Abstract
PURPOSE: Patients with advanced non-small cell lung cancer (NSCLC) have varying responses to immunotherapy, but there are no reliable, accepted biomarkers to accurately predict its therapeutic efficacy. The present study aimed to construct individualized models through automatic machine learning (autoML) to predict the efficacy of immunotherapy in patients with inoperable advanced NSCLC. METHODS: A total of 63 eligible participants were included and randomized into training and validation groups. Radiomics features were extracted from the volumes of interest of the tumor circled in the preprocessed computed tomography (CT) images. Golden feature, clinical, radiomics, and fusion models were generated using a combination of various algorithms through autoML. The models were evaluated using a multi-class receiver operating characteristic curve. RESULTS: In total, 1,219 radiomics features were extracted from regions of interest. The ensemble algorithm demonstrated superior performance in model construction. In the training cohort, the fusion model exhibited the highest accuracy at 0.84, with an area under the curve (AUC) of 0.89-0.98. In the validation cohort, the radiomics model had the highest accuracy at 0.89, with an AUC of 0.98-1.00; its prediction performance in the partial response subgroup outperformed that in both the clinical and radiomics models. Patients with low rad scores achieved improved progression-free survival (PFS); (median PFS 16.2 vs. 13.4, P = 0.009). CONCLUSION: autoML accurately and robustly predicted the short-term outcomes of patients with inoperable NSCLC treated with immune checkpoint inhibitor immunotherapy by constructing CT-based radiomics models, confirming it as a powerful tool to assist in the individualized management of patients with advanced NSCLC. CLINICAL SIGNIFICANCE: This article highlights that autoML promotes the accuracy and efficiency of feature selection and model construction. The radiomics model generated by autoML predicted the efficacy of immunotherapy in patients with advanced NSCLC effectively. This may provide a rapid and non-invasive method for making personalized clinical decisions.