Affiliation:
1. Cancer Research Center, Beijing Chest Hospital Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute Beijing China
2. Department of Thoracic Surgery, Sichuan Provincial People's Hospital University of Electronic Science and Technology of China Chengdu Sichuan China
3. Department of Medical Oncology, Beijing Tuberculosis and Thoracic Tumor Research Institute/Beijing Chest Hospital Capital Medical University Beijing China
Abstract
AbstractBackgroundImmunotherapy combined with chemotherapy has been approved as first‐line therapy for small cell lung cancer (SCLC) due to the survival benefit in randomized controlled trials. However, predicting its efficacy remains a challenge in the absence of currently available biomarkers.MethodsA total of 140 individuals with SCLC who received immunotherapy were evaluated retrospectively. These patients were split into two distinct cohorts, the discovery cohort (80% of the total, n = 112) and the validation cohort (20% of the total, n = 28). The objective response rate (ORR), disease control rate (DCR), and responder (progression‐free survival [PFS] > 6 months) were all predicted using neural networks.ResultsWe developed predictive models for three clinical outcomes. ORR scored 0.8964 area under the receiver operating characteristic curve (AUC) in the discovery cohort and 0.8421 AUC in the validation cohort. DCR model had AUC of 0.8618 in the discovery cohort and AUC of 0.7396 in the validation cohort. The responder model had AUC of 0.8116 in the discovery cohort and AUC of 0.7041 in the validation cohort. The models were then compressed into a doctor‐friendly tool.ConclusionThese neural network‐based models, which are based on routine clinical characteristics, accurately predict the efficacy of immunotherapy in patients with SCLC, particularly in terms of ORR.