MRI radiomics independent of clinical baseline characteristics and neoadjuvant treatment modalities predicts response to neoadjuvant therapy in rectal cancer

被引:33
作者
Song, Maxiaowei [1 ]
Li, Shuai [1 ]
Wang, Hongzhi [1 ]
Hu, Ke [2 ]
Wang, Fengwei [3 ]
Teng, Huajing [1 ]
Wang, Zhi [4 ]
Liu, Jin [4 ]
Jia, Angela Y. [5 ]
Cai, Yong [1 ]
Li, Yongheng [1 ]
Zhu, Xianggao [1 ]
Geng, Jianhao [1 ]
Zhang, Yangzi [1 ]
Wan, XiangBo [6 ]
Wang, Weihu [1 ]
机构
[1] Peking Univ Canc Hosp & Inst, Dept Radiat Oncol, Key Lab Carcinogenesis & Translat Res, Minist Educ Beijing, Beijing, Peoples R China
[2] Peking Union Med Coll Hosp, Chinese Acad Med Sci & Peking Union Med Coll, Dept Radiat Oncol, Beijing, Peoples R China
[3] Tianjin Union Med Ctr, Dept Oncol, Tianjin, Peoples R China
[4] Blot Info & Tech Beijing Co Ltd, Beijing, Peoples R China
[5] Johns Hopkins Univ, Dept Radiat Oncol & Mol Radiat Sci, Sch Med, Baltimore, MD USA
[6] Sun Yat Sen Univ, Guangdong Inst Gastroenterol, Dept Radiat Oncol, Dept Med Engn,Affiliated Hosp 6, Guangzhou, Peoples R China
基金
北京市自然科学基金;
关键词
PATHOLOGICAL COMPLETE RESPONSE; PREOPERATIVE CHEMORADIOTHERAPY; OPEN-LABEL; TUMOR HETEROGENEITY; PHASE-II; CHEMORADIATION; CHEMOTHERAPY; RADIOTHERAPY; OXALIPLATIN; CAPECITABINE;
D O I
10.1038/s41416-022-01786-7
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Background To analyse the performance of multicentre pre-treatment MRI-based radiomics (MBR) signatures combined with clinical baseline characteristics and neoadjuvant treatment modalities to predict complete response to neoadjuvant (chemo)radiotherapy in locally advanced rectal cancer (LARC). Methods Baseline MRI and clinical characteristics with neoadjuvant treatment modalities at four centres were collected. Decision tree, support vector machine and five-fold cross-validation were applied for two non-imaging and three radiomics-based models' development and validation. Results We finally included 674 patients. Pre-treatment CEA, T stage, and histologic grade were selected to generate two non-imaging models: C model (clinical baseline characteristics alone) and CT model (clinical baseline characteristics combining neoadjuvant treatment modalities). The prediction performance of both non-imaging models were poor. The MBR signatures comprising 30 selected radiomics features, the MBR signatures combining clinical baseline characteristics (CMBR), and the CMBR incorporating neoadjuvant treatment modalities (CTMBR) all showed good discrimination with mean AUCs of 0.7835, 0.7871 and 0.7916 in validation sets, respectively. The three radiomics-based models had insignificant discrimination in performance. Conclusions The performance of the radiomics-based models were superior to the non-imaging models. MBR signatures seemed to reflect LARC's true nature more accurately than clinical parameters and helped identify patients who can undergo organ preservation strategies.
引用
收藏
页码:249 / 257
页数:9
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