Identifying an optimal machine learning generated image marker to predict survival of gastric cancer patients

Huong Pham; Meredith A. Jones; Tiancheng Gai; Warid Islam; Gopichandh Danala; Javier Jo; Bin Zheng

doi:10.1117/12.2611788

4 April 2022 Identifying an optimal machine learning generated image marker to predict survival of gastric cancer patients

Huong Pham, Meredith A. Jones, Tiancheng Gai, Warid Islam, Gopichandh Danala, Javier Jo, Bin Zheng

Author Affiliations +

Proceedings Volume 12033, Medical Imaging 2022: Computer-Aided Diagnosis; 120331K (2022) https://doi.org/10.1117/12.2611788
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

Computer-aided detection and/or diagnosis (CAD) schemes typically include machine learning classifiers trained using handcrafted features. The objective of this study is to investigate the feasibility of identifying and applying a new quantitative imaging marker to predict survival of gastric cancer patients. A retrospective dataset including CT images of 403 patients is assembled. Among them, 162 patients have more than 5-year survival. A CAD scheme is applied to segment gastric tumors depicted in multiple CT image slices. After gray-level normalization of each segmented tumor region to reduce image value fluctuation, we used a special feature selection library of a publicly available Pyradiomics software to compute 103 features. To identify an optimal approach to predict patient survival, we investigate two logistic regression model (LRM) generated imaging markers. The first one fuses image features computed from one CT slice and the second one fuses the weighted average image features computed from multiple CT slices. Two LRMs are trained and tested using a leave-one-case-out cross-validation method. Using the LRM-generated prediction scores, receiving operating characteristics (ROC) curves are computed and the area under ROC curve (AUC) is used as index to evaluate performance in predicting patients’ survival. Study results show that the case prediction-based AUC values are 0.70 and 0.72 for two LRM-generated image markers fused with image features computed from a single CT slide and multiple CT slices, respectively. This study demonstrates that (1) radiomics features computed from CT images carry valuable discriminatory information to predict survival of gastric cancer patients and (2) fusion of quasi-3D image features yields higher prediction accuracy than using simple 2D image features.

Citation Download Citation

Huong Pham, Meredith A. Jones, Tiancheng Gai, Warid Islam, Gopichandh Danala, Javier Jo, and Bin Zheng "Identifying an optimal machine learning generated image marker to predict survival of gastric cancer patients", Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 120331K (4 April 2022); https://doi.org/10.1117/12.2611788

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