Evidence map›Paper›PMID 39466459›Full record

ArticleInsights into imaging2024

Prediction of microvascular invasion in hepatocellular carcinoma with conventional ultrasound, Sonazoid-enhanced ultrasound, and biochemical indicator: a multicenter study.

Dan Lu, Li-Fan Wang, Hong Han, Lin-Lin Li, Wen-Tao Kong, Qian Zhou, Bo-Yang Zhou, Yi-Kang Sun, Hao-Hao Yin, Ming-Rui Zhu and 7 more

Abstract read
In one paragraph

Article in Insights into imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Dan Lu *Department of Ultrasound, Institute of Ultrasound in Medicine and Engineering, Zhongshan Hospital, Fudan University, Shanghai, China.
Li-Fan Wang *Department of Ultrasound, Institute of Ultrasound in Medicine and Engineering, Zhongshan Hospital, Fudan University, Shanghai, China.
Hong Han *Department of Ultrasound, Institute of Ultrasound in Medicine and Engineering, Zhongshan Hospital, Fudan University, Shanghai, China.
Lin-Lin LiDepartment of Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong, Provincial Clinical Research Center for Cancer, Guangzhou, China.
Wen-Tao KongDepartment of Ultrasound, Nanjing DrumTower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China.
Qian ZhouDepartment of Ultrasound, Nanjing DrumTower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China.
Bo-Yang ZhouDepartment of Ultrasound, Institute of Ultrasound in Medicine and Engineering, Zhongshan Hospital, Fudan University, Shanghai, China.
Yi-Kang SunDepartment of Ultrasound, Institute of Ultrasound in Medicine and Engineering, Zhongshan Hospital, Fudan University, Shanghai, China.
Hao-Hao YinDepartment of Ultrasound, Institute of Ultrasound in Medicine and Engineering, Zhongshan Hospital, Fudan University, Shanghai, China.
Ming-Rui ZhuDepartment of Ultrasound, Institute of Ultrasound in Medicine and Engineering, Zhongshan Hospital, Fudan University, Shanghai, China.
Xin-Yuan HuSchool of Medicine, Anhui University of Science and Technology, Anhui, China.
Qing LuDepartment of Ultrasound, Institute of Ultrasound in Medicine and Engineering, Zhongshan Hospital, Fudan University, Shanghai, China.
Han-Sheng XiaDepartment of Ultrasound, Institute of Ultrasound in Medicine and Engineering, Zhongshan Hospital, Fudan University, Shanghai, China.
Xi WangDepartment of Ultrasound, Institute of Ultrasound in Medicine and Engineering, Zhongshan Hospital, Fudan University, Shanghai, China.
Chong-Ke ZhaoDepartment of Ultrasound, Institute of Ultrasound in Medicine and Engineering, Zhongshan Hospital, Fudan University, Shanghai, China. zhaochongke123@163.com.
Jian-Hua ZhouDepartment of Ultrasound, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong, Provincial Clinical Research Center for Cancer, Guangzhou, China. zhoujh@sysucc.org.cn.
Hui-Xiong XuDepartment of Ultrasound, Institute of Ultrasound in Medicine and Engineering, Zhongshan Hospital, Fudan University, Shanghai, China. xu.huixiong@126.com.ORCID http://orcid.org/0000-0002-8699-854X

Funding

National Natural Science Foundation of China 82202174
6 · The paper itself

Abstract

purposeTo develop and validate a preoperative prediction model based on multimodal ultrasound and biochemical indicator for identifying microvascular invasion (MVI) in patients with a single hepatocellular carcinoma (HCC) ≤ 5 cm.

methodsFrom May 2022 to November 2023, a total of 318 patients with pathologically confirmed single HCC ≤ 5 cm from three institutions were enrolled. All of them underwent preoperative biochemical, conventional ultrasound (US), and contrast-enhanced ultrasound (CEUS) (Sonazoid, 0.6 mL, bolus injection) examinations. Univariate and multivariate logistic regression analyses on clinical information, biochemical indicator, and US imaging features were performed in the training set to seek independent predictors for MVI-positive. The models were constructed and evaluated using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis in both validation and test sets. Subgroup analyses in patients with different liver background and tumor sizes were conducted to further investigate the model's performance.

resultsLogistic regression analyses showed that obscure tumor boundary in B-mode US, intra-tumoral artery in pulsed-wave Doppler US, complete Kupffer-phase agent clearance in Sonazoid-CEUS, and biomedical indicator PIVKA-II were independently correlated with MVI-positive. The combined model comprising all predictors showed the highest AUC, which were 0.937 and 0.893 in the validation and test sets. Good calibration and prominent net benefit were achieved in both sets. No significant difference was found in subgroup analyses.

conclusionsThe combination of biochemical indicator, conventional US, and Sonazoid-CEUS features could help preoperative MVI prediction in patients with a single HCC ≤ 5 cm. CRITICAL RELEVANCE STATEMENT: Investigation of imaging features in conventional US, Sonazoid-CEUS, and biochemical indicators showed a significant relation with MVI-positivity in patients with a single HCC ≤ 5 cm, allowing the construction of a model for preoperative prediction of MVI status to help treatment decision making. KEY POINTS: MVI status is important for patients with a single HCC ≤ 5 cm. The model based on conventional US, Sonazoid-CEUS and PIVKA-II performs best for MVI prediction. The combined model has potential for preoperative prediction of MVI status.

Indexed as

Contrast-enhanced ultrasoundHepatocellular carcinomaKupffer-phaseMicrovascular invasionSonazoid

Identifiers

PMID39466459
PMCPMC11519233

What Socratic holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.