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A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

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Pixel-level Radiomics and Deep Learning for Predicting Ki-67 Expression in Breast Cancer Based on Dual-modal

Wei Wei¹, Fei Xia², Di Zhang³

¹Department of Ultrasound, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, China (W.W., D.Z., W.Z., Y.G., W.L., C.X.Z.); Department of Ultrasound, the First Affiliated Hospital of Wannan Medical College (Yijishan Hospital), Wuhu, Anhui 241000, China (W.W., H.J.F.).

Academic Radiology

|January 15, 2026

Summary

This summary is machine-generated.

A new deep learning model, Vision-Mamba-US-RFMs-Clinical (V-MURC), accurately predicts Ki-67 expression in breast cancer using ultrasound images and radiomics. This tool aids in personalized breast cancer treatment decisions.

Keywords:

Breast cancer Deep learning Ki-67 expression Radiomics feature maps SHapley Additive exPlanations

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

Area of Science:

Radiology and Medical Imaging
Artificial Intelligence in Medicine
Oncology

Background:

Ki-67 expression is a key biomarker for breast cancer (BC) proliferation and treatment response.
Accurate prediction of Ki-67 is crucial for guiding personalized therapeutic strategies.
Current methods for Ki-67 assessment can be invasive and time-consuming.

Purpose of the Study:

To develop and validate a deep learning model for non-invasive prediction of Ki-67 expression in breast cancer.
To utilize a novel pixel-level radiomics approach integrating 2D and strain elastography ultrasound images.
To assess the clinical utility of the developed model for treatment decision-making.

Main Methods:

A multicenter study involving 1031 breast cancer patients.
Development of the Vision-Mamba deep learning model incorporating ultrasound images and pixel-level radiomics feature maps (RFMs).
Integration of clinical predictors and validation using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).

Main Results:

The Vision-Mamba-US-RFMs-Clinical (V-MURC) model demonstrated high predictive performance across internal, external, and prospective validation cohorts (AUCs > 0.94).
The V-MURC model significantly outperformed individual models, showing excellent discrimination and calibration.
SHapley Additive exPlanations (SHAP) analysis provided interpretability for the model's predictions.

Conclusions:

The V-MURC model, leveraging pixel-level RFMs from ultrasound, accurately predicts Ki-67 expression in breast cancer.
This AI-driven approach offers a valuable, non-invasive tool for individualized breast cancer treatment planning.
The model shows significant potential for clinical application in optimizing patient management.