Artificial intelligence can improve cancer detection in a double reading screening mammography scenario

Zhengqiang Jiang; Ziba Gandomkar; Phuong Dung (Yun) Trieu; Seyedamir Tavakoli Taba; Melissa L. Barron; Sarah J. Lewis

doi:10.1117/12.3006376

29 March 2024 Artificial intelligence can improve cancer detection in a double reading screening mammography scenario

Zhengqiang Jiang, Ziba Gandomkar, Phuong Dung (Yun) Trieu, Seyedamir Tavakoli Taba, Melissa L. Barron, Sarah J. Lewis

Author Affiliations +

Proceedings Volume 12929, Medical Imaging 2024: Image Perception, Observer Performance, and Technology Assessment; 1292903 (2024) https://doi.org/10.1117/12.3006376
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

This paper investigates whether two publicly available Artificial Intelligence (AI) models can detect retrospectively identified missed cancers within a double reader breast screening program and determine whether challenging mammographic cases are reflected in the performance of AI models. Transfer learning was conducted on the Globally-aware Multiple Instance Classifier (GMIC) and Global-Local Activation Maps (GLAM) models using an Australian mammographic dataset. Mammograms were enhanced to improve poor contrast using the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm. The sensitivity of the two AI models with pre-trained and transfer learning modes was evaluated on four mammographic case groups: ‘missed’ cancers, ‘prior-visible’ cancers, ‘prior-invisible’ cancers and ‘current’ cancers from the archives of a double reader breast screening program. The GMIC model outperformed the GLAM model with pre-trained and transfer learning modes in terms of sensitivity for all four cancer groups. The performance of the GMIC and GLAM models was best in ‘prior-visible’ cancers, followed by ‘prior-invisible’ cancers, ‘current’ cancers and ‘missed’ cancers. The performance of the GMIC and GLAM models on the ‘missed’ cancer cases was 84.2% and 81.5%, respectively while for the ‘prior-visible’ cancer cases, the performance was 92.7% and 89.2%, respectively. After transfer learning, both the GMIC and GLAM models demonstrated statistically significant improvement (>9.4%) in terms of sensitivity for all cancer groups. The AI models with transfer learning showed significant improvement in malignancy detection in challenging mammographic cases. The study also supports the potential of the AI models to identify missed cancers within a double reader breast screening program.

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(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Zhengqiang Jiang, Ziba Gandomkar, Phuong Dung (Yun) Trieu, Seyedamir Tavakoli Taba, Melissa L. Barron, and Sarah J. Lewis "Artificial intelligence can improve cancer detection in a double reading screening mammography scenario", Proc. SPIE 12929, Medical Imaging 2024: Image Perception, Observer Performance, and Technology Assessment, 1292903 (29 March 2024); https://doi.org/10.1117/12.3006376

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