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Efficacy of Mammographic Artificial Intelligence-Based Computer-Aided Detection in Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy

  • 0Department of Radiology, Seoul Saint Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea.
Life (basel, Switzerland) +

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November 27, 2024

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November 27, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

This study shows that an AI-based computer-aided detection (AI-CAD) system can help predict pathologic complete response (pCR) in breast cancer patients after neoadjuvant chemotherapy (NAC). AI-CAD shows potential in digital mammography for assessing treatment effectiveness.

Area Of Science

  • Oncology
  • Radiology
  • Artificial Intelligence in Medicine

Background

  • Neoadjuvant chemotherapy (NAC) is a standard treatment for breast cancer.
  • Accurate prediction of pathologic complete response (pCR) after NAC is crucial for treatment planning.
  • Digital mammography plays a role in monitoring treatment response.

Purpose Of The Study

  • To evaluate the potential of an AI-based computer-aided detection (AI-CAD) system in digital mammography for predicting pCR in breast cancer patients post-NAC.
  • To compare the performance of AI-CAD with conventional CAD and other imaging modalities in predicting pCR.

Main Methods

  • Retrospective analysis of 132 breast cancer patients who received NAC.
  • Analysis of pre- and post-NAC digital mammograms using conventional CAD and AI-CAD.
  • Review of mammography, ultrasound, MRI, and diffusion-weighted imaging (DWI) by radiologists.
  • Assessment of diagnostic performance, including concordance rates and AUC.

Main Results

  • AI-CAD showed high pre-NAC concordance (97%) and comparable post-NAC concordance (89.4%) to conventional CAD.
  • MRI demonstrated the highest diagnostic performance for pCR prediction.
  • AI-CAD was identified as a significant predictor of pCR in univariate analysis.

Conclusions

  • AI-CAD in digital mammography shows promise for evaluating pCR in breast cancer patients after NAC.
  • While MRI remains a strong predictor, AI-CAD offers a valuable tool for assessing treatment response.
  • Further research can explore the integration of AI-CAD into routine clinical practice for breast cancer management.

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