora, India.
² Human Immunology Department, Research Branch, Sidra Medicine, Doha, Qatar.
³ Department of Human Genetics-Precision Medicine in Diabetes, Obesity and Cancer Program, Sidra Medicine, Doha, Qatar.
⁴ DigiBiomics Inc, Mississauga, ON, Canada.
⁵ Department of Computer Science and Engineering, Islamic University of Science and Technology, Awantipora, India.
⁶ Watson-Crick Centre for Molecular Medicine, Islamic University of Science and Technology, Awantipora, India.

Abstract

Esophageal cancer (EC) remains a significant health challenge globally, with increasing incidence and high mortality rates. Despite advances in treatment, there remains a need for improved diagnostic methods and understanding of disease progression. This study addresses the significant challenges in the automatic classification of EC, particularly in distinguishing its primary subtypes: adenocarcinoma and squamous cell carcinoma, using histopathology images. Traditional histopathological diagnosis, while being the gold standard, is subject to subjectivity and human error and imposes a substantial burden on pathologists. This study proposes a binary class classification system for detecting EC subtypes in response to these challenges. The system leverages deep learning techniques and tissue-level labels for enhanced accuracy. We utilized 59 high-resolution histopathological images from The Cancer Genome Atlas (TCGA) Esophageal Carcinoma dataset (TCGA-ESCA). These images were preprocessed, segmented into patches, and analyzed using a pre-trained ResNet101 model for feature extraction. For classification, we employed five machine learning classifiers: Support Vector Classifier (SVC), Logistic Regression (LR), Decision Tree (DT), AdaBoost (AD), Random Forest (RF), and a Feed-Forward Neural Network (FFNN). The classifiers were evaluated based on their prediction accuracy on the test dataset, yielding results of 0.88 (SVC and LR), 0.64 (DT and AD), 0.82 (RF), and 0.94 (FFNN). Notably, the FFNN classifier achieved the highest Area Under the Curve (AUC) score of 0.92, indicating its superior performance, followed closely by SVC and LR, with a score of 0.87. This suggested approach holds promising potential as a decision-support tool for pathologists, particularly in regions with limited resources and expertise. The timely and precise detection of EC subtypes through this system can substantially enhance the likelihood of successful treatment, ultimately leading to reduced mortality rates in patients with this aggressive cancer.

Keywords: deep learning; histopathology; image processing; machine learning; normalization; patching; transfer learning; whole slide image.

Grants and funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This study was funded, in part, by a Research Grant (Grant number: ID No. 2022-16465) from the Indian Council of Medical Research (ICMR) Govt. of India, New Delhi and a Core Research Grant (CRG/2021/003805) from Science and Engineering Research Board (SERB), Govt. of India, New Delhi to MM. Promotion of University Research and Scientific Excellence (PURSE) (SR/PURSE/2022/121) grant from the Department of Science and Technology, Govt. of India, New Delhi to the Islamic University of Science and Technology (IUST), Awantipora.