The predictive value of modified-DeepSurv in overall survivals of patients with lung cancer

Jie Lei; Xin Xu; Junrui Xu; Jia Liu; Yi Wang; Chao Wu; Renquan Zhang; Zhemin Zhang; Tao Jiang

iScience (Nov 2023)

The predictive value of modified-DeepSurv in overall survivals of patients with lung cancer

Jie Lei,
Xin Xu,
Junrui Xu,
Jia Liu,
Yi Wang,
Chao Wu,
Renquan Zhang,
Zhemin Zhang,
Tao Jiang

Affiliations

Jie Lei: Department of Thoracic Surgery, The Second Affiliated Hospital, Air Force Medical University, Xi’an 710038, China
Xin Xu: Department of Respiratory Medicine, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China
Junrui Xu: Department of Thoracic Surgery, First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
Jia Liu: Operations Management Department, People’s Hospital of Xinjiang Uygur Autonomous Region, No. 91 Tianchi Road, Tianshan District, Urumqi 830001, China
Yi Wang: Department of Epidemiology and Biostatistics, School of Public Health and Management, Wenzhou Medical University, Wenzhou 325035, China
Chao Wu: Department of Respiratory and Critical Care Medicine, People’s Hospital of Xinjiang Uygur Autonomous Region, No. 91 Tianchi Road, Tianshan District, Urumqi 830001, China; Xinjiang Clinical Research Center for Interstitial Lung Diseases, People’s Hospital of Xinjiang Uygur Autonomous Region, No. 91 Tianchi Road, Tianshan District, Urumqi 830001, China
Renquan Zhang: Department of Thoracic Surgery, First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Corresponding author
Zhemin Zhang: Department of Respiratory Medicine, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China; Corresponding author
Tao Jiang: Department of Thoracic Surgery, The Second Affiliated Hospital, Air Force Medical University, Xi’an 710038, China; Corresponding author

Journal volume & issue: Vol. 26, no. 11
p. 108200

Abstract

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Summary: The traditional prognostic model may induce the possibility of incorrect assessment of mortality risk under the assumption of linearity. It is urgent to develop a non-linearity precise prognostic model for achieving personalized medicine in lung cancer. In our study, we develop and validate a prognostic model “Modified-DeepSurv” for patients with lung carcinoma based on deep learning and evaluate its value for prognosis, while Cox proportional hazard regression was used to develop another model “CPH.” The C-index of the Modified-DeepSurv and CPH was 0.956 (95% confidence interval [CI]: 0.946–0.974) and 0.836 (95% CI: 0.774–0.896), respectively, in the training cohort, while the C-index of the Modified-DeepSurv and CPH was 0.932 (95%CI: 0.908–0.964) and 0.777 (95%CI: 0.633–0.919), respectively, in the test dataset. The Modified-DeepSurv model visualization was realized by a user-friendly graphic interface. Modified-DeepSurv can effectively predict the survival of lung cancer patients and is superior to the conventional CPH model.

Published in iScience

ISSN: 2589-0042 (Online)
Publisher: Elsevier
Country of publisher: United States
LCC subjects: Science
Website: http://www.cell.com/iscience/home

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