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.
Keywords: grey-level co-occurrence matrix; non-small cell lung cancer; positron emission tomography/ computed tomography; radiomics; radiotherapy; segmentation.
© 2022 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, LLC on behalf of The American Association of Physicists in Medicine.