Construction and validation of deep learning model for cachexia in extensive-stage small cell lung cancer patients...

Cachexia is observed in around 60% of patients with extensive-stage small cell lung cancer (ES-SCLC) and may play an important role in the development of resistance to immunotherapy. This study aims to evaluate the influence of cachexia on the effectiveness of immunotherapy, develop and assess a deep learning (DL)-based prediction model for cachexia, as well as its prognostic value.

The analysis encompassed ES-SCLC patients who received the combination of first-line immunotherapy and chemotherapy from Shandong Cancer Hospital and Institute, Qilu Hospital, and Jining First People's Hospital. Survival analysis was conducted to examine the correlation between cachexia and the efficacy of immunotherapy. Medical records and computed tomography (CT) images of the third lumbar vertebra (L3) level were collected to construct the clinical model, radiomics, and DL models. The receiver operating characteristic (ROC) curve analysis was conducted to assess and analyze the efficacy of various models in detecting and evaluating the risk of cachexia.

A total of 231 ES-SCLC patients were enrolled in the study. Cachexia was related to inferior progression-free survival (PFS) and overall survival (OS). In internal and external validation cohorts, the area under the curve (AUC) of the DL model were 0.73 and 0.71. Conversely, the radiomics model in external validation cohort recorded an AUC of 0.67, highlighting the superior performance of the DL model and its demonstrated capability for effective generalization in external validation. All patients were categorized into two groups, namely high risk and low risk using the DL model. It was shown that patients with low-risk cachexia were associated with significantly prolonged PFS and OS.

The DL model not only had better performance in predicting cachexia but also correlated with survival outcomes of ES-SCLC patients who receiving initial immunotherapy.