Deep multi-task prediction of lung cancer and cancer-free progression from censored heterogenous clinical imaging

Riqiang Gao; Lingfeng Li; Yucheng Tang; Sanja L. Antic; Alexis B. Paulson; Yuankai Huo; Kim L. Sandler; Pierre P. Massion; Bennett A. Landman

doi:10.1117/12.2548464

10 March 2020 Deep multi-task prediction of lung cancer and cancer-free progression from censored heterogenous clinical imaging

Riqiang Gao, Lingfeng Li, Yucheng Tang, Sanja L. Antic, Alexis B. Paulson, Yuankai Huo, Kim L. Sandler, Pierre P. Massion, Bennett A. Landman

Author Affiliations +

Proceedings Volume 11313, Medical Imaging 2020: Image Processing; 113130D (2020) https://doi.org/10.1117/12.2548464
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Annual low dose computed tomography (CT) lung screening is currently advised for individuals at high risk of lung cancer (e.g., heavy smokers between 55 and 80 years old). The recommended screening practice significantly reduces all-cause mortality, but the vast majority of screening results are negative for cancer. If patients at very low risk could be identified based on individualized, image-based biomarkers, the health care resources could be more efficiently allocated to higher risk patients and reduce overall exposure to ionizing radiation. In this work, we propose a multi-task (diagnosis and prognosis) deep convolutional neural network to improve the diagnostic accuracy over a baseline model while simultaneously estimating a personalized cancer-free progression time (CFPT). A novel Censored Regression Loss (CRL) is proposed to perform weakly supervised regression so that even single negative screening scans can provide small incremental value. Herein, we study 2287 scans from 1433 de-identified patients from the Vanderbilt Lung Screening Program (VLSP) and the Consortium for Molecular and Cellular Characterization of Screen-Detected Lesions (MCL) cohorts. Using five-fold cross-validation, we train a 3D attention-based network under two scenarios: (1) single-task learning with only classification, and (2) multi-task learning with both classification and regression. The single-task learning leads to a higher AUC compared with the Kaggle challenge winner pre-trained model (0.878 v. 0.856), and multitask learning significantly improves the single-task one (AUC 0.895, p<0.01, McNemar test). In summary, the image based predicted CFPT can be used in follow-up year lung cancer prediction and data assessment.

Conference Presentation

Citation Download Citation

Riqiang Gao, Lingfeng Li, Yucheng Tang, Sanja L. Antic, Alexis B. Paulson, Yuankai Huo, Kim L. Sandler, Pierre P. Massion, and Bennett A. Landman "Deep multi-task prediction of lung cancer and cancer-free progression from censored heterogenous clinical imaging", Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113130D (10 March 2020); https://doi.org/10.1117/12.2548464

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