MRI-based pre-Radiomics and delta-Radiomics models accurately predict the post-treatment response of rectal adenocarcinoma to neoadjuvant chemoradiotherapy.

MRI machine learning neoadjuvant chemoradiotherapy radiomics rectal adenocarcinoma

Journal

Frontiers in oncology
ISSN: 2234-943X
Titre abrégé: Front Oncol
Pays: Switzerland
ID NLM: 101568867

Informations de publication

Date de publication:
2023
Historique:
received: 28 12 2022
accepted: 06 02 2023
entrez: 17 3 2023
pubmed: 18 3 2023
medline: 18 3 2023
Statut: epublish

Résumé

To develop and validate magnetic resonance imaging (MRI)-based pre-Radiomics and delta-Radiomics models for predicting the treatment response of local advanced rectal cancer (LARC) to neoadjuvant chemoradiotherapy (NCRT). Between October 2017 and August 2022, 105 LARC NCRT-naïve patients were enrolled in this study. After careful evaluation, data for 84 patients that met the inclusion criteria were used to develop and validate the NCRT response models. All patients received NCRT, and the post-treatment response was evaluated by pathological assessment. We manual segmented the volume of tumors and 105 radiomics features were extracted from three-dimensional MRIs. Then, the eXtreme Gradient Boosting algorithm was implemented for evaluating and incorporating important tumor features. The predictive performance of MRI sequences and Synthetic Minority Oversampling Technique (SMOTE) for NCRT response were compared. Finally, the optimal pre-Radiomics and delta-Radiomics models were established respectively. The predictive performance of the radionics model was confirmed using 5-fold cross-validation, 10-fold cross-validation, leave-one-out validation, and independent validation. The predictive accuracy of the model was based on the area under the receiver operator characteristic (ROC) curve (AUC). There was no significant difference in clinical factors between patients with good and poor reactions. Integrating different MRI modes and the SMOTE method improved the performance of the radiomics model. The pre-Radiomics model (train AUC: 0.93 ± 0.06; test AUC: 0.79) and delta-Radiomcis model (train AUC: 0.96 ± 0.03; test AUC: 0.83) all have high NCRT response prediction performance by LARC. Overall, the delta-Radiomics model was superior to the pre-Radiomics model. MRI-based pre-Radiomics model and delta-Radiomics model all have good potential to predict the post-treatment response of LARC to NCRT. Delta-Radiomics analysis has a huge potential for clinical application in facilitating the provision of personalized therapy.

Identifiants

pubmed: 36925913
doi: 10.3389/fonc.2023.1133008
pmc: PMC10013156
doi:

Types de publication

Journal Article

Langues

eng

Pagination

1133008

Informations de copyright

Copyright © 2023 Wang, Wu, Tian, Ma, Jiang, Zhao, Cui, Li, Hu, Yu and Xu.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Auteurs

Likun Wang (L)

Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
Department of Molecular Imaging and Nuclear Medicine, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.
Department of Ultrasound Medicine, The First Affiliated Hospital of Hebei North University, Zhangjiakou, China.

Xueliang Wu (X)

Graduate School, Tianjin Medical University, Tianjin, China.
Department of Gastrointestinal Surgery, Tianjin Medical University Nankai Hospital, Tianjin, China.

Ruoxi Tian (R)

Department of Colorectal Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences, Beijing, China.

Hongqing Ma (H)

Department of General Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.

Zekun Jiang (Z)

College of Computer Science, Sichuan University, Chengdu, China.

Weixin Zhao (W)

College of Computer Science, Sichuan University, Chengdu, China.

Guoqing Cui (G)

Medical Image Center, The First Affiliated Hospital of Hebei North University, Zhangjiakou, China.

Meng Li (M)

Graduate School, Hebei North University, Zhangjiakou, China.

Qinsheng Hu (Q)

Department of Orthopedics, West China Hospital, Sichuan University, Chengdu, Sichuan, China.

Xiangyang Yu (X)

Department of Gastrointestinal Surgery, Tianjin Medical University Nankai Hospital, Tianjin, China.

Wengui Xu (W)

Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.

Classifications MeSH