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Related Experiment Video

Updated: Sep 10, 2025

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
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Differentiation of Suspicious Microcalcifications Using Deep Learning: DCIS or IDC.

Wenjie Xu1, Shuitang Deng1, Guoqun Mao1

  • 1Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China (W.X., S.D., G.M., C.Z.).

Academic Radiology
|August 20, 2025
PubMed
Summary

Deep learning effectively distinguishes ductal carcinoma in situ (DCIS) from invasive ductal carcinoma (IDC) using mammography microcalcifications. This AI approach offers a non-invasive method for accurate breast cancer diagnosis.

Keywords:
Breast cancerDeep learningMicocalcifications

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Medicine
  • Oncology

Background:

  • Differentiating ductal carcinoma in situ (DCIS) from invasive ductal carcinoma (IDC) is crucial for breast cancer treatment.
  • Mammographic microcalcifications present a diagnostic challenge in distinguishing these two conditions.

Purpose of the Study:

  • To evaluate the efficacy of a deep learning model in differentiating DCIS from IDC based on mammographic microcalcifications.
  • To compare the performance of a deep learning model against a clinical model and a combined model.

Main Methods:

  • A retrospective study involving 294 breast cancer cases (106 DCIS, 188 IDC) from two centers.
  • Development of a clinical model using logistic regression and a deep learning model using Resnet101 features.
  • Creation of a combined model integrating deep learning features and clinical variables.

Main Results:

  • The deep learning model achieved an AUC of 0.97, sensitivity of 0.94, and specificity of 0.92.
  • The combined model showed comparable performance with an AUC of 0.97, sensitivity of 0.96, and specificity of 0.92.
  • Both deep learning and combined models significantly outperformed the clinical model (AUC 0.67).

Conclusions:

  • Deep learning offers a powerful non-invasive tool for distinguishing DCIS from IDC with suspicious microcalcifications.
  • AI-driven analysis of mammographic features can significantly improve diagnostic accuracy in breast cancer.