Rationale Accurate early prediction of chemotherapy response in non-small cell lung cancer (NSCLC) remains a clinical challenge. This study aimed to evaluate the utility of PET-based radiomic features extracted from 18 Ffluorodeoxyglucose ( 18 FFDG) PET/computed tomography (CT) scans in predicting treatment response to platinum based chemotherapy in NSCLC patients. Methods An ambispective observational study was conducted on 70 histopathologically confirmed NSCLC patients who underwent 18 FFDG PET/CT imaging before and after chemotherapy at a tertiary cancer center. Radiomic features were extracted from the primary lesion using commercially available texture analysis software, applying a fixed standardized uptake value (SUV) threshold-based volume of interest segmentation. Conventional metabolic parameters SUVmax, SUVmean, metabolic tumor volume (MTV), total lesion glycolysis (TLG) and 46 radiomic features (GLRLM, NGLDM, GLCM, shape features) were analyzed. Treatment response was assessed using PERCIST criteria and patients were categorized as responders or non-responders. Statistical analysis included Mann–Whitney U test and receiver operating characteristic analysis to determine discriminatory ability of parameters. Results Among the radiomic and metabolic parameters evaluated, only a few showed statistically significant differences between responders and non-responders. GLRLM GLNU, GLCM correlation, NGLDM contrast, and shape surface demonstrated fair predictive performance with area under the curve (AUC) values >0.7. Conventional parameters such as SUVmax and TLG did not show statistically significant differences. The optimal cutoff values, sensitivity, specificity, AUCs, and P values of significant features were also obtained. Conclusion Select radiomic features derived from 18 FFDG PET/CT scans, particularly GLNU, GLCM correlation, and NGLDM contrast, hold promise in predicting response to platinum-based chemotherapy in NSCLC. These findings suggest the potential utility of radiomics in enhancing personalized treatment strategies, although further validation is warranted.
Prasanth et al. (Thu,) studied this question.