Potential advantages of FDG-PET radiomic feature map for target volume delineation in lung cancer radiotherapy

To investigate the potential benefits of FDG PET radiomic feature maps (RFMs) for target delineation in non-small cell lung cancer (NSCLC) radiotherapy.

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 (GTV_CT , CTV_CT ), PET (GTV_PET40 , CTV_PET40 ), and RFMs (GTV_RFM , CTV_RFM ,). Intratumoral heterogeneity areas were segmented as GTV_PET50-Boost and radiomic boost target volume (RTV_Boost ) on PET and RFMs, respectively. GTV_CT in homogenous tumors and GTV_PET40 in heterogeneous tumors were considered as GTV_{gold standard} (GTV_GS ). One-way analysis of variance was conducted to determine the threshold that finds the best conformity for GTV_RFM with GTV_GS . 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.

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 GTV_RFM-entropy , GTV_RFM-contrast , and GTV_{RFM-H-correlation} with GTV_GS , respectively. The linear regression results showed a positive correlation between GTV_GS and GTV_RFM-entropy (r = 0.98, p < 0.001), between GTV_GS and GTV_RFM-contrast (r = 0.93, p < 0.001), and between GTV_GS and GTV_{RFM-H-correlation} (r = 0.91, p < 0.001). The average threshold values of 45% and 15% were resulted in the best segmentation matching between CTV_RFM-entropy and CTV_RFM-contrast with CTV_GS , respectively. Moreover, we used RFM to determine RTV_Boost in the heterogeneous tumors. Comparison of RTV_Boost with GTV_PET50-Boost MAPE showed the volume error differences of 31.7%, 36%, and 34.7% in RTV_{Boost-entropy} , RTV_{Boost-contrast} , and RTV_{Boost-H-correlation} , respectively.

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.