Identifying Prognostic Markers From Clinical, Radiomics, and Deep Learning Imaging Features for Gastric Cancer Survival Prediction

Degan Hao; Qiong Li; Qiu-Xia Feng; Liang Qi; Xi-Sheng Liu; Dooman Arefan; Yu-Dong Zhang; Shandong Wu

doi:10.3389/fonc.2021.725889

Identifying Prognostic Markers From Clinical, Radiomics, and Deep Learning Imaging Features for Gastric Cancer Survival Prediction

Front Oncol. 2022 Feb 2:11:725889. doi: 10.3389/fonc.2021.725889. eCollection 2021.

Authors

Degan Hao¹, Qiong Li², Qiu-Xia Feng², Liang Qi², Xi-Sheng Liu², Dooman Arefan³, Yu-Dong Zhang², Shandong Wu^{1

3

4

5}

Affiliations

¹ Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, United States.
² Department of Radiology, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China.
³ Department of Radiology, University of Pittsburgh, Pittsburgh, PA, United States.
⁴ Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States.
⁵ Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States.

Abstract

Background: Gastric cancer is one of the leading causes of cancer death in the world. Improving gastric cancer survival prediction can enhance patient prognostication and treatment planning.

Methods: In this study, we performed gastric cancer survival prediction using machine learning and multi-modal data of 1061 patients, including 743 for model learning and 318 independent patients for evaluation. A Cox proportional-hazard model was trained to integrate clinical variables and CT imaging features (extracted by radiomics and deep learning) for overall and progression-free survival prediction. We further analyzed the prediction effects of clinical, radiomics, and deep learning features. Concordance index (c-index) was used as the model performance metric, and the predictive effects of multi-modal features were measured by hazard ratios (HRs) at pre- and post-operative settings.

Results: Among 318 patients in the independent testing group, the hazard predicted by Cox from multi-modal features is associated with their survival. The highest c-index was 0.783 (95% CI, 0.782-0.783) and 0.770 (95% CI, 0.769-0.771) for overall and progression-free survival prediction, respectively. The post-operative variables are significantly (p<0.001) more predictive than the pre-operative variables. Pathological tumor stage (HR=1.336 [overall survival]/1.768 [progression-free survival], p<0.005), pathological lymph node stage (HR=1.665/1.433, p<0.005), carcinoembryonic antigen (CEA) (HR=1.632/1.522, p=0.02), chemotherapy treatment (HR=0.254/0.287, p<0.005), radiomics signature [HR=1.540/1.310, p<0.005], and deep learning signature [HR=1.950/1.420, p<0.005]) are significant survival predictors.

Conclusion: Our study showed that CT radiomics and deep learning imaging features are significant pre-operative predictors, providing additional prognostic information to the pathological staging markers. Lower CEA levels and chemotherapy treatments also increase survival chances. These findings can enhance gastric cancer patient prognostication and inform treatment planning.

Keywords: deep learning - CNN; gastric cancer; multi-modal data analysis; radiomics; survival analysis (source: MeSH NLM).