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Mammographically occult breast cancers detected with AI-based diagnosis supporting software: clinical and histopathologic characteristics

Hee Jeong Kim et al. — Insights into Imaging (2022)

Abstract


Background

To demonstrate the value of an artificial intelligence (AI) software in the detection of mammographically occult breast cancers and to determine the clinicopathologic patterns of the cancers additionally detected using the AI software.


Methods

By retrospectively reviewing our institutional database (January 2017–September 2019), we identified women with mammographically occult breast cancers and analyzed their mammography with an AI software that provided a malignancy score (range 0–100; > 10 considered as positive). The hot spots in the AI report were compared with the US and MRI findings to determine if the cancers were correctly marked by the AI software. The clinicopathologic characteristics of the AI-detected cancers were analyzed and compared with those of undetected cancers.


Results

Among the 1890 breast cancers, 6.8% (128/1890) were mammographically occult, among which 38.3% (49/128) had positive results in the AI analysis. Of them, 81.6% (40/49) were correctly marked by the AI software and determined as “AI-detected cancers.” As such, 31.3% (40/128) of mammographically occult breast cancers could be identified by the AI software. Of the AI-detected cancers, 97.5% were found in heterogeneously or extremely dense breasts, 52.5% were asymptomatic, 86.5% were invasive, and 29.7% had axillary lymph node metastasis. Compared with undetected cancers, the AI-detected cancers were more likely to be found in younger patients (p < 0.001), undergo neoadjuvant chemotherapy as well as mastectomy rather than breast-conserving operation (both p < 0.001), and accompany axillary lymph node metastasis (p = 0.003).


Conclusions

AI conferred an added value in the detection of mammographically occult breast cancers.

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AUTHORS

Hee Jeong Kim 1, Hak Hee Kim 2, Ki Hwan Kim 3, Woo Jung Choi 1, Eun Young Chae 1, Hee Jung Shin 1, Joo Hee Cha 1, Woo Hyun Shim 1

1Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, South Korea.

2Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, South Korea. hhkim@amc.seoul.kr.

3Lunit Inc., 15F, 27, Teheran-ro 2-gil, Gangnam-gu, Seoul, 06241, South Korea.

PUBLISHED
Insights into Imaging (2022)

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