Evidence map›Paper›PMID 40487940›Full record

ArticleWorld journal of gastrointestinal oncology2025

Integrating ultrasound and serum indicators for evaluating outcomes of targeted immunotherapy in advanced liver cancer.

Hai-Bin Tu, Si-Yi Feng, Li-Hong Chen, Yu-Jie Huang, Ju-Zhen Zhang, Su-Yu Peng, Ding-Luan Lin, Xiao-Jian Ye

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in World journal of gastrointestinal oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

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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

1 citing paper in PubMed.

  1. Review
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

8 authors.

Hai-Bin TuDepartment of Ultrasound, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, Fujian Province, China.
Si-Yi FengDepartment of Ultrasound, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, Fujian Province, China.
Li-Hong ChenDepartment of Ultrasound, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, Fujian Province, China.
Yu-Jie HuangDepartment of Ultrasound, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, Fujian Province, China.
Ju-Zhen ZhangDepartment of Ultrasound, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, Fujian Province, China.
Su-Yu PengDepartment of Ultrasound, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, Fujian Province, China.
Ding-Luan LinDepartment of Positron Emission Tomography, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, Fujian Province, China.
Xiao-Jian YeDepartment of Ultrasound, The First Affiliated Hospital of Fujian Medical University, Fuzhou 350001, Fujian Province, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHepatocellular carcinoma (HCC) is a major global contributor to cancer-related mortality, with advanced stages presenting substantial therapeutic challenges. Although targeted immunotherapy shows potential, many patients exhibit poor responses, underscoring the need for predictive tools to optimize treatment strategies. Emerging data indicate that ultrasound features (

aimTo develop a non-invasive predictive model for targeted immunotherapy in advanced HCC, incorporating both internal and external validation.

methodsPatients with advanced HCC who received targeted immunotherapy at two medical centers were enrolled and divided into internal training, internal validation, and external validation cohorts. Comprehensive clinical data were gathered. Initially, 13 machine learning algorithms were tested using the internal training cohort. The algorithm yielding the highest area under the curve (AUC) in the internal validation cohort was selected to construct a predictive model, termed the Target Immunotherapy Predictive Model (TIPM). TIPM performance was then compared with that of traditional tumor staging systems (tumor-node-metastasis, Barcelona Clinic Liver Cancer, China Liver Cancer, Hong Kong Liver Cancer, and C-reactive protein and alpha-fetoprotein in immunotherapy).

resultsA total of 306 patients participated in the study, with 143 in the internal training cohort, 62 in the internal validation cohort, and 101 in the external validation cohort. In the internal validation cohort, the random forest model achieved the highest AUC (0.975, 95% confidence interval: 0.924-0.998). The key predictors for TIPM were tumor size, platelet count, tumor stiffness change, and white blood cell count. During external validation, TIPM outperformed conventional models, reaching an AUC of 0.899 (95% confidence interval: 0.840-0.957). Calibration curves demonstrated strong concordance with observed outcomes, while decision curve analysis confirmed TIPM's enhanced clinical value. Additional metrics, such as the net reclassification index and integrated discrimination improvement, further supported TIPM's superior predictive accuracy.

conclusionTIPM provides a robust tool for predicting targeted immunotherapy efficacy in advanced HCC, facilitating personalized treatment planning.

Indexed as

Advanced hepatocellular carcinomaMachine learningNon-invasivePredictive modelingTargeted immunotherapy

Identifiers

PMID40487940
PMCPMC12142245

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