标题:Radiomics analysis of DWI data to identify the rectal cancer patients qualified for local excision after neoadjuvant chemoradiotherapy
作者:Tang, Zhenchao; Liu, Zhenyu; Zhang, Xiaoyan; Shi, Yanjie; Wang, Shou; Fang, Mengjie; Sun, Yingshi; Dong, Enqing; Tian, Jie
通讯作者:Dong, EQ;Tian, J;Sun, YS;Tian, J
作者机构:[Tang, Zhenchao; Dong, Enqing] Shandong Univ, Sch Mech Elect & Informat Engn, Weihai 264209, Shandong, Peoples R China.; [Liu, Zhenyu; Wang, Shou; F 更多
会议名称:Conference on Medical Imaging - Computer-Aided Diagnosis
会议日期:FEB 12-15, 2018
来源:MEDICAL IMAGING 2018: COMPUTER-AIDED DIAGNOSIS
出版年:2018
卷:10575
DOI:10.1117/12.2293329
关键词:Locally advanced rectal cancer; Radiomcis; Diffusion weight imaging;; Local excision
摘要:The Locally advanced rectal cancer (LARC) patients were routinely treated with neoadjuvant chemoradiotherapy (CRT) firstly and received total excision afterwards. While, the LARC patients might relieve to T1N0M0/T0N0M0 stage after the CRT, which would enable the patients be qualified for local excision. However, accurate pathological TNM stage could only be obtained by the pathological examination after surgery. We aimed to conduct a Radiomics analysis of Diffusion weighted Imaging (DWI) data to identify the patients in T1N0M0/T0N0M0 stages before surgery, in hope of providing clinical surgery decision support. 223 routinely treated LARC patients in Beijing Cancer Hospital were enrolled in current study. DWI data and clinical characteristics were collected after CRT. According to the pathological TNM stage, the patients of T1N0M0 and T0N0M0 stages were labelled as 1 and the other patients were labelled as 0. The first 123 patients in chronological order were used as training set, and the rest patients as validation set. 563 image features extracted from the DWI data and clinical characteristics were used as features. Two-sample T test was conducted to pre-select the top 50% discriminating features. Least absolute shrinkage and selection operator (Lasso)-Logistic regression model was conducted to further select features and construct the classification model. Based on the 14 selected image features, the area under the Receiver Operating Characteristic (ROC) curve (AUC) of 0.8781, classification Accuracy (ACC) of 0.8432 were achieved in the training set. In the validation set, AUC of 0.8707, ACC (ACC) of 0.84 were observed.
收录类别:CPCI-S
资源类型:会议论文
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