Interpretable deep learning survival predictive tool for small cell lung cancer.

ABSTRACT:

Background

Small cell lung cancer (SCLC) is an aggressive and almost universally lethal neoplasm. There is no accurate predictive method for its prognosis. Artificial intelligence deep learning may bring new hope.

Methods

By searching the Surveillance, Epidemiology, and End Results database (SEER), 21,093 patients' clinical data were eventually included. Data were then divided into two groups (train dataset/test dataset). The train dataset (diagnosed in 2010-2014, N = 17,296) was utilized to conduct a deep learning survival model, validated by itself and the test dataset (diagnosed in 2015, N = 3,797) in parallel. According to clinical experience, age, sex, tumor site, T, N, M stage (7th American Joint Committee on Cancer TNM stage), tumor size, surgery, chemotherapy, radiot

SUBMITTER: Zhang D 

PROVIDER: S-EPMC10196231 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature