No nodule left behind: evaluating lung nodule malignancy classification with different stratification schemes

Thomas Zeng; Elias Furst; Yiyang Wang; Roselyne Tchoua; Jacob Furst; Daniela Raicu

doi:10.1117/12.2653633

7 April 2023 No nodule left behind: evaluating lung nodule malignancy classification with different stratification schemes

Thomas Zeng, Elias Furst, Yiyang Wang, Roselyne Tchoua, Jacob Furst, Daniela Raicu

Author Affiliations +

Proceedings Volume 12465, Medical Imaging 2023: Computer-Aided Diagnosis; 124651O (2023) https://doi.org/10.1117/12.2653633
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

Machine learning models have been widely used in lung cancer computer-aided diagnosis (CAD) studies. However, the heterogeneity in the visual appearance of lung nodules as well as lack of consideration of hidden subgroups in the data are significant obstacles to generating accurate CAD outcomes across all nodule instances. Previous lung cancer CAD models aim to achieve Empirical Risk Minimization (ERM), which leads to a high overall accuracy but often fails at predicting certain subgroups caused by the lung cancer heterogeneity. In this study, we aim to discover hidden lung nodule subgroups and enhance the malignancy classification performance of the worst-performance subgroup when compared to traditional ERM methods. We experiment with three different stratification methods for lung nodule subgroup discovery: spiculation-based, clustering-based, and malignancybased. A high overlap between subgroup labels generated from the clustering-based approach and labels obtained from radiologists’ semantic annotations indicates our discovered subgroups are semantically meaningful. We successfully improved the worst malignancy classification performance lung nodule subgroup through utilizing Group Distributionally Robust Optimization (gDRO) when compared to ERM as a baseline. Our study creates a framework for augmenting lung nodule malignancy classification under domain shift situations caused by the disease heterogeneity and underscores the necessity of addressing hidden stratification for future CAD schemes.

Conference Presentation

Citation Download Citation

Thomas Zeng, Elias Furst, Yiyang Wang, Roselyne Tchoua, Jacob Furst, and Daniela Raicu "No nodule left behind: evaluating lung nodule malignancy classification with different stratification schemes", Proc. SPIE 12465, Medical Imaging 2023: Computer-Aided Diagnosis, 124651O (7 April 2023); https://doi.org/10.1117/12.2653633

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available