Retrospective Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. May 15, 2024; 16(5): 1849-1860
Published online May 15, 2024. doi: 10.4251/wjgo.v16.i5.1849
Magnetic resonance imaging-based lymph node radiomics for predicting the metastasis of evaluable lymph nodes in rectal cancer
Yong-Xia Ye, Liu Yang, Zheng Kang, Mei-Qin Wang, Xiao-Dong Xie, Ke-Xin Lou, Jun Bao, Mei Du, Zhe-Xuan Li
Yong-Xia Ye, Zheng Kang, Mei-Qin Wang, Xiao-Dong Xie, Mei Du, Zhe-Xuan Li, Department of Radiology, The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing 210011, Jiangsu Province, China
Liu Yang, Department of Colorectal Surgery, Jiangsu Cancer Hospital and Jiangsu Institute of Cancer Research and The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing 210000, Jiangsu Province, China
Ke-Xin Lou, Department of Pathology, The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing 210011, Jiangsu Province, China
Jun Bao, Colorectal Center, The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing 210011, Jiangsu Province, China
Author contributions: Ye YX contributed to writing-review & editing, writing-original draft, validation, project administration, methodology, investigation, and data curation; Yang L contributed to conceptualization, writing-review & editing, supervision, and funding acquisition; Xie XD contributed to methodology, data curation and formal analysis; Zheng K contributed to investigation, validation and project administration; Wang MQ contributed to validation, resources and investigation; Lou KX contributed to resources and supervision; Li ZX contributed to investigation and visualization; Du M contributed to investigation; Bao J contributed to writing-review & editing, supervision, project administration and funding acquisition.
Supported by the National Natural Science Foundation of China, No. 81602145 and No. 82072704; Jiangsu Province TCM Science and Technology Development Plan Monographic Project, No. ZT202118; Jiangsu Provincial Natural Science Foundation, No. BK20171509; China Postdoctoral Science Foundation, No. 2018M632265; The “333 Talents” Program of Jiangsu Province, No. BRA2020390; Key R&D Plan of Jiangsu Provincial Department of Science and Technology, No. BE2020723; Nanjing Medical University Project, No. NMUC2020046; Nanjing Science and Technology Project, No. 202110027; and Elderly Health Research Project of Jiangsu Provincial Health Commission, No. LR2022006.
Institutional review board statement: The study was reviewed and approved by the Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research Institutional Review Board (Approval No. AF-SOP026-01).
Informed consent statement: The informed consent statement has been exempted by the Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research Institutional Review Board.
Conflict-of-interest statement: 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.
Data sharing statement: Data supporting the findings of this study are available from the corresponding author upon reasonable request.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Liu Yang, MD, PhD, Doctor, Department of Colorectal Surgery, Jiangsu Cancer Hospital and Jiangsu Institute of Cancer Research and The Affiliated Cancer Hospital of Nanjing Medical University, No. 42 Baiziting Road, Nanjing 210000, Jiangsu Province, China. yangliu@njmu.edu.cn
Received: December 18, 2023
Peer-review started: December 19, 2023
First decision: January 15, 2024
Revised: January 23, 2024
Accepted: March 4, 2024
Article in press: March 4, 2024
Published online: May 15, 2024
Processing time: 142 Days and 23.5 Hours
Abstract
BACKGROUND

Lymph node (LN) staging in rectal cancer (RC) affects treatment decisions and patient prognosis. For radiologists, the traditional preoperative assessment of LN metastasis (LNM) using magnetic resonance imaging (MRI) poses a challenge.

AIM

To explore the value of a nomogram model that combines Conventional MRI and radiomics features from the LNs of RC in assessing the preoperative metastasis of evaluable LNs.

METHODS

In this retrospective study, 270 LNs (158 nonmetastatic, 112 metastatic) were randomly split into training (n = 189) and validation sets (n = 81). LNs were classified based on pathology-MRI matching. Conventional MRI features [size, shape, margin, T2-weighted imaging (T2WI) appearance, and CE-T1-weighted imaging (T1WI) enhancement] were evaluated. Three radiomics models used 3D features from T1WI and T2WI images. Additionally, a nomogram model combining conventional MRI and radiomics features was developed. The model used univariate analysis and multivariable logistic regression. Evaluation employed the receiver operating characteristic curve, with DeLong test for comparing diagnostic performance. Nomogram performance was assessed using calibration and decision curve analysis.

RESULTS

The nomogram model outperformed conventional MRI and single radiomics models in evaluating LNM. In the training set, the nomogram model achieved an area under the curve (AUC) of 0.92, which was significantly higher than the AUCs of 0.82 (P < 0.001) and 0.89 (P < 0.001) of the conventional MRI and radiomics models, respectively. In the validation set, the nomogram model achieved an AUC of 0.91, significantly surpassing 0.80 (P < 0.001) and 0.86 (P < 0.001), respectively.

CONCLUSION

The nomogram model showed the best performance in predicting metastasis of evaluable LNs.

Keywords: Radiomics; Lymph node metastasis; Rectal cancer; Magnetic resonance imaging

Core Tip: We have developed and validated a predictive model that combines radiomic features with conventional magnetic resonance imaging features. This model has shown promising results in preoperative assessment of lymph node metastasis (LNM), improving the accuracy of LNM evaluation by radiologists. Additionally, the study has focused on individual LNs and has the potential to provide information on both the quantity and location of metastatic LNs.