Pretreatment MRI-Based Radiomics for Prediction of Rectal Cancer Outcome: A Discovery and Validation Study.

Locally advanced rectal cancer Magnetic resonance imaging Neoadjuvant chemoradiotherapy Prognosis Radiomics

Journal

Academic radiology
ISSN: 1878-4046
Titre abrégé: Acad Radiol
Pays: United States
ID NLM: 9440159

Informations de publication

Date de publication:
22 Nov 2023
Historique:
received: 25 09 2023
revised: 22 10 2023
accepted: 29 10 2023
medline: 24 11 2023
pubmed: 24 11 2023
entrez: 23 11 2023
Statut: aheadofprint

Résumé

Accurate prediction of local recurrence or distant metastasis is critical for developing individualized therapies for locally advanced rectal cancer (LARC) patients after standard therapy. This study aims to develop and validate a multiparameter MRI-based radiomics signature (RS) for prognostic prediction in LARC patients receiving neoadjuvant chemoradiotherapy (nCRT) and total mesorectal excision (TME) and to explore the ability of RS for personalized survival risk stratification. In this multi-center study, 454 patients who received nCRT and TME and completed 3 years of follow-up participated. RS was constructed for prognostic prediction based on features extracted from pretreatment multiparameter MRI in a training cohort (TC; n = 298), which was tested in an internal validation cohort (IVC; n = 75) and further validated in an independent external validation cohort (EVC; n = 81). Furthermore, the ability of RS for personalized survival risk stratification was explored using the Kaplan-Meier survival curves. The RS model showed satisfactory accuracy for prognostic prediction with AUCs of 0.83, 0.81 and 0.82 in the TC, IVC and EVC, respectively. In addition, RS helped to refine risk stratification for LARC patients on the basis of significantly different 3-year disease-free survival rates, independent of their pathological stage, pre-surgery CEA, and even treatment modality. The proposed RS can be used not only to predict local recurrence or distant metastasis but also to serve as an effective postoperative survival risk stratification tool for clinicians to facilitate decision-making for LARC patients receiving standard treatment.

Identifiants

pubmed: 37996362
pii: S1076-6332(23)00610-4
doi: 10.1016/j.acra.2023.10.055
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Hongyan Huang (H)

Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen 518000, P.R. China.

Lujun Han (L)

Department of Medical Imaging, Sun Yat-sen University Cancer Center, 651 Dongfeng Road East, Guangzhou 510060, P.R. China.

Jianbo Guo (J)

Department of Radiology, Meizhou People's Hospital, No. 63 Huangtang Road, Meizhou 514000 P.R. China.

Yanyu Zhang (Y)

Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Duobao AVE 56, Liwan district, Guangzhou, P.R. China.

Shiwei Lin (S)

Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen 518000, P.R. China.

Shengli Chen (S)

Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen 518000, P.R. China.

Xiaoshan Lin (X)

Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen 518000, P.R. China.

Caixue Cheng (C)

Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen 518000, P.R. China.

Zheng Guo (Z)

Department of Hematology and Oncology, International Cancer Center, Shenzhen Key Laboratory of Precision Medicine for Hematological Malignancies, Shenzhen University General Hospital, Shenzhen University Clinical Medical Academy, Shenzhen University Health Science Center, Xueyuan AVE 1098, Nanshan District, Shenzhen, Guangdong 518000, P.R. China.

Yingwei Qiu (Y)

Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen 518000, P.R. China. Electronic address: qiuyw1201@163.com.

Classifications MeSH