Prediction of Ki-67 Status in Breast Cancer Using a Habitat-Guided 2.5D Multiparametric MRI Deep Learning Model.

Authors

Zeyang Miao, Run Xu, Mengyao Guo, Hong Chen, Xuting Fang, Qiong Li, Peng Luo, Guanwu Li

Published in

Journal of computer assisted tomography. Sep 01, 2026. Epub Sep 01, 2026.

Abstract

Intratumoral heterogeneity may limit the representativeness of biopsy-based Ki-67 assessment in breast cancer. We therefore developed and validated a habitat-guided 2.5D deep learning (DL) model based on multiparametric MRI for noninvasive preoperative prediction of high versus low Ki-67 expression.
This retrospective study enrolled 333 patients with invasive breast carcinoma from 2 distinct MRI vendor cohorts (Siemens, training set, n=233; United Imaging, independent test set, n=100). All patients underwent preoperative multiparametric MRI, including DCE-MRI and DWI. Hemodynamic parametric maps (wash-in, wash-out) and ADC maps were generated and subsequently clustered using a K-means algorithm (k=3) to create a functional habitat mask that quantitatively encodes intratumoral heterogeneity. A 7-channel 2.5D input tensor was then constructed by concatenating the central habitat-guided slice with its 6 adjacent anatomic slices. A ResNet18 backbone was trained to classify high (≥20%) versus low Ki-67 expression. The model's performance was rigorously evaluated against conventional 2D DL, clinical, and combined (DL+clinical) models using AUC, the DeLong test, and decision curve analysis (DCA).
In the challenging independent cross-vendor test set, our habitat-guided DL25D model demonstrated superior performance, achieving an AUC of 0.821 (95% CI: 0.736-0.906) and a sensitivity of 0.804. It significantly outperformed both the conventional DL2D model (AUC: 0.654, P=0.002) and the clinical model (AUC: 0.686, P=0.019). The incorporation of clinical variables failed to yield further improvement (combined model AUC: 0.837, P=0.483 vs. DL25D; NRI=0.021, P>0.05). DCA confirmed the superior net clinical benefit of our approach across a wide spectrum of threshold probabilities. Importantly, Grad-CAM visualizations revealed that the habitat-guided model strategically focused its attention on intratumoral core regions, whereas the conventional 2D model was distracted by tumor margins and background tissue.
The habitat-guided 2.5D deep learning model showed potential as a noninvasive imaging adjunct for preoperative Ki-67 status prediction in breast cancer. Multicenter prospective validation is required before clinical use.

PMID:
42677973
Bibliographic data and abstract were imported from PubMed on 01 Sep 2026.

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