AUTHOR=Shen Qiang , Chen Hongyu 

TITLE=A novel risk classification system based on the eighth edition of TNM frameworks for esophageal adenocarcinoma patients: A deep learning approach

JOURNAL=Frontiers in Oncology

VOLUME=Volume 12 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.887841

DOI=10.3389/fonc.2022.887841

ISSN=2234-943X

ABSTRACT=Objective: To develop and validate a deep learning predictive model with better performance in survival estimation of esophageal adenocarcinoma (EAC).
Method: Cases diagnosed between January 2010 and December 2018 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. A deep learning survival neural network was developed and validated based on 17 variables, including demographic information, clinicopathological characteristics and treatment details. Based on the total risk score derived from this algorithm, a novel staging system was constructed and compared to the 8th edition of tumor, node, and metastasis (TNM) staging system.
Results: Of 7764 EAC patients eligible for the study, 6818 (87.8%) were males and the median (interquartile range, IQR) age was 65 (58-72) years. The deep learning model generated significantly superior predictions to the 8th edition staging system on the test data set (C-index: 0.773 [95%CI, 0.757-0.789] vs 0.683 [95%CI, 0.667-0.699]; P < 0.001). Calibration curves revealed the deep learning model was well calibrated for 1-year and 3-year OS, most points almost directly distributing on the 45° line. DCAs showed that the novel staging system exhibited a more significant positive net benefit than TNM staging system. A user-friendly and precise web-based calculator with a portably executable file was implemented to visualize the deep learning predictive model.
Conclusion: A deep learning predictive model was developed and validated, which possesses more excellent calibration and discrimination abilities in survival prediction of EAC. The novel staging system based on the deep learning algorithm may serve as a useful tool in clinical decision making given its easy-to-use and better clinical applicability.