Deep learning model for predicting the presence of stromal invasion of breast cancer on digital breast tomosynthesis

To develop a deep learning (DL)-based algorithm to predict the presence of stromal invasion in breast cancer using digital breast tomosynthesis (DBT). Our institutional review board approved this retrospective study and waived the requirement for informed consent from the patients. Initially, 499 pa...

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Bibliographic Details
Published in:Radiological physics and technology 2023-09, Vol.16 (3), p.406-413
Main Authors: Shimokawa, Daiki, Takahashi, Kengo, Oba, Ken, Takaya, Eichi, Usuzaki, Takuma, Kadowaki, Mizuki, Kawaguchi, Kurara, Adachi, Maki, Kaneno, Tomofumi, Fukuda, Toshinori, Yagishita, Kazuyo, Tsunoda, Hiroko, Ueda, Takuya
Format: Article
Language:English
Subjects:
Algorithms
Breast cancer
Deep learning
Imaging
Informed consent
Machine learning
Medical and Radiation Physics
Medicine
Medicine & Public Health
Nuclear Medicine
Radiology
Radiotherapy
Research Article
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Summary:To develop a deep learning (DL)-based algorithm to predict the presence of stromal invasion in breast cancer using digital breast tomosynthesis (DBT). Our institutional review board approved this retrospective study and waived the requirement for informed consent from the patients. Initially, 499 patients (mean age 50.5 years, age range, 29–90 years) who were referred to our hospital under the suspicion of breast cancer and who underwent DBT between March 1 and August 31, 2019, were enrolled in this study. Among the 499 patients, 140 who underwent surgery after being diagnosed with breast cancer were selected for the analysis. Based on the pathological reports, the 140 patients were classified into two groups: those with non-invasive cancer ( n  = 20) and those with invasive cancer ( n  = 120). VGG16, Resnet50, DenseNet121, and Xception architectures were used as DL models to differentiate non-invasive from invasive cancer. The diagnostic performance of the DL models was assessed based on the area under the receiver operating characteristic curve (AUC). The AUC for the four models were 0.56 [95% confidence intervals (95% CI) 0.49–0.62], 0.67 (95% CI 0.62–0.74), 0.71 (95% CI 0.65–0.75), and 0.75 (95% CI 0.69–0.81), respectively. Our proposed DL model trained on DBT images is useful for predicting the presence of stromal invasion in breast cancer.
ISSN:1865-0333
1865-0341
DOI:10.1007/s12194-023-00731-4