Retrospective Review of Missed Cancer Detection and Its Mammography Findings with Artificial-Intelligence-Based, Computer-Aided Diagnosis.

Ga Eun Park,Bong Joo Kang,Jeongmin Lee,Sung Hun Kim

doi:10.3390/diagnostics12020387

Ga Eun Park, Bong Joo Kang + Show 2 more

Open Access

https://doi.org/10.3390/diagnostics12020387

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Abstract

To investigate whether artificial-intelligence-based, computer-aided diagnosis (AI-CAD) could facilitate the detection of missed cancer on digital mammography, a total of 204 women diagnosed with breast cancer with diagnostic (present) and prior mammograms between 2018 and 2020 were included in this study. Two breast radiologists reviewed the mammographic features and classified them into true negative, minimal sign or missed cancer. They analyzed the AI-CAD results with an abnormality score and assessed whether the AI-CAD correctly localized the known cancer sites. Of the 204 cases, 137 were classified as true negative, 33 as minimal signs, and 34 as missed cancer. The sensitivity, specificity and diagnostic accuracy of AI-CAD were 84.7%, 91.5% and 86.3% on diagnostic mammogram and 67.2%, 91.2% and 83.38% on prior mammogram, respectively. The AI-CAD correctly localized 27 cases from 34 missed cancers on prior mammograms. The findings in the preceding mammography of AI-CAD-detected missed cancer were common in the order of calcifications, focal asymmetry and asymmetry. Asymmetry was the most common finding among the seven cases, which could not be detected by AI-CAD in the missed cases (5/7). The assistance of AI-CAD can be helpful in the early detection of breast cancer in mammography screenings.

Highlights

Mammography is proven to be an effective method for reducing the mortality of breast cancer [1]
157 cases were mammography-visible on diagnostic mammograms, and 67 cases were visible on prior mammograms
The aim of this retrospective study was to assess the potential of using AI-computer-aided detection (CAD) to improve the detection of missed cancer in mammography screenings

Summary

Introduction

Mammography is proven to be an effective method for reducing the mortality of breast cancer [1]. Factors that contribute to lowering the sensitivity of mammography are dense breast parenchyma, rapid tumor growth rate, and the finding and reading of subtle errors (perceptual or interpretive). Studies have shown that approximately one-third of newly diagnosed breast cancers were retrospectively visible in prior mammograms [2,3]. Missed cancer refers to cancer that can be retrospectively visualized in preceding mammograms that were initially interpreted as negative.

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