Interpretable deep learning survival predictive tool for small cell lung cancer

Dongrui Zhang; Baohua Lu; Bowen Liang; Bo Li; Ziyu Wang; Meng Gu; Wei Jia; Yuanming Pan

doi:10.3389/fonc.2023.1162181

Interpretable deep learning survival predictive tool for small cell lung cancer

Front Oncol. 2023 May 5:13:1162181. doi: 10.3389/fonc.2023.1162181. eCollection 2023.

Authors

Dongrui Zhang¹, Baohua Lu², Bowen Liang³, Bo Li³, Ziyu Wang⁴, Meng Gu⁴, Wei Jia¹, Yuanming Pan⁴

Affiliations

¹ Department of Respiratory and Critical Care Medicine, Tianjin Chest Hospital, Tianjin, China.
² Department of Oncology, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China.
³ Department of Traditional Chinese Medicine, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China.
⁴ Cancer Research Center, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China.

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, radiotherapy, and history of malignancy were chosen as predictive clinical features. The C-index was the main indicator to evaluate model performance.

Results: The predictive model had a 0.7181 C-index (95% confidence intervals, CIs, 0.7174-0.7187) in the train dataset and a 0.7208 C-index (95% CIs, 0.7202-0.7215) in the test dataset. These indicated that it had a reliable predictive value on OS for SCLC, so it was then packaged as a Windows software which is free for doctors, researchers, and patients to use.

Conclusion: The interpretable deep learning survival predictive tool for small cell lung cancer developed by this study had a reliable predictive value on their overall survival. More biomarkers may help improve the prognostic predictive performance of small cell lung cancer.

Keywords: clinical tool; deep learning; neural network; predictive model; small cell lung cancer.

Grants and funding

This work was supported by the Project of Tianjin Chest Hospital (No. 2018XKZ28) and by Tianjin Key Medical Discipline (Specialty) Construction Project to DZ and the Programme of Cultivation Plan of Beijing Municipal Health Administration (No. PX2023059) to YP.