Quantitative classification of invasive and noninvasive breast cancer using dynamic magnetic resonance imaging of the mammary gland

Author:

Miyazaki Yoshiaki1,Shimizu Juichiro2,Kubo Yuichiro3,Tabata Nobuyuki3,Aso Tomohiko1

Affiliation:

1. Department of Radiological Technology, National Cancer Center, Chuo-ku, Tokyo, Japan,

2. Department of Clinical Radiology, Faculty of Health Science, Hiroshima International University, Higashihiroshima, Hiroshima, Japan,

3. Department of Radiology, National Hospital Organization Kyushu Cancer Center, Minami-Ku, Fukuoka, Japan,

Abstract

Objectives Breast cancers are classified as invasive or noninvasive based on histopathological findings. Although time-intensity curve (TIC) analysis using magnetic resonance imaging (MRI) can differentiate benign from malignant disease, its diagnostic ability to quantitatively distinguish between invasive and noninvasive breast cancers has not been determined. In this study, we evaluated the ability of TIC analysis of dynamic MRI data (MRI-TIC) to distinguish between invasive and noninvasive breast cancers. Material and Methods We collected and analyzed data for 429 cases of epithelial invasive and noninvasive breast carcinomas. TIC features were extracted in washout areas suggestive of malignancy. Results The graph determining the positive diagnosis rate for invasive and noninvasive cases revealed that the cut-off θi/ni value was 21.6° (invasive: θw > 21.6°, noninvasive: θw ≤ 21.6°). Tissues were classified as invasive or noninvasive using this cut-off value, and each result was compared with the histopathological diagnosis. Using this method, the accuracy of tissue classification by MRI-TIC was 88.6% (380/429), which was higher than that using ultrasound (73.4%, 315/429). Conclusion MRI-TIC is effective for the classification of invasive vs. noninvasive breast cancer.

Publisher

Scientific Scholar

Subject

Radiology, Nuclear Medicine and imaging

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Biosimilars in Breast Cancer;Biosimilars for Cancer Treatment;2024

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