Feature level quantitative ultrasound and CT information fusion to predict the outcome of head & neck cancer radiotherapy treatment: Enhanced principal component analysis.

backgroundRadiation therapy is a common treatment for head and neck (H&N) cancers. Radiomic features, which are determined from biomedical imaging, can be effective biomarkers used to assess tumor heterogeneity and have been used to predict response to treatment. However, most studies employ only a single biomedical imaging modality to determine radiomic features. PURPOSE: The objective of this study was to evaluate the effectiveness of radiomic feature fusion, combining quantitative ultrasound spectroscopy (QUS) and computed tomography (CT) imaging modalities, in predicting the outcomes of radiation therapy for H&N cancer prior to start.

methodAn enhanced version of principal component analysis (EPCA) was proposed to fuse 70 radiomic features from CT and 476 radiomic features from QUS in order to predict the response to radiation therapy in patients with H&N cancers (partial response vs. complete response). EPCA is a PCA method with Hessian matrix regularization and

resultsSeventy-one (n = 71) (66 male (93%) and female (7chmch%)) H&N cancer patients were recruited with bulky metastatic neck lymph node (LN) involvement. Patients had a mean age of 59 ± 10 and 25 (35.2%) were complete responders and 46 (64.8%) were partial-responders. In terms of predicting responses, the EPCA-SVM classifier had better performance than EPCA-KNN, and achieved 79

conclusionThe results demonstrated that the proposed predictive model is able to predict a binary H&N cancer treatment outcome, feature level fusion of CT and QUS radiomics has superiority over single imaging modality and EPCA is an effective approach to fuse the features.