Share Email Print

Proceedings Paper

Machine learning-powered prediction of recurrence in patients with non-small cell lung cancer using quantitative clinical and radiomic biomarkers
Author(s): Sehwa Moon; Dahim Choi; Ji-Yeon Lee; Myoung Hee Kim; Helen Hong; Bong-Seog Kim; Jang-Hwan Choi
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Lung cancer is a fatal disease, non-small cell lung cancer (NSCLC) being the most prevalent type. One of the main purposes of researching NSCLC is identifying patients at high risk for recurrence after surgical resection so that specific and suitable treatments can be found for them. The classification of cancer by anatomic disease extent, that is, by tumor-size (T stage) and nodal-involvement (N stage), is the most widely accepted determinant of appropriate treatment and prognosis among practicing clinicians. However, TN stage-based risk prediction can be inaccurate, as there is moderate observer variability when reporting the size of the lesion. Here, we propose a lung cancer–recurrence prediction model using principal component analysis (PCA) and machine learning (ML) techniques and considering radiomic features and clinical data, including the TN stage. After being filtered by a statistical model, the principal components, including Tand N-stage data and the handcrafted radiomic features from CT images, were applied to various ML models (i.e., random forests, support vector machines, naive Bayesian classifiers, and both boosting). We conducted this study, not only on recurrence, but also recurrence within two years of surgical resection, since more than 80% of recurrence occurs within this time frame. In both cases, the experimental results showed that combining radiomic features and clinical data improves the prediction of lung-cancer recurrence over that of models that only use TN stage data in terms of the 5-fold cross-validation accuracy mean, the receiver operating characteristic (ROC), the area under the ROC curve (AUC), and Kaplan-Meier curves. Finally, this model has been embedded in a website and is being prepared for the Ministry of Food and Drug Safety (MFDS) medical device registration and approval in South Korea.

Paper Details

Date Published: 16 March 2020
PDF: 8 pages
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113140Y (16 March 2020);
Show Author Affiliations
Sehwa Moon, Ewha Womans Univ. (Korea, Republic of)
Dahim Choi, Ewha Womans Univ. (Korea, Republic of)
Ji-Yeon Lee, Ewha Womans Univ. (Korea, Republic of)
Myoung Hee Kim, Ewha Womans Univ. (Korea, Republic of)
Helen Hong, Seoul Women's Univ. (Korea, Republic of)
Bong-Seog Kim, Veterans Health Service Medical Ctr. (Korea, Republic of)
Jang-Hwan Choi, Ewha Womans Univ. (Korea, Republic of)

Published in SPIE Proceedings Vol. 11314:
Medical Imaging 2020: Computer-Aided Diagnosis
Horst K. Hahn; Maciej A. Mazurowski, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?