Korean J Radiol.
2025 May;26(5):400-410. 10.3348/kjr.2024.0767.
Discriminating Tumor Deposits From Metastatic Lymph Nodes in Rectal Cancer: A Pilot Study Utilizing Dynamic Contrast-Enhanced MRI
- Wu Xh
1 - Que Yt2
- Yang Xy3
- Wen Zq4
- Ma Yr4
- Zhang Zw5
- Liu Qm4
- Fan Wj4,6
- Ding L7
- Lang Yj7
- Wu Yz8
- Yuan Jp6
- Yu Sp4
- Liu Yy4
- Chen Y4
- Affiliations
-
- 1Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
- 2Department of Radiology, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- 3Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
- 4Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- 5Department of Radiology, Cancer Hospital Chinese Academy of Medical Sciences, Shenzhen Center, Shenzhen, China
- 6Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
- 7Department of Pathology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- 8Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China
- KMID: 2567699
- DOI: http://doi.org/10.3348/kjr.2024.0767
Abstract
Objective
To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer.
Materials and Methods
A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups. Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs.
Results
All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027).
Conclusion
The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.
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