Development and Validation of a Deep Learning-Based Radiomic Signature for Predicting Treatment Response to Immunotherapy in Non-Small Cell Lung Cancer (NSCLC)

Precision oncology faces an essential problem regarding creating stable biomarkers to forecast immunotherapy responses in non-small cell lung cancer (NSCLC). The research develops and validates deep learning-based radiomic signatures that provide accurate prediction potential regarding NSCLC immune therapy responses in patients. Radiomics methods were applied to CT and PET images for pre-treatment data extraction, producing heterogeneous tumor features. The researchers employed a deep learning model to analyze these features to develop to develop an effective radio mic signature to determine immunotherapy response. The model used NSCLC patients who received immunotherapy for model training and testing purposes using RECIST criteria and progression-free survival (PFS) for treatment response measurement. Researchers evaluated the radiomic signature performance by assessing accuracy and sensitivity alongside specificity and the Area under the receiver operating characteristic curve (AUC). A deep learning-based radiomic signature proved much more valuable than standard clinical and pathological measures as it effectively predicted which patients would profit from immunotherapy. The signature established generalizability through additional testing on different patient groups, which confirmed its reliability. The findings suggest that uniting deep learning technology with radio mics is a non-surgical approach for tailoring therapy plans, enhancing patient success, and reducing untreated cutting therapies in NSCLC.