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Peer-reviewedCC BY-NC-NDBMC medical imaging

Ki-67/p53-associated angiogenic heterogeneity for preoperative WHO grading of meningiomas: a spatial habitat radiomics study.

  • Huanhuan Xie1
  • Xiting Peng2
  • Xingzhi Chen3
  • Yanting Yu1,4
  • Zhang Yi1
  • Wei Meng1
  • Chencui Huang3
  • Yi You3
  • Fei Dong1
  • Minming Zhang1
  • Xiaojun Guan5
  • Xiaojun Xu6
6 affiliations
  1. 1Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
  2. 2Department of Radiology, Taizhou Municipal Hospital, Taizhou, China.
  3. 3Deepwise AI Lab, Beijing Deepwise & League of PHD Technology Co., Ltd, Beijing, China.
  4. 4Department of Radiology, The Central Hospital Affiliated to Shaoxing University, Shaoxing Central Hospital), Shaoxing, China.
  5. 5Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China. [email protected].
  6. 6Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China. [email protected].
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Abstract

Purpose

This study aimed to develop and validate a classification model for preoperative prediction of WHO grading of meningioma based on spatial habitat radiomics that can be explained by Ki-67 and p53 biomarkers.Methods

A retrospective cohort of 400 patients (261 WHO grade I; 139 grade II/III) from a single institution was analyzed. Contrast-enhancing tumors were partitioned into three biologically distinct subregions (hypo-, intermediate-, and hyper-enhancement) using a 3D Simple Linear Iterative Clustering algorithm. Radiomics and clinical-radiological features were selected based on dual associations with Ki-67 ≥ 5% and p53 ≥ 5%. Subsequently, three classification models for preoperative prediction of WHO grading were constructed: clinical-radiological model (C-model), whole-tumor radiomics model (CRW-model), and subregion-based radiomics model (CRS-model). Performance was evaluated using AUC in training (n = 280) and test (n = 120) cohorts.Results

Spatial habitat radiomics demonstrated biomarker-specific predictive power: hyper-enhancement subregions achieved the highest Ki-67 prediction AUC (training: 0.81; test: 0.70), while hypo-enhancement subregions excelled in p53 prediction (training: 0.93; test: 0.57). The CRS-model, achieved the highest AUC in both the training cohort (0.89 vs. 0.80 for CRW-model and 0.76 for C-model) and the test cohort (0.76 vs. 0.68 and 0.67, respectively). In the training cohort, the CRS-model significantly outperformed both the CRW-model and C-model after Bonferroni correction (adjusted p = 0.000450 and 7.06 × 10⁻⁷, respectively). In the test cohort, the CRS-model showed numerically higher AUCs, but the differences were not statistically significant after correction (adjusted p = 0.0891 and 0.0918, respectively).Conclusions

Spatial habitat radiomics may help characterize intratumoral microenvironmental heterogeneity associated with Ki-67 and p53 expression, while providing a biologically interpretable framework for preoperative WHO grading of meningiomas.

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Cite

BibTeXRIS
BibTeX
@article{xie2026ki67p53associated,
  title = {{Ki-67/p53-associated angiogenic heterogeneity for preoperative WHO grading of meningiomas: a spatial habitat radiomics study.}},
  author = {Huanhuan Xie and Xiting Peng and Xingzhi Chen and Yanting Yu and Zhang Yi and Wei Meng and Chencui Huang and Yi You and Fei Dong and Minming Zhang and Xiaojun Guan and Xiaojun Xu},
  journal = {BMC medical imaging},
  year = {2026},
  month = aug,
  doi = {10.1186/s12880-026-02632-5},
  url = {https://europepmc.org/article/MED/42958086},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - Ki-67/p53-associated angiogenic heterogeneity for preoperative WHO grading of meningiomas: a spatial habitat radiomics study.
AU  - Huanhuan Xie
AU  - Xiting Peng
AU  - Xingzhi Chen
AU  - Yanting Yu
AU  - Zhang Yi
AU  - Wei Meng
AU  - Chencui Huang
AU  - Yi You
AU  - Fei Dong
AU  - Minming Zhang
AU  - Xiaojun Guan
AU  - Xiaojun Xu
PY  - 2026
DA  - 2026/08/03
JO  - BMC medical imaging
DO  - 10.1186/s12880-026-02632-5
UR  - https://europepmc.org/article/MED/42958086
AB  - Purpose  This study aimed to develop and validate a classification model for preoperative prediction of WHO grading of meningioma based on spatial habitat radiomics that can be explained by Ki-67 and p53 biomarkers.Methods  A retrospective cohort of 400 patients (261 WHO grade I; 139 grade II/III) from a single institution was analyzed. Contrast-enhancing tumors were partitioned into three biologically distinct subregions (hypo-, intermediate-, and hyper-enhancement) using a 3D Simple Linear Iterative Clustering algorithm. Radiomics and clinical-radiological features were selected based on dual associations with Ki-67 ≥ 5% and p53 ≥ 5%. Subsequently, three classification models for preoperative prediction of WHO grading were constructed: clinical-radiological model (C-model), whole-tumor radiomics model (CRW-model), and subregion-based radiomics model (CRS-model). Performance was evaluated using AUC in training (n = 280) and test (n = 120) cohorts.Results  Spatial habitat radiomics demonstrated biomarker-specific predictive power: hyper-enhancement subregions achieved the highest Ki-67 prediction AUC (training: 0.81; test: 0.70), while hypo-enhancement subregions excelled in p53 prediction (training: 0.93; test: 0.57). The CRS-model, achieved the highest AUC in both the training cohort (0.89 vs. 0.80 for CRW-model and 0.76 for C-model) and the test cohort (0.76 vs. 0.68 and 0.67, respectively). In the training cohort, the CRS-model significantly outperformed both the CRW-model and C-model after Bonferroni correction (adjusted p = 0.000450 and 7.06 × 10⁻⁷, respectively). In the test cohort, the CRS-model showed numerically higher AUCs, but the differences were not statistically significant after correction (adjusted p = 0.0891 and 0.0918, respectively).Conclusions  Spatial habitat radiomics may help characterize intratumoral microenvironmental heterogeneity associated with Ki-67 and p53 expression, while providing a biologically interpretable framework for preoperative WHO grading of meningiomas.
SN  - 1471-2342
ER  -