Evidence map›Paper›PMID 42919290›Full record

ArticleJournal of translational medicine2026

Three-dimensional multimodal fractal dynamics integrating spatial metabolism, cytoarchitecture, and perfusion for preoperative prediction of microvascular invasion in colorectal liver metastases.

Ziyi Guo, Chao Guo, Zhengran Lu, Kailin Zou, Hongnan Zhang

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Article in Journal of translational medicine, 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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5 · Who and what money

Authors and funding

5 authors.

Ziyi GuoDepartment of Nuclear Medicine, First Affiliated Hospital of Jinzhou Medical University, Jinzhou, Liaoning, China.
Chao GuoArchitecture and Civil Engineering Institute, Guangdong University of Petrochemical Technology, Guandu Second Road, Maoming, 525000, China. guochao2024@gdupt.edu.cn.ORCID http://orcid.org/0000-0001-9389-731X
Zhengran LuArchitecture and Civil Engineering Institute, Guangdong University of Petrochemical Technology, Guandu Second Road, Maoming, 525000, China. luzhengran2024@gdupt.edu.cn.
Kailin ZouDepartment of Radiology, First Affiliated Hospital of Jinzhou Medical University, Jinzhou, Liaoning, China.
Hongnan ZhangDepartment of Radiology, First Affiliated Hospital of Jinzhou Medical University, Jinzhou, Liaoning, China.

Funding

Projects of Talents Recruitment of GDUPT 2024rcyj1055Projects of Talents Recruitment of GDUPT 2024rcyj1056
6 · The paper itself

Abstract

backgroundAccurately predicting microvascular invasion (MVI) preoperatively remains a core challenge for precision surgical decision-making in patients with colorectal liver metastases (CRLM). This study developed and validated a three-dimensional multimodal fractal dynamic model (FRAC-3D) that integrates spatial metabolism, cytoarchitecture, and blood perfusion, aiming to non-invasively identify MVI status preoperatively and evaluate patient prognosis.

methodsUtilizing a retrospective nested cohort design, stabilized inverse probability of treatment weighting (sIPTW) was applied to the derivation cohort (n = 100) for baseline debiasing. A core cohort of 145 patients with complete PET/CT, CE-MRI, and DWI imaging was ultimately included. Spatial metabolic fractal dimension (FD_PET), architectural fractal dimension (FD_ADC), and perfusion fractal acceleration (Afd_MRI) were extracted to construct the FRAC-3D model. The model's discrimination, calibration, and clinical benefit were validated in an independent temporal validation cohort (n = 45).

resultsMultivariable analysis confirmed that FD_ADC is the strongest independent predictor of MVI (OR = 3.85, 95% CI: 2.45-6.04, P < 0.001). The FRAC-3D model achieved an AUC of 0.938 and a negative predictive value of 96.2% in the validation cohort. Compared with the dual-modal model, FRAC-3D significantly improved risk restratification (continuous NRI = 0.425, P < 0.001). A "highly chaotic" imaging phenotype, identified based on fractal feature clustering, demonstrated an MVI incidence as high as 96.7% and a median recurrence-free survival (RFS) of only 5.2 months.

conclusionThrough the cross-validation of multidimensional biophysical features, the FRAC-3D model enables high-precision preoperative prediction of MVI status. This study advances the perioperative assessment of CRLM from traditional 'static anatomical metrics' into a novel dimension of 'microenvironmental dynamic evolution', providing a robust, physics-driven tool for individualized surgical margin planning and the precise stratification of targeted neoadjuvant therapy.

Indexed as

Colorectal NeoplasmsFractalsImaging, Three-DimensionalLiver NeoplasmsMicrovesselsMultimodal ImagingPreoperative CareAgedFemaleHumansMaleMiddle AgedNeoplasm InvasivenessPerfusionPreoperative PeriodPrognosisColorectal liver metastasesFractal dynamicsMicrovascular invasionMultimodal imagingPredictive modelTumor microenvironment

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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.