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MRI radiomics independent of clinical baseline characteristics and neoadjuvant treatment modalities predicts response to neoadjuvant therapy in rectal cancer.
Song, Maxiaowei; Li, Shuai; Wang, Hongzhi; Hu, Ke; Wang, Fengwei; Teng, Huajing; Wang, Zhi; Liu, Jin; Jia, Angela Y; Cai, Yong; Li, Yongheng; Zhu, Xianggao; Geng, Jianhao; Zhang, Yangzi; Wan, XiangBo; Wang, Weihu.
Afiliación
  • Song M; Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China.
  • Li S; Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China.
  • Wang H; Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China.
  • Hu K; Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
  • Wang F; Department of Oncology, Tianjin Union Medical Center, Tianjin, China.
  • Teng H; Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China.
  • Wang Z; Blot Info & Tech (Beijing) Co. Ltd, Beijing, China.
  • Liu J; Blot Info & Tech (Beijing) Co. Ltd, Beijing, China.
  • Jia AY; Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Cai Y; Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China.
  • Li Y; Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China.
  • Zhu X; Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China.
  • Geng J; Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China.
  • Zhang Y; Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China.
  • Wan X; Department of Radiation Oncology, Department of Medical Engineering, Guangdong Institute of Gastroenterology, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Wang W; Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China. wangweihu88@163.com.
Br J Cancer ; 127(2): 249-257, 2022 07.
Article en En | MEDLINE | ID: mdl-35368044
ABSTRACT

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.
Asunto(s)

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Neoplasias del Recto / Terapia Neoadyuvante Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Br J Cancer Año: 2022 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Neoplasias del Recto / Terapia Neoadyuvante Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Br J Cancer Año: 2022 Tipo del documento: Article País de afiliación: China