LAUSR

Abstract Purpose To investigate the potential benefits of FDG PET radiomic feature maps (RFMs) for target delineation in non‐small cell lung cancer (NSCLC) radiotherapy. Methods Thirty‐two NSCLC patients undergoing FDG PET/CT imaging were included. For each patient, nine grey‐level co‐occurrence matrix (GLCM) RFMs were generated. gross target volume (GTV) and clinical target volume (CTV) were contoured on CT (GTVCT, CTVCT), PET (GTVPET40, CTVPET40), and RFMs (GTVRFM, CTVRFM,). Intratumoral heterogeneity areas were segmented as GTVPET50‐Boost and radiomic boost target volume (RTVBoost) on PET and RFMs, respectively. GTVCT in homogenous tumors and GTVPET40 in heterogeneous tumors were considered as GTVgold standard (GTVGS). One‐way analysis of variance was conducted to determine the threshold that finds the best conformity for GTVRFM with GTVGS. Dice similarity coefficient (DSC) and mean absolute percent error (MAPE) were calculated. Linear regression analysis was employed to report the correlations between the gold standard and RFM‐derived target volumes. Results Entropy, contrast, and Haralick correlation (H‐correlation) were selected for tumor segmentation. The threshold values of 80%, 50%, and 10% have the best conformity of GTVRFM‐entropy, GTVRFM‐contrast, and GTVRFM‐H‐correlation with GTVGS, respectively. The linear regression results showed a positive correlation between GTVGS and GTVRFM‐entropy (r = 0.98, p < 0.001), between GTVGS and GTVRFM‐contrast (r = 0.93, p < 0.001), and between GTVGS and GTVRFM‐H‐correlation (r = 0.91, p < 0.001). The average threshold values of 45% and 15% were resulted in the best segmentation matching between CTVRFM‐entropy and CTVRFM‐contrast with CTVGS, respectively. Moreover, we used RFM to determine RTVBoost in the heterogeneous tumors. Comparison of RTVBoost with GTVPET50‐Boost MAPE showed the volume error differences of 31.7%, 36%, and 34.7% in RTVBoost‐entropy, RTVBoost‐contrast, and RTVBoost‐H‐correlation, respectively. Conclusions FDG PET‐based radiomics features in NSCLC demonstrated a promising potential for decision support in radiotherapy, helping radiation oncologists delineate tumors and generate accurate segmentation for heterogeneous region of tumors.