Prediction of the Efficacy of Radiotherapy in Head-and-neck Tumors Patients by Dosiomics and Radiomics

Abstract

Background: The purpose is to investigate the efficacy of distant metastasis (DM) and locoregional recurrences (LRs) prediction through radiomics and dosimics in head-and-neck cancer (HNC) cases. Methods: The cases of HNC were obtained from The Cancer Imaging Archive. For the radiomics model, the features were extracted from the pretreatment computed tomography image by the pyradiomics, then the condensed features were selected as the clinically related features by the Boruta method. Finally, the random forest was used to construct the DM and LR prediction model by inputting those condensed features. For the dosiomics model, the features were extracted from the 3-dimensional dose distribution from radiation treatment plans. The radiomics and dosiomics condensed features were utilized to training and validate the prediction model’s performance. The area under the curve (AUC) value and receiver operating characteristic (ROC) curve were used to assess and compare these models. Results: Seven related features were extracted by the Boruta algorithm, which included one radiomics and two dosiomics features for the DM; two radiomics and two dosiomics features for the LR. Independent training and validation of the prediction and prognostic performance of the model have been observed. The roc-AUC values of the training model for the Rmodel and Dmodel were 0.793 and 0.797, 0.657 and 0.650 for the DM and LR; the roc-AUC values of the validation model for the Rmodel and Dmodel were 0.733 and 0.767, 0.646 and 0.6 for the DM and LR. The roc-AUC values of the training and validation for the radiomics and dosiomics integration model were 0.772 and 0.7, 0.792 and 0.762 for the DM and LR, respectively. Conclusion: Integration of radiomics and dosiomics prediction model can benefit LR in the radiotherapy patient of the head-and-neck squamous cell carcinoma, so the dosiomics should not be neglected for the related investigations.