Predictions of Response in Non-small Cell Lung Cancer Patients Treated with Immune Checkpoint Inhibitors Using Clinical Data, Deep Learning, and Radiomics.

[BACKGROUND] Determining predictive biomarkers for immunotherapy response in non-small cell lung cancer (NSCLC) patients is a complex task.

[OBJECTIVE] This research aimed to develop a multimodal model (CRDL) integrating clinical data, deep learning (DL), and radiomics (Rad) to predict immune responses in NSCLC patients receiving checkpoint blockade therapies. This study also evaluated whether CRDL outperforms unimodal, pre-fusion models (Pre-FMs) and post-fusion models (Post-FMs).

[METHODS] This is a retrospective study that utilized data from 228 lung cancer patients at the Memorial Sloan Kettering Cancer Center, with varying Programmed Death-Ligand 1(PD-L1) expression levels among the patients. 228 NSCLC patients were randomly divided into two groups in a 7:3 ratio: the training cohort (n=159) and the validation cohort (n=69). Image histological features were extracted using the "PyRadiomics" package, and DL features were obtained through the deep convolutional neural network from chest computed tomography images, and clinical data from the patients were also collected. Feature reduction was performed using t-tests and the Least absolute shrinkage and selection operator regression. Unimodal modal and Pre-FMs were constructed using random forests, while the post-fusion model was developed using a support vector machine approach. The performance of the model was measured by the area under the receiver operating characteristic curve (AUC).

[RESULTS] 512 DL features and 382 Rad features were extracted. The CRDL model demonstrated superior performance with AUC values of 0.884 in the validation dataset and 0.976 in the training dataset, surpassing the best DL model in both unimodal and pre-fusion settings, which had training and validation AUCs of 0.854 and 0.749.

[CONCLUSION] The CRDL model accurately forecasts immunotherapy responses in NSCLC patients, offering one dependable non-invasive test.