Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.
ADVANCED SEARCH
Log in
Top
Breast Cancer Research and Treatment
Published in: Breast Cancer Research and Treatment 2/2019

01-09-2019 | Magnetic Resonance Imaging | Clinical trial

Preliminary results of computer-aided diagnosis for magnetic resonance imaging of solid breast lesions

Authors: Qiujie Yu, Kuan Huang, Ye Zhu, Xiaodan Chen, Wei Meng

Published in: Breast Cancer Research and Treatment | Issue 2/2019

Login to get access

Abstract

Purpose

The present study aimed to determine suitable optimal classifiers and investigate the general applicability of computer-aided diagnosis (CAD) to compare magnetic resonance (MR)-CAD with MR imaging (MRI) in distinguishing benign from malignant solid breast masses.

Methods

We analyzed a total of 251 patients (mean age: 44.8 ± 12.3 years; range: 21–81 years) with 274 breast masses (154 benign masses, 120 malignant masses) using a Gaussian mixture model and a random forest machine model for segmentation and classification.

Results

The diagnostic performance of MRI alone and MRI plus CAD were compared with respect to sensitivity, specificity, and area under the curve (AUC), using receiver operating characteristic curve analysis. The discriminating power to detect malignancy using MR-CAD with an AUC of 0.955 (sensitivity was 95.8% and the specificity was 92.9%) was significantly higher than that of MRI alone with an AUC of 0.785 (sensitivity was 71.7% and the specificity was 85.7%).

Conclusion

CAD is feasible to differentiate breast lesions, and it can complement MRI, thereby making it easier to diagnose breast lesions and obviating the need for unnecessary biopsies.
Literature
1.
Mariscotti G, Houssami N, Durando M et al (2014) Accuracy of mammography, digital breast tomosynthesis, ultrasound and MR imaging in preoperative assessment of breast cancer. Anticancer Res 34:1219–1225PubMed
2.
Lee-Felker SA, Tekchandani L, Thomas M et al (2017) Newly diagnosed breast cancer: comparison of contrast-enhanced spectral mammography and breast MR imaging in the evaluation of extent of disease. Radiology 285:389–400CrossRefPubMed
3.
Warner E, Plewes DB, Hill KA et al (2004) Surveillance of BRCA1 and BRCA2 mutation carriers with magnetic resonance imaging, ultrasound, mammography, and clinical breast examination. JAMA 292:1317–1325CrossRefPubMed
4.
5.
Roganovec D, Djilas D, Bujnovic S et al (2015) Breast MRI, digital mammography and breast tomosynthesis: comparison of three methods for early detection of breast cancer. Born J Basic Med Sci 15:64–68
6.
Kanao S, Karaoka M, lima M et al (2018) Differentiating benign and malignant inflammatory breast lesions: value of T2 weighted and diffusion weighted MR images. Magn Reson Imaging 50:38–44CrossRefPubMed
7.
Jalalian A, Mashohor S, Mahmud R et al (2017) Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection. EXCLI J 20:113–137
8.
Honda E, Nakayama R, Toyama H et al (2016) Computer-aided diagnosis scheme for distinguishing between benign and malignant masses in breast DCE-MRI. J Digit Imaging 29:388–393CrossRefPubMed
9.
Song SE, Seo BK, Cho KR et al (2015) Computer-aided detection (CAD) system for breast MRI in assessment of local tumor extent, nodal status, and multifocality of invasive breast cancers: preliminary study. Cancer Imaging 8:1CrossRef
10.
Chaddad A, Luedi M, Zinn PO et al (2017) Corrigendum to “automated feature extraction in brain tumor by magnetic resonance imaging using gaussian mixture models”. Int J Biomed Imaging 2017:1CrossRef
11.
Banaie M, Soltanian-Zadeh H, Saligheh-Rad HR et al (2018) Spatiotemporal features of DCE-MRI for breast cancer diagnosis. Comput Methods Progr Biomed 155:153–164CrossRef
12.
Gallego-Ortiz C, Martel AL (2016) Improving the accuracy of computer-aided diagnosis for breast MR imaging by differentiating between mass and non mass lesions. Radiology 278:679–688CrossRefPubMed
13.
Gubern-Merida A, Marti R, Melendez J et al (2015) Automated localization of breast cancer in dynamic contrast-enhanced-MRI. Med Image Anal 20:265–274CrossRefPubMed
14.
Spainhour JC, Janech MG, Schwacke JH et al (2014) The application of Gaussian mixture models for signal quantification in MALDI-TOF mass spectrometry of peptides. PLoS ONE 9:e111016CrossRefPubMedPubMedCentral
15.
Gan H, Sang N, Huang R (2015) Manifold regularized semi-supervised gaussian mixture model. J Opt Soc Am A 32:566–575CrossRef
16.
Kawabata T (2018) Gaussian-input Gaussian mixture model for representing density maps and atomic models. J Struct Biol 203:1–16CrossRefPubMed
17.
Naidu BR, Babu MSP (2018) Biometric authentication data with three traits using compression technique, HOG, GMM and fusion technique. Data Brief 18:1976–1986CrossRefPubMedPubMedCentral
18.
19.
20.
Huang YH, Chang YC, Huang CS et al (2013) Computer-aided diagnosis of mass-like lesion in breast MRI: differential analysis of the 3-D morphology between benign and malignant tumors. Comput Methods Progr Biomed 112:508–517CrossRef
Metadata
Title
Preliminary results of computer-aided diagnosis for magnetic resonance imaging of solid breast lesions
Authors
Qiujie Yu
Kuan Huang
Ye Zhu
Xiaodan Chen
Wei Meng
Publication date
01-09-2019
Publisher
Springer US
Published in
Breast Cancer Research and Treatment / Issue 2/2019
Print ISSN: 0167-6806
Electronic ISSN: 1573-7217
DOI
https://doi.org/10.1007/s10549-019-05297-7

Other articles of this Issue 2/2019

Breast Cancer Research and Treatment 2/2019 Go to the issue

Preclinical study

Triple-negative breast cancer cell line sensitivity to englerin A identifies a new, targetable subtype

Clinical trial

Locoregional risk assessment after neoadjuvant chemotherapy in patients with primary breast cancer: clinical utility of the CPS + EG score

Clinical trial

Adherence to anti-estrogen therapy in women with hormone receptor-positive breast cancer utilizing bubble packaging: a pilot study

Epidemiology

Treatment and survival of Asian women diagnosed with breast cancer in New Zealand

Clinical trial

Effects of high-intensity interval training on vascular endothelial function and vascular wall thickness in breast cancer patients receiving anthracycline-based chemotherapy: a randomized pilot study

Review

HER2+ breast cancer treatment and cardiotoxicity: monitoring and management
Webinar | 19-02-2024 | 17:30 (CET)

Keynote webinar | Spotlight on antibody–drug conjugates in cancer

Watch now

Antibody–drug conjugates (ADCs) are novel agents that have shown promise across multiple tumor types. Explore the current landscape of ADCs in breast and lung cancer with our experts, and gain insights into the mechanism of action, key clinical trials data, existing challenges, and future directions.

Dr. Véronique Diéras
Prof. Fabrice Barlesi
Developed by: Springer Medicine
Image Credits