Development and Validation of a DeepSurv Nomogram to Predict Survival Outcomes and Guide Personalized Adjuvant Chemotherapy in Non-Small Cell Lung Cancer

• Published in: Frontiers in oncology 2022-06, Vol.12, p.895014-895014
• Main Authors: Yang, Bin, Liu, Chengxing, Wu, Ren, Zhong, Jing, Li, Ang, Ma, Lu, Zhong, Jian, Yin, Saisai, Zhou, Changsheng, Ge, Yingqian, Tao, Xinwei, Zhang, Longjiang, Lu, Guangming
• Format: Article
• Language: English
• Subjects: chemotherapy; DeepSurv; non-small cell lung cancer; Oncology; radiomics; survival outcome
• ISSN: 2234-943X; 2234-943X
• DOI: 10.3389/fonc.2022.895014

ObjectiveTo develop and validate a DeepSurv nomogram based on radiomic features extracted from computed tomography images and clinicopathological factors, to predict the overall survival and guide individualized adjuvant chemotherapy in patients with non-small cell lung cancer (NSCLC). Patients and MethodsThis retrospective study involved 976 consecutive patients with NSCLC (training cohort, n=683; validation cohort, n=293). DeepSurv was constructed based on 1,227 radiomic features, and the risk score was calculated for each patient as the output. A clinical multivariate Cox regression model was built with clinicopathological factors to determine the independent risk factors. Finally, a DeepSurv nomogram was constructed by integrating the risk score and independent clinicopathological factors. The discrimination capability, calibration, and clinical usefulness of the nomogram performance were assessed using concordance index evaluation, the Greenwood-Nam-D'Agostino test, and decision curve analysis, respectively. The treatment strategy was analyzed using a Kaplan-Meier curve and log-rank test for the high- and low-risk groups. ResultsThe DeepSurv nomogram yielded a significantly better concordance index (training cohort, 0.821; validation cohort 0.768) with goodness-of-fit (P