Your browser doesn't support javascript.
loading
Predicting Microsatellite Instability Status in Colorectal Cancer Based on Triphasic Enhanced Computed Tomography Radiomics Signatures: A Multicenter Study.
Cao, Yuntai; Zhang, Guojin; Zhang, Jing; Yang, Yingjie; Ren, Jialiang; Yan, Xiaohong; Wang, Zhan; Zhao, Zhiyong; Huang, Xiaoyu; Bao, Haihua; Zhou, Junlin.
Affiliation
  • Cao Y; Department of Radiology, Affiliated Hospital of Qinghai University, Xining, China.
  • Zhang G; Second Clinical School, Lanzhou University, Lanzhou, China.
  • Zhang J; Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China.
  • Yang Y; Key Laboratory of Medical Imaging, Lanzhou, China.
  • Ren J; Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China.
  • Yan X; Sichuan Academy of Medical Sciences Sichuan Provincial People's Hospital, Chengdu, China.
  • Wang Z; Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China.
  • Zhao Z; The Fifth Affiliated Hospital of Zunyi Medical University, Zhuhai, China.
  • Huang X; Department of Radiology, Second People's Hospital of Lanzhou City, Lanzhou, China.
  • Bao H; Department of Pharmaceuticals Diagnosis, GE Healthcare, Beijing, China.
  • Zhou J; Department of Critical Medicine, Affiliated Hospital of Qinghai University, Xining, China.
Front Oncol ; 11: 687771, 2021.
Article de En | MEDLINE | ID: mdl-34178682
ABSTRACT

BACKGROUND:

This study aimed to develop and validate a computed tomography (CT)-based radiomics model to predict microsatellite instability (MSI) status in colorectal cancer patients and to identify the radiomics signature with the most robust and high performance from one of the three phases of triphasic enhanced CT.

METHODS:

In total, 502 colorectal cancer patients with preoperative contrast-enhanced CT images and available MSI status (441 in the training cohort and 61 in the external validation cohort) were enrolled from two centers in our retrospective study. Radiomics features of the entire primary tumor were extracted from arterial-, delayed-, and venous-phase CT images. The least absolute shrinkage and selection operator method was used to retain the features closely associated with MSI status. Radiomics, clinical, and combined Clinical Radiomics models were built to predict MSI status. Model performance was evaluated by receiver operating characteristic curve analysis.

RESULTS:

Thirty-two radiomics features showed significant correlation with MSI status. Delayed-phase models showed superior predictive performance compared to arterial- or venous-phase models. Additionally, age, location, and carcinoembryonic antigen were considered useful predictors of MSI status. The Clinical Radiomics nomogram that incorporated both clinical risk factors and radiomics parameters showed excellent performance, with an AUC, accuracy, and sensitivity of 0.898, 0.837, and 0.821 in the training cohort and 0.964, 0.918, and 1.000 in the validation cohort, respectively.

CONCLUSIONS:

The proposed CT-based radiomics signature has excellent performance in predicting MSI status and could potentially guide individualized therapy.
Mots clés

Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Type d'étude: Clinical_trials / Observational_studies / Prognostic_studies / Risk_factors_studies Langue: En Journal: Front Oncol Année: 2021 Type de document: Article Pays d'affiliation: Chine

Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Type d'étude: Clinical_trials / Observational_studies / Prognostic_studies / Risk_factors_studies Langue: En Journal: Front Oncol Année: 2021 Type de document: Article Pays d'affiliation: Chine