Tumor Voxel Dose-Response Matrix Prediction Using Deep Learning
International Journal of Radiation Oncology, Biology, Physics
Purpose/Objective(s): Tumor voxel dose-response matrix (DRM) can be assessed using a series of FDG-PET/CT feedback images acquired during radiotherapy. Predicting the tumor voxel DRM earlier is crucial for effectively implementing adaptive treatment management. However, it is also challenging due to FDG uptake dynamic fluctuation in tumor cells. This study investigated the feasibility of predicting tumor voxel DRM during the early treatment weeks using the advanced deep learning (DL) technique. Materials/Methods: Serial FDG-PET/CT images were acquired at the pretreatment (pre-Tx), the 2nd and 4th treatment weeks during standard chemoradiotherapy (35 £ 2 Gy) from each of the 50 patients with head and neck squamous cell carcinomas (HNSCC). The reference value of tumor voxel DRM (DRMref), representing the average metabolic change ratio during the treatment, was determined using a linear regression performed on the standard uptake values (SUV)s obtained at the pre-Tx (SUV0), the 2nd (SUV2) and the 4th (SUV4) treatment weeks following deformable PET/CT image registration. A DL model, 3D residual-Unet with a total of 3.4 million parameters, was trained to predict the tumor voxel DRMref with using the SUV0 and SUV2 matrices as inputs. The performance of the DL model was evaluated using 10-fold cross-validation and was compared to that of a linear regression (LR) model determined on the SUV0 and SUV2 matrices. Results: The mean (SD) of the tumor voxel DRMref was 0.46 (0.2) over all 34612 tumor voxels. The predicted tumor voxel DRM was 0.5 (0.38) and 0.46 (0.15) for the LR model and the DL model, respectively. For those resistant voxels (23.7% of all tumor voxels) with a DRMref > 0.6, the DRM deviation was 0.13 (0.4) and -0.11 (0.13) for the LR model and the DL model, respectively. For those sensitive voxels (76.3%) with a DRMref ≤ 0.6, the DRM deviation was 0.01 (0.23) and 0.03 (0.08) for the LR model and the DL model, respectively. Conclusion: The proposed DL model can predict the tumor voxel DRM with a single FDG-PET feedback image acquired during the 2nd treatment week of radiotherapy for HNSCC patients. The prediction accuracy was improved compared to that of the LR model with a substantial reduction in the variances of the prediction errors. This work demonstrates the great potential of utilizing DL techniques to improve the efficiency of tumor response assessment and adaptive treatment management.
Chen S, Yan D, Qin A, Deraniyagala R, Krauss DJ, Chen PY, et al. [Stevens CW, Snyder M]. Tumor voxel dose-response matrix prediction using deep learning. Int J Radiat Oncol Biol Phys. 2023 Oct;117(2 Suppl.):S66-S67. doi:10.1016/j.ijrobp.2023.06.370