title: Rectal Cancer: Assessment of Neoadjuvant Chemoradiation Outcome based on Radiomics of Multiparametric MRI.
abstract: OBJECTIVE
  To evaluate multiparametric MRI features in predicting pathologic response after preoperative chemoradiation therapy (CRT) for locally advanced rectal cancer (LARC).
  
  
  METHODS
  Forty-eight consecutive patients (January 2012-November 2014) receiving neoadjuvant CRT were enrolled. All underwent anatomical T1/T2, diffusion-weighted MRI (DWI) and dynamic contrast-enhanced (DCE) MRI before CRT. A total of 103 imaging features, analyzed using both volume-averaged and voxelized methods, were extracted for each patient. Univariate analyses were performed to evaluate the capability of each individual parameter in predicting pathologic complete response (pCR) or good response (GR) evaluated based on tumor regression grade. Artificial neural network with 4-fold validation technique was further utilized to select the best predictor sets to classify different response groups and the predictive performance was calculated using receiver operating characteristic (ROC) curves.
  
  
  RESULTS
  The conventional volume-averaged analysis could provide an area under ROC curve (AUC) ranging from 0.54 to 0.73 in predicting pCR. While if the models were replaced by voxelized heterogeneity analysis, the prediction accuracy measured by AUC could be improved to 0.71-0.79. Similar results were found for GR prediction. In addition, each subcategory images could generate moderate power in predicting the response, which if combining all information together, the AUC could be further improved to 0.84 for pCR and 0.89 for GR prediction, respectively.
  
  
  CONCLUSIONS
  Through a systematic analysis of multiparametric MR imaging features, we are able to build models with improved predictive value over conventional imaging metrics. The results are encouraging, suggesting the wealth of imaging radiomics should be further explored to help tailoring the treatment into the era of personalized medicine. Clin Cancer Res; 22(21); 5256-64. ©2016 AACR.
authors.affiliation: Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, Rutgers-The State University of New Jersey, New Brunswick, New Jersey.
authors.forename: Ke
authors.initials: K
authors.lastname: Nie
authors.affiliation: Department of Radiation Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
authors.forename: Liming
authors.initials: L
authors.lastname: Shi
authors.affiliation: Department of Radiation Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
authors.forename: Qin
authors.initials: Q
authors.lastname: Chen
authors.affiliation: Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
authors.forename: Xi
authors.initials: X
authors.lastname: Hu
authors.affiliation: Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, Rutgers-The State University of New Jersey, New Brunswick, New Jersey.
authors.forename: Salma K
authors.initials: SK
authors.lastname: Jabbour
authors.affiliation: Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, Rutgers-The State University of New Jersey, New Brunswick, New Jersey.
authors.forename: Ning
authors.initials: N
authors.lastname: Yue
authors.affiliation: Department of Radiation Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China. sunxiaonan@hotmail.com tyniu@zju.edu.cn.|Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.|Institute of Translational Medicine, Zhejiang University, Hangzhou, China.
authors.forename: Tianye
authors.initials: T
authors.lastname: Niu
authors.affiliation: Department of Radiation Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China. sunxiaonan@hotmail.com tyniu@zju.edu.cn.
authors.forename: Xiaonan
authors.initials: X
authors.lastname: Sun
mesh_terms: D019540:Area Under Curve; D059248:Chemoradiotherapy / Q000379:methods; D003287:Contrast Media / Q000378:metabolism; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D020360:Neoadjuvant Therapy / Q000379:methods; D012372:ROC Curve; D012004:Rectal Neoplasms / Q000473:pathology* / Q000628:therapy*; D015203:Reproducibility of Results; D016896:Treatment Outcome
chemical_list: D003287:Contrast Media