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Multicenter Study
. 2025 Oct;32(10):5975-5986.
doi: 10.1016/j.acra.2025.07.056. Epub 2025 Aug 13.

A Multiparametric MRI-based Model for decoding Extraprostatic Extension in Prostate Cancer via Habitat-guided Radiomics and Clinical Integration

Ye Xiang  1 , Hongchao Yao  2 , Ping Lin  3 , Peixi Hu  2 , Jingguo Li  2 , Fengjun Dong  2 , Ping Yang  2 , Ziyun Tang  2 , Bowen Tian  2 , Jin-Ming Cao  4 , Xu Feng  5 , Fan Li  6
Affiliations
Multicenter Study

A Multiparametric MRI-based Model for decoding Extraprostatic Extension in Prostate Cancer via Habitat-guided Radiomics and Clinical Integration

Ye Xiang et al. Acad Radiol. 2025 Oct.

Abstract

Rationale and objectives: Develop and validate a multiparametric MRI-based integrative model that combines intratumoral microenvironmental heterogeneity, peritumoral radiomic features, and clinicopathological variables for the noninvasive preoperative prediction of extraprostatic extension (EPE) in prostate cancer, aiming to support surgical decision-making and reduce postoperative biochemical recurrence.

Methods: MRI and clinicopathological data from 590 prostate cancer (PCa) patients across four centers (August 2013-September 2023) were retrospectively collected and divided into a training cohort (n = 249), internal validation cohort (n = 106), and two external test cohorts (n₁ = 199, n₂ = 36). Radiomic and habitat features were extracted from T2WI, DWI, ADC, and DCE MRI sequences. Six models were constructed: Clinical, Radiomics, Peri2mm, Peri4mm, Peri6mm, and Habitat. These were subsequently integrated into a fusion model (Habitat + Peri6mm + Clinical). Model performance was evaluated using ROC analysis, DCA, calibration curve, DeLong's test, nomogram and SHAP interpretation.

Results: The habitat model outperformed all unimodal models, achieving AUCs of 0.978 (training), 0.893 (validation), 0.832 and 0.836 (external test sets). Among radiomics-based models, the Peri6mm model showed the highest performance, with AUCs ranging from 0.949 to 0.752. The fusion model achieved the best overall predictive performance, with AUCs of 0.982 (training), 0.921 (validation), and 0.853 and 0.839 (external).

Conclusion: Habitat and peritumoral radiomic features were independently predictive of EPE. The fusion model, integrating clinical, peritumoral, and habitat-derived features, further enhanced preoperative predictive accuracy.

Keywords: Extraprostatic extension(EPE); Intratumoral heterogeneity; Magnetic resonance imaging (MRI); Peritumoral radiomics; Prostatic cancer.

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Conflict of interest statement

Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Reports a relationship with that includes:. Has patent pending to. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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