Shan Yao, Yi Wei, Bin Song

Shan Yao, Yi Wei, Bin Song, Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China

Abstract Medical imaging is of great value for the comprehensive evaluation of hepatocellular carcinoma from diagnosis to prognosis, which contributes to optimal clinical management making.

Key Words: Hepatocellular carcinoma; Medical imaging; Clinical management

TO THE EDITOR

In the current issue, we read with interest a retrospective study by Delvecchioet al[1],where liver resection (LR) and radiofrequency ablation (RFA) were evaluated as the treatment of choice for single hepatocellular carcinoma (HCC) (≤ 30 mm) located in posterosuperior segments (PSS) in elderly patients. Based on operative time, hospital stay, and short- and long-term outcomes, RFA was recommended as a suitable option.

The critical value of tumor size for LR or RFA differs in various criteria and guidelines, most of which is 20 mm. Single HCC with a tumor size of ≤ 30 mm was mainly targeted in this study. Locations with difficulties in surgery (PSS) and age (for the elderly ≥ 70 years old) were considered while making the treatment decision. It offered an insightful perspective and a specific focus, providing a supplement to this field with certain guiding significance for clinical management practice.

As described in the study, all subjects underwent computed tomography (CT) or magnetic resonance imaging (MRI) before treatment to access the tumor location and size, which are the two key points of this study. The diagnosis and stage of HCC were based on the European Association for the Study of the Liver criteria[2], which also regard medical imaging manifestations as a dominant support. Thus,medical imaging plays a vital role in the accurate diagnosis and qualitative evaluation of HCC. Along with morphological features, such as tumor location and size, satellite nodules, portal vein embolus, and invasion of adjacent tissues can be evaluated using CT or MRI, which are also of prognostic significance for patients with HCC after treatment.

Apart from the abovementioned perioperative and postoperative indicators for selecting treatment option, preoperative evaluation can be performed using noninvasive medical imaging with high sensitivity and repeatability. In a study by Chaet al[3], pretreatment imaging was utilized to compare the outcomes of RFA and LR for HCC ≤ 30 mm, and a high positive predictive value was achieved.Burgeoning functional imaging technologies, such as gadoxetic acid-enhanced MRI, intravoxel incoherent motion, T1 mapping, have enabled insightful assessment of microvascular invasion,hepatocyte membrane function, hepatocyte density changes, tissue microcirculation, and liver reserve function. Meanwhile, artificial intelligence-imaging combining radiomics has been empowering deep data mining of CT or MRI images of HCC from diagnosis to prognosis. In prior studies, we found that preoperative CT imaging combined with clinical features could predict the rate of liver regeneration after right hepatectomy for HCCs with an accuracy of 0.78 and an area under the curve (AUC) of 0.84[4]. Gadoxetic acid-enhanced MRI-derived features showed great potential for preoperative prediction of early recurrence of LR for HCCs, with the related model demonstrating a significant AUC of 0.841(95%CI: 0.769-0.919)[5]. Taken together, medical imaging is closely related to optimal treatment decision-making and survival quality for patients. In future clinical practice, it is necessary to take full advantage of medical imaging to comprehensively evaluate tumor and liver conditions preoperatively as a treatment plan trade-off, so as to maximize the benefits for patients with HCC and meet the demands of precision medicine.

FOOTNOTES

Author contributions:Yao S and Song B designed the research; Yao S and Wei Y conducted literature search and analysis; Yao S wrote the letter and made critical revisions to the letter; Song B and Wei Y provided material and funding support for the article.

Supported bythe Science and Technology Support Program of Sichuan Province, No. 2021YFS0144 and No.2021YFS0021; and China Postdoctoral Science Foundation, No. 2021M692289.

Conflict-of-interest statement:All authors declared no potential conflict of interests related to this publication.

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 BYNC 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 noncommercial. See: https://creativecommons.org/Licenses/by-nc/4.0/

Country/Territory of origin:China

ORCID number:Shan Yao 0000-0002-0665-2566; Yi Wei 0000-0003-3993-9747; Bin Song 0000-0001-7007-6367.

S-Editor:Gong ZM

L-Editor:A

P-Editor:Gong ZM