Fang Nie,* Xiufeng Pei,* Jiale Du, Wanting Shi, Jianying Wang, Lu Feng, Yonggang Liu

Department of Thoracic Oncology, Baotou Cancer Hospital, Baotou, Inner Mongolia, 014000, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Yonggang Liu, Email zlnkldf8000@sina.com

Objective: This study aimed to develop a clinical early warning prediction model to evaluate the prognosis and response to chemoimmunotherapy in patients with extensive-stage small cell lung cancer (ES-SCLC), thereby guiding clinical decision-making.
Methods: A retrospective analysis was conducted on the clinical data and radiomics parameters of 309 patients with ES-SCLC hospitalized at Baotou Cancer Hospital from February 2020 to September 2024. Patients were divided into reactive and non-reactive groups based on their response to chemoimmunotherapy.Machine learning algorithms (including random forests, decision trees, artificial neural networks, and generalized linear regression) were used to predict the combined treatment response. The model’s predictive ability was evaluated using the receiver operating characteristic (ROC) curve and clinical decision curve analysis(DCA). The prognostic evaluation of patients receiving combination therapy was based on the COX regression model, with predictive performance assessed through nomogram visualization and calibration curves.
Results: Out of 309 patients with ES-SCLC, 248 (80.26%) responded to combination therapy. Logistic regression and Least absolute shrinkage and selection operator (LASSO) regression analyses identified Energy, sum of squares(SOS), mean sum(MES), sum variance(SUV), sum entropy(SUE), difference variance(DIV), and pathomics score as independent risk factors for treatment response. The area under the ROC curve for predicting treatment response using machine learning were 0.764 (95% confidence interval [CI]: 0.707~0.821) and 0.901 (95% CI: 0.846~0.956) in the training and validation sets. The C-index of the radiomics and pathomics prognostic nomogram model based on the COX prognostic model was 0.766 and 0.812 in those sets, respectively.
Conclusion: We developed prediction model based on multi-omics demonstrated satisfactory performance in predicting chemoimmunotherapy response in patients with ES-SCLC. The random forest prediction model, in particular, provides accurate response and prognostic risk assessments, thereby assisting clinical decision-making.

Keywords: extensive-stage small cell lung cancer, immune combination therapy, therapeutic response, radiomics, machine learning, prediction model