Evidence map›Paper›PMID 42712913›Full record

ArticleJournal of hepatocellular carcinoma2026

Dynamic Vascular Spatiotemporal Heterogeneity on Multiphase CT for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma.

Shucheng Yang, Meng Liu, Yongjie Zhou, Jinhong Zhao, Yongming Tan, Weijia Wang

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Article in Journal of hepatocellular carcinoma, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Shucheng Yang *Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, People's Republic of China.
Meng Liu *Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, People's Republic of China.
Yongjie ZhouDepartment of Radiology, Jiangxi Cancer Hospital, Nanchang, 330006, People's Republic of China.
Jinhong ZhaoDepartment of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People's Republic of China.
Yongming TanDepartment of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, People's Republic of China.
Weijia WangClinical Research Center for Medical Imaging of Jiangxi Province, Nanchang, 330006, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Microvascular invasion (MVI) is an important determinant of postoperative recurrence in hepatocellular carcinoma (HCC) but is usually confirmed only after surgery. This study aimed to develop and externally validate a multiphase CT framework integrating dynamic vascular trajectory, spatial intratumoral heterogeneity, and deep imaging features for preoperative MVI prediction and exploratory prognostic stratification. Patients and Methods: This retrospective multicenter study included 405 patients with pathologically confirmed HCC: 198 in the development cohort and 207 in two independent external-validation cohorts. Clinical variables, static multiphase radiomics, Delta vascular trajectory features, three-dimensional intratumoral heterogeneity (3D-ITH)/habitat features, and Vision Transformer (ViT) features were extracted. All preprocessing, model fitting, and threshold selection were confined to the development cohort. A capacity-constrained multimodal Transformer was compared with clinical, single-modality, simple concatenation, and tree-based models. Results: The cohort included 148 MVI-positive and 257 MVI-negative patients according to the locked blinded multireviewer reference standard. In pooled external validation, Transformer fusion achieved an area under the curve of 0.941 (95% CI, 0.906-0.969), sensitivity of 0.951, specificity of 0.754, accuracy of 0.831, negative predictive value of 0.960, and Brier score of 0.113. Corresponding AUCs were 0.957 and 0.934 in the two external cohorts. Delta-only and clinical models achieved pooled external AUCs of 0.793 and 0.675, respectively. High fusion-risk patients had poorer composite RFS/PFS than low-risk patients (hazard ratio, 2.05; 95% CI, 1.36-3.08; P < 0.001). Conclusion: The proposed dynamic vascular heterogeneity-centered framework demonstrated high external discrimination for preoperative MVI prediction in HCC and provided exploratory stratification of the available composite RFS/PFS endpoint. Prospective validation and recalibration are required before clinical implementation.

Indexed as

delta radiomicsexternal validationhabitat imagingmultimodal fusionrecurrence-free survivalvision transformer

Identifiers

PMID42712913
PMCPMC13550832

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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 OpenQuestion graph.