Skip to content
DraconiticResearch
Search Sources API
203,234 studies31 sourcesUpdated 27 min ago
Peer-reviewedCC BYFrontiers in oncology

Multiparametric MRI-based habitat and peritumoral radiomics integrating semi-automated segmentation for preoperative prediction of high-grade prostate cancer: a dual-center study

  • Danni Hua1
  • Luyang Ma2
  • Xiaodong Ji2
  • Lixiang Huang2
  • Yujiao Zhao2
  • Wen Shen2
  1. 1Department of Radiology, First Central Hospital of Tianjin Medical University , ,
  2. 2Department of Radiology, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin Institute of imaging medicine , ,
Read full textPDFDOICite

Abstract

Background: This study aimed to develop and validate an interpretable machine learning framework that combines semi-automated segmentation, intratumoral habitats derived from multiparametric MRI (mpMRI), and multiscale peritumoral radiomics to predict high-grade prostate cancer (HGPCa) before surgery.

Methods: This retrospective dual-center study involved 274 patients, comprising 208 from Center 1 for development and 66 from Center 2 for external test. The clinical and imaging variables included patient age, serum PSA, fPSA, PSAD, maximum tumor diameter, PI-RADS scores, and anatomical zone distribution (PZ vs. TZ). The development cohort was randomly divided into a training cohort (n = 145) and an internal validation cohort (n = 63). Intratumoral habitats were delineated using semi-automated segmentation and unsupervised K-means clustering, while peritumoral radiomic features were extracted from concentric 1-, 3-, and 5-mm shells. Predictive classifiers were optimized through machine learning algorithms, particularly a multilayer perceptron (MLP) for the habitat signature. A combined clinical-radiomics nomogram was constructed, with SHapley Additive exPlanations (SHAP) employed to enhance model interpretability. Subgroup analyses were performed to assess the discriminative performance of the combined nomogram across the peripheral zone (PZ) and transition zone (TZ).

Results: The combined nomogram exhibited superior diagnostic performance, achieving AUCs of 0.914, 0.903, and 0.797 in the training cohort, internal validation cohort, and external test cohort, respectively. The MLP-based habitat model showed good discriminative performance, with AUCs of 0.896, 0.897, and 0.782 across the training cohort, internal validation cohort, and external test cohort, respectively. Among the peritumoral regions, the 5-mm shell demonstrated the best discriminative performance. SHAP analysis identified Habitat 1 Shape Sphericity as the most influential feature. Subgroup analysis demonstrated the discriminatory ability of the combined nomogram in both the PZ (AUC: 0.796) and TZ (AUC: 0.753) subgroups.

Conclusion: The integration of semi-automated segmentation-based habitat analysis and peritumoral radiomics provides a promising approach for preoperative risk stratification of prostate cancer.

Related

  1. Multiparametric MRI radiomics model for preoperative prediction of IDH1 R132H immunopositivity status in adult diffuse Glioma
    Hung Duy Nguyen, Mai Huong Thi Le, Hue Thi Tran +5
    The Neuroradiology JournalPeer-reviewedLicence not statedFull textMedical imaging & radiologyPMC
  2. Preoperative prediction of HCC microvascular invasion by multimodel peritumoral diffusion MRI.
    Yunfei Zhang, Jiejun Chen, Xianling Qian +1
    European radiology experimentalPeer-reviewedCC BYFull textMedical imaging & radiologyEurope PMCDOAJ
  3. Multiparametric MRI with DWI, SWI, and DSC-PWI for preoperative prediction of Ki-67, IDH, and ATRX in adult-type diffuse gliomas.
    Muhammet Arif Kursun, Begumhan Baysal, Gulnihal Ay +2
    BMC medical imagingPeer-reviewedCC BY-NC-NDFull textHuman geneticsEurope PMC
  4. Multiparametric MRI radiomics and clinical features for preoperative prediction of high-grade meningioma: a two-centre retrospective study
    Lulu Gai, Yudong Yan, Wenzheng Lyu +3
    Frontiers in OncologyPeer-reviewedCC BY 4.0OncologyFrontiersCrossref
  5. Multiparametric MRI radiomics combined with clinical-radiologic features improves preoperative prediction of muscle invasion in bladder cancer
    Lei Qu, Zhihui Qu, Xuerong Ye +2
    Frontiers in OncologyPeer-reviewedCC BY 4.0Full textMedical imaging & radiologyCrossrefDOAJ

Cite

BibTeXRIS
BibTeX
@article{hua2026multiparametric,
  title = {{Multiparametric MRI-based habitat and peritumoral radiomics integrating semi-automated segmentation for preoperative prediction of high-grade prostate cancer: a dual-center study}},
  author = {Danni Hua and Luyang Ma and Xiaodong Ji and Lixiang Huang and Yujiao Zhao and Wen Shen},
  journal = {Frontiers in oncology},
  year = {2026},
  month = oct,
  doi = {10.3389/fonc.2026.1937448},
  url = {https://europepmc.org/article/PMC/PMC13671978},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Multiparametric MRI-based habitat and peritumoral radiomics integrating semi-automated segmentation for preoperative prediction of high-grade prostate cancer: a dual-center study
AU  - Danni Hua
AU  - Luyang Ma
AU  - Xiaodong Ji
AU  - Lixiang Huang
AU  - Yujiao Zhao
AU  - Wen Shen
PY  - 2026
DA  - 2026/10/02
JO  - Frontiers in oncology
DO  - 10.3389/fonc.2026.1937448
UR  - https://europepmc.org/article/PMC/PMC13671978
AB  - Background: This study aimed to develop and validate an interpretable machine learning framework that combines semi-automated segmentation, intratumoral habitats derived from multiparametric MRI (mpMRI), and multiscale peritumoral radiomics to predict high-grade prostate cancer (HGPCa) before surgery.  Methods: This retrospective dual-center study involved 274 patients, comprising 208 from Center 1 for development and 66 from Center 2 for external test. The clinical and imaging variables included patient age, serum PSA, fPSA, PSAD, maximum tumor diameter, PI-RADS scores, and anatomical zone distribution (PZ vs. TZ). The development cohort was randomly divided into a training cohort (n = 145) and an internal validation cohort (n = 63). Intratumoral habitats were delineated using semi-automated segmentation and unsupervised K-means clustering, while peritumoral radiomic features were extracted from concentric 1-, 3-, and 5-mm shells. Predictive classifiers were optimized through machine learning algorithms, particularly a multilayer perceptron (MLP) for the habitat signature. A combined clinical-radiomics nomogram was constructed, with SHapley Additive exPlanations (SHAP) employed to enhance model interpretability. Subgroup analyses were performed to assess the discriminative performance of the combined nomogram across the peripheral zone (PZ) and transition zone (TZ).  Results: The combined nomogram exhibited superior diagnostic performance, achieving AUCs of 0.914, 0.903, and 0.797 in the training cohort, internal validation cohort, and external test cohort, respectively. The MLP-based habitat model showed good discriminative performance, with AUCs of 0.896, 0.897, and 0.782 across the training cohort, internal validation cohort, and external test cohort, respectively. Among the peritumoral regions, the 5-mm shell demonstrated the best discriminative performance. SHAP analysis identified Habitat 1 Shape Sphericity as the most influential feature. Subgroup analysis demonstrated the discriminatory ability of the combined nomogram in both the PZ (AUC: 0.796) and TZ (AUC: 0.753) subgroups.  Conclusion: The integration of semi-automated segmentation-based habitat analysis and peritumoral radiomics provides a promising approach for preoperative risk stratification of prostate cancer.
SN  - 2234-943X
ER  -