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A prediction model based on digital breast pathology image information.

Guoxin Sun¹, Liying Cai², Xiong Yan³

¹School of Clinical Medicine, Qingdao University, Qingdao, China.

|May 17, 2024

Summary

This study developed a nomogram model using pathological images to predict benign and malignant breast diseases. The model shows good predictive performance, aiding in breast cancer diagnosis.

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Area of Science:

Digital pathology
Medical imaging analysis
Oncology

Background:

Heavy workload in breast cancer pathological diagnosis.
Need for accurate and efficient diagnostic tools.
Development of image-based predictive models.

Purpose of the Study:

To establish a nomogram model for predicting benign and malignant breast diseases using pathological images.
To validate the predictive performance of the developed nomogram model.
To assist in the diagnosis of breast tissue pathological images.

Main Methods:

Collected 2,723 H&E-stained pathological images from 1,474 patients.
Extracted image features including R, G, B channels, and information entropy.
Utilized multivariable logistic regression to build the predictive model.

Main Results:

R channel value, B channel value, and information entropy were significant predictors (P < 0.05).
Nomogram model achieved AUC of 0.889 (training) and 0.838 (validation).
Calibration and decision curve analyses confirmed the model's diagnostic utility.

Conclusions:

The nomogram model demonstrates strong predictive performance for benign and malignant breast diseases.
This image-based model serves as a valuable tool for auxiliary diagnosis in pathology.
The findings support the integration of digital pathology tools in clinical practice.