Lindner C. Contributing to the prediction of prognosis for treated hepatocellular carcinoma: Imaging aspects that sculpt the future. World J Gastrointest Surg 2024; 16(10): 3377-3380 [DOI: 10.4240/wjgs.v16.i10.3377]
Corresponding Author of This Article
Cristian Lindner, MD, Doctor, Department of Radiology, Faculty of Medicine, University of Concepcion, No. 1290 Victor Lamas, Concepcion 4030000, Biobío, Chile. clindner@udec.cl
Research Domain of This Article
Radiology, Nuclear Medicine & Medical Imaging
Article-Type of This Article
Letter to the Editor
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
World J Gastrointest Surg. Oct 27, 2024; 16(10): 3377-3380 Published online Oct 27, 2024. doi: 10.4240/wjgs.v16.i10.3377
Contributing to the prediction of prognosis for treated hepatocellular carcinoma: Imaging aspects that sculpt the future
Cristian Lindner
Cristian Lindner, Department of Radiology, Faculty of Medicine, University of Concepcion, Concepcion 4030000, Biobío, Chile
Cristian Lindner, Department of Radiology, Hospital Regional Guillermo Grant Benavente, Concepcion 4030000, Biobío, Chile
Author contributions: Lindner C wrote this article.
Conflict-of-interest statement: The author declares that there is no conflict of interest.
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: Cristian Lindner, MD, Doctor, Department of Radiology, Faculty of Medicine, University of Concepcion, No. 1290 Victor Lamas, Concepcion 4030000, Biobío, Chile. clindner@udec.cl
Received: July 27, 2024 Revised: August 19, 2024 Accepted: August 28, 2024 Published online: October 27, 2024 Processing time: 62 Days and 1.9 Hours
Abstract
A novel nomogram model to predict the prognosis of hepatocellular carcinoma (HCC) treated with radiofrequency ablation and transarterial chemoembolization was recently published in the World Journal of Gastrointestinal Surgery. This model includes clinical and laboratory factors, but emerging imaging aspects, particularly from magnetic resonance imaging (MRI) and radiomics, could enhance the predictive accuracy thereof. Multiparametric MRI and deep learning radiomics models significantly improve prognostic predictions for the treatment of HCC. Incorporating advanced imaging features, such as peritumoral hypointensity and radiomics scores, alongside clinical factors, can refine prognostic models, aiding in personalized treatment and better predicting outcomes. This letter underscores the importance of integrating novel imaging techniques into prognostic tools to better manage and treat HCC.
Core Tip: Emerging imaging techniques, particularly multiparametric magnetic resonance imaging and radiomics, enhance prognostic models for hepatocellular carcinoma (HCC) treated with radiofrequency ablation and transarterial chemoembolization. Incorporating advanced imaging features alongside clinical factors refines prediction accuracy, aiding personalized treatment and improving patient outcomes. This integration is crucial for advancing HCC prognosis and therapy.
