Deep learning-based computer-aided diagnosis in screening breast ultrasound to reduce false-positive diagnoses

Soo Yeon Kim, Yunhee Choi, Eun Kyung Kim, Boo Kyung Han, Jung Hyun Yoon, Ji Soo Choi, Jung Min Chang

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

A major limitation of screening breast ultrasound (US) is a substantial number of false-positive biopsy. This study aimed to develop a deep learning-based computer-aided diagnosis (DL-CAD)-based diagnostic model to improve the differential diagnosis of screening US-detected breast masses and reduce false-positive diagnoses. In this multicenter retrospective study, a diagnostic model was developed based on US images combined with information obtained from the DL-CAD software for patients with breast masses detected using screening US; the data were obtained from two hospitals (development set: 299 imaging studies in 2015). Quantitative morphologic features were obtained from the DL-CAD software, and the clinical findings were collected. Multivariable logistic regression analysis was performed to establish a DL-CAD-based nomogram, and the model was externally validated using data collected from 164 imaging studies conducted between 2018 and 2019 at another hospital. Among the quantitative morphologic features extracted from DL-CAD, a higher irregular shape score (P =.018) and lower parallel orientation score (P =.007) were associated with malignancy. The nomogram incorporating the DL-CAD-based quantitative features, radiologists’ Breast Imaging Reporting and Data Systems (BI-RADS) final assessment (P =.014), and patient age (P <.001) exhibited good discrimination in both the development and validation cohorts (area under the receiver operating characteristic curve, 0.89 and 0.87). Compared with the radiologists’ BI-RADS final assessment, the DL-CAD-based nomogram lowered the false-positive rate (68% vs. 31%, P <.001 in the development cohort; 97% vs. 45% P <.001 in the validation cohort) without affecting the sensitivity (98% vs. 93%, P =.317 in the development cohort; each 100% in the validation cohort). In conclusion, the proposed model showed good performance for differentiating screening US-detected breast masses, thus demonstrating a potential to reduce unnecessary biopsies.

Original languageEnglish
Article number395
JournalScientific Reports
Volume11
Issue number1
DOIs
StatePublished - Dec 2021

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