Room: AAPM ePoster Library
To predict residual tumor from radiation changes and inflammation through machine learning (ML) models based on radiomic features (RF) extracted from CT images.
HIPPA-compliant, IRB-approved retrospective analysis of patients with squamous cell carcinoma of the head and neck (HNSCC) treated with chemoradiotherapy (chemoRT) at UMMC. Thirty-six patients with residual disease on CT scan performed in 2 months interval- either in primary site, nodal station or both were enrolled. Figure-1 shows the flowchart of HNSCC patients treated with chemoRT. All gross tumor volumes (GTVs) were transferred from the treatment planning CT scan to MIM® Software. Then, a radiologist contoured the tumors in 2 months follow-up CT2 and 3 months follow-up PET/CT scan using MIM’s tools. Next, the segmented GTVs from CT images were exported to MatLab® where RFs were extracted through different approaches: (a) from the region of interest (ROI) which best represented the GTV (RF2=280), (b) from the volume of interest (VOI) of the GTV (RF3=455). Finally, ML models such as support vector machine (SVM), neural network (NN) and, random forest (RaF) were used to predict changes and progress in HNSCC cancer patients treated with chemoRT.
ML models used all RF extracted from ROIs and VOIs (Figure-2). For 2D scheme, RF extracted from CT2 had the predictive ability to anticipating residual lesion in PET/CT exam (AUC=0.702). For 3D scheme, predicting positive path for residual tumor from CT2 and PET/CT had good and moderate ability (AUC=0.720 and 0.678, respectively). ML models used 10-fold cross-validation for tuning parameter optimization. Variable importance metric >80 was used to select which features contribute most to the predictive ability of each model (Table-1).
ML models based on RF of CT images were able to predict the residual tumor from radiation changes in a small group of HNSCC cancer patients treated with chemoRT.
CT, Texture Analysis, Feature Selection