Three-dimensional multimodal fractal dynamics integrating spatial metabolism, cytoarchitecture, and perfusion for preoperative prediction of microvascular invasion in colorectal liver metastases
- 1Department of Nuclear Medicine, First Affiliated Hospital of Jinzhou Medical University
- 2Architecture and Civil Engineering Institute, Guangdong University of Petrochemical Technology
- 3Department of Radiology, First Affiliated Hospital of Jinzhou Medical University
Abstract
Background Accurately 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. Methods Utilizing 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). Results Multivariable 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. Conclusion Through 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.
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BibTeX
@article{guo2026threedimensional,
title = {{Three-dimensional multimodal fractal dynamics integrating spatial metabolism, cytoarchitecture, and perfusion for preoperative prediction of microvascular invasion in colorectal liver metastases}},
author = {Ziyi Guo and Chao Guo and Zhengran Lu and Kailin Zou and Hongnan Zhang},
journal = {Journal of Translational Medicine},
year = {2026},
month = jul,
doi = {10.1186/s12967-026-08712-y},
url = {https://doi.org/10.1186/s12967-026-08712-y},
note = {CC BY},
}
RIS
TY - JOUR
TI - Three-dimensional multimodal fractal dynamics integrating spatial metabolism, cytoarchitecture, and perfusion for preoperative prediction of microvascular invasion in colorectal liver metastases
AU - Ziyi Guo
AU - Chao Guo
AU - Zhengran Lu
AU - Kailin Zou
AU - Hongnan Zhang
PY - 2026
DA - 2026/07/01
JO - Journal of Translational Medicine
DO - 10.1186/s12967-026-08712-y
UR - https://doi.org/10.1186/s12967-026-08712-y
AB - Background Accurately 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. Methods Utilizing 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). Results Multivariable 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. Conclusion Through 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.
SN - 1479-5876
ER -