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

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 (GTV(CT), CTVCT), PET (GTV(PET40), CTVPET40), and RFMs (GTV(RFM), CTVRFM,). Intratumoral heterogeneity areas were segmented as GTV(PET50-Boost) and radiomic boost target volume (RTVBoost) 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. 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 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 CTVRFM-entropy and CTVRFM-contrast with CTVGS, respectively. Moreover, we used RFM to determine RTVBoost in the heterogeneous tumors. Comparison of RTVBoost with GTV(PET50-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.

Automated summary

This study investigated 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 were included and nine grey-level co-occurrence matrix (GLCM) RFMs were generated for each patient. The results showed that RFMs, specifically entropy, contrast, and Haralick correlation, were useful for tumor segmentation. Linear regression analysis demonstrated positive correlations between the gold standard and RFM-derived target volumes. These findings suggest that FDG PET-based radiomics features have promising potential for decision support in radiotherapy for NSCLC, aiding in accurate tumor delineation and segmentation of heterogeneous tumor regions.