Development and Validation of a DeepSurv Nomogram to Predict Survival Outcomes and Guide Personalized Adjuvant Chemotherapy in Non-Small Cell Lung Cancer - Yang, Bin; Liu, Chengxing; Wu, Ren; Zhong, Jing; Li, Ang; Ma, Lu; Zhong, Jian; Yin, Saisai; Zhou, Changsheng; Ge, Yingqian; Tao, Xinwei; Zhang, Longjiang; et al. (Frontiers in Oncology, 2022)
Objective: To develop and validate a DeepSurv nomogram based on radiomic features extracted from computed tomography (CT) images and clinicopathological factors to predict overall survival (OS) in patients with non-small cell lung cancer (NSCLC) and guide individualized adjuvant chemotherapy in patients with NSCLC. Patients and methods: This retrospective study involved 976 consecutive patients with NSCLC (training, n=683; validation cohort, n=293). DeepSurv was constructed based on 1227 radiomic features, and the risk score was calculated for each patient as the output. A clinical multivariate Cox regression model was built with clinicopathological parameters to identify 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 low-risk and high-risk groups. Results: The DeepSurv nomogram yielded a significantly better concordance index (training 0.821, validation 0.768) with good fitness (P<.05). The risk score, age, TTF-1, Ki-67, and stage were independent risk factors for NSCLC. The Greenwood-Nam-D’Agostino test showed good calibration performance (P=.39). Both high- and low-risk patients did not gain benefits from adjuvant chemotherapy, and chemotherapy in low-risk groups may lead to a poorer prognosis. Conclusions : A DeepSurv nomogram based on the stratified risk score and independent risk factors has excellent predictive performance for survival outcomes in NSCLC patients. And, it could be used to guide personalized adjuvant chemotherapy in patients with NSCLC.