Chen HL, He RL, Gu MT, Zhao XY, Song KR, Zou WJ, Jia NY, Liu WM. Nomogram prediction of vessels encapsulating tumor clusters in small hepatocellular carcinoma ≤ 3 cm based on enhanced magnetic resonance imaging. World J Gastrointest Oncol 2024; 16(5): 1808-1820 [PMID: 38764811 DOI: 10.4251/wjgo.v16.i5.1808]
Corresponding Author of This Article
Ning-Yang Jia, PhD, Doctor, Department of Radiology, The Third Affiliated Hospital of Shanghai Naval Military Medical University, No. 225 Changhai Road, Yangpu District, Shanghai 200438, China. ningyangjia@163.com
Research Domain of This Article
Radiology, Nuclear Medicine & Medical Imaging
Article-Type of This Article
Retrospective Study
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Chen HL, He RL, Gu MT, Zhao XY, Song KR, Zou WJ, Jia NY, Liu WM. Nomogram prediction of vessels encapsulating tumor clusters in small hepatocellular carcinoma ≤ 3 cm based on enhanced magnetic resonance imaging. World J Gastrointest Oncol 2024; 16(5): 1808-1820 [PMID: 38764811 DOI: 10.4251/wjgo.v16.i5.1808]
Hui-Lin Chen, Rui-Lin He, School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
Hui-Lin Chen, Kai-Rong Song, Ning-Yang Jia, Department of Radiology, The Third Affiliated Hospital of Shanghai Naval Military Medical University, Shanghai 200438, China
Meng-Ting Gu, Xing-Yu Zhao, Wen-Jie Zou, Wan-Min Liu, Department of Radiology, Tongji University Affiliated Tongji Hospital, Shanghai 200065, China
Co-first authors: Hui-Lin Chen and Rui-Lin He.
Co-corresponding authors: Ning-Yang Jia and Wan-Min Liu.
Author contributions: Chen HL and He RL contributed equally to this work and share first authorship; Jia NY and Liu WM contributed equally to this work and should be considered as co-corresponding authors; Liu WM and Jia NY designed this study; Chen HL, Liu WM, Gu MT, He RL, Zhao XY, Song KR, and Zou WJ performed the primary literature and data collection; Chen HL and Liu WM analyzed the data and wrote the manuscript; Jia NY and Liu WM were responsible for revising the manuscript for important intellectual content; all authors read and approved the final version.
Supported bythe Project of Shanghai Municipal Commission of Health, No. 2022LJ024.
Institutional review board statement: The studies involving human participants were reviewed and approved by the Third Affiliated Hospital of Shanghai Naval Military Medical University and Tongji University Affiliated Tongji Hospital.
Informed consent statement: Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
Conflict-of-interest statement: 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.
Data sharing statement: No additional data are available.
Corresponding author: Ning-Yang Jia, PhD, Doctor, Department of Radiology, The Third Affiliated Hospital of Shanghai Naval Military Medical University, No. 225 Changhai Road, Yangpu District, Shanghai 200438, China. ningyangjia@163.com
Received: November 10, 2023 Peer-review started: November 10, 2023 First decision: January 30, 2024 Revised: February 2, 2024 Accepted: March 12, 2024 Article in press: March 12, 2024 Published online: May 15, 2024 Processing time: 181 Days and 10.3 Hours
Abstract
BACKGROUND
Vessels encapsulating tumor clusters (VETC) represent a recently discovered vascular pattern associated with novel metastasis mechanisms in hepatocellular carcinoma (HCC). However, it seems that no one have focused on predicting VETC status in small HCC (sHCC). This study aimed to develop a new nomogram for predicting VETC positivity using preoperative clinical data and image features in sHCC (≤ 3 cm) patients.
AIM
To construct a nomogram that combines preoperative clinical parameters and image features to predict patterns of VETC and evaluate the prognosis of sHCC patients.
METHODS
A total of 309 patients with sHCC, who underwent segmental resection and had their VETC status confirmed, were included in the study. These patients were recruited from three different hospitals: Hospital 1 contributed 177 patients for the training set, Hospital 2 provided 78 patients for the test set, and Hospital 3 provided 54 patients for the validation set. Independent predictors of VETC were identified through univariate and multivariate logistic analyses. These independent predictors were then used to construct a VETC prediction model for sHCC. The model’s performance was evaluated using the area under the curve (AUC), calibration curve, and clinical decision curve. Additionally, Kaplan-Meier survival analysis was performed to confirm whether the predicted VETC status by the model is associated with early recurrence, just as it is with the actual VETC status and early recurrence.
RESULTS
Alpha-fetoprotein_lg10, carbohydrate antigen 199, irregular shape, non-smooth margin, and arterial peritumoral enhancement were identified as independent predictors of VETC. The model incorporating these predictors demonstrated strong predictive performance. The AUC was 0.811 for the training set, 0.800 for the test set, and 0.791 for the validation set. The calibration curve indicated that the predicted probability was consistent with the actual VETC status in all three sets. Furthermore, the decision curve analysis demonstrated the clinical benefits of our model for patients with sHCC. Finally, early recurrence was more likely to occur in the VETC-positive group compared to the VETC-negative group, regardless of whether considering the actual or predicted VETC status.
CONCLUSION
Our novel prediction model demonstrates strong performance in predicting VETC positivity in sHCC (≤ 3 cm) patients, and it holds potential for predicting early recurrence. This model equips clinicians with valuable information to make informed clinical treatment decisions.
Core Tip: Our research objective is to construct a nomogram that combines preoperative clinical parameters and image features to predict patterns of vessels encapsulating tumor clusters and evaluate the prognosis of small hepatocellular carcinoma patients. The nomogram has undergone extensive validation across multiple patient centers, demonstrating robust predictive performance and promising implications for postoperative recurrence prediction.
