Literature DB >> 19913125

Can breast MRI computer-aided detection (CAD) improve radiologist accuracy for lesions detected at MRI screening and recommended for biopsy in a high-risk population?

T Arazi-Kleinman1, P A Causer, R A Jong, K Hill, E Warner.   

Abstract

AIM: To evaluate the sensitivity and specificity of magnetic resonance imaging (MRI) computer-aided detection (CAD) for breast MRI screen-detected lesions recommended for biopsy in a high-risk population.
MATERIAL AND METHODS: Fifty-six consecutive Breast Imaging Reporting and Data System (BI-RADS) 3-5 lesions with histopathological correlation [nine invasive cancers, 13 ductal carcinoma in situ (DCIS) and 34 benign] were retrospectively evaluated using a breast MRI CAD prototype (CAD-Gaea). CAD evaluation was performed separately and in consensus by two radiologists specializing in breast imaging, blinded to the histopathology. Thresholds of 50, 80, and 100% and delayed enhancement were independently assessed with CAD. Lesions were rated as malignant or benign according to threshold and delayed enhancement only and in combination. Sensitivities, specificities, and negative predictive values (NPV) were determined for CAD assessments versus pathology. Initial MRI BI-RADS interpretation without CAD versus CAD assessments were compared using paired binary diagnostic tests.
RESULTS: Threshold levels for lesion enhancement were: 50% to include all malignant (and all benign) lesions; and 100% for all invasive cancer and high-grade DCIS. Combined use of threshold and enhancement patterns for CAD assessment was best (73% sensitivity, 56% specificity and 76% NPV for all cancer). Sensitivities and NPV were better for invasive cancer (100%/100%) than for all malignancies (54%/76%). Radiologists' MRI interpretation was more sensitive than CAD (p=0.05), but less specific (p=0.001) for cancer detection.
CONCLUSION: The breast MRI CAD system used could not improve the radiologists' accuracy for distinguishing all malignant from benign lesions, due to the poor sensitivity for DCIS detection.

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Year:  2009        PMID: 19913125     DOI: 10.1016/j.crad.2009.08.003

Source DB:  PubMed          Journal:  Clin Radiol        ISSN: 0009-9260            Impact factor:   2.350


  8 in total

1.  Computer-aided diagnosis of rheumatoid arthritis with optical tomography, Part 1: feature extraction.

Authors:  Ludguier D Montejo; Jingfei Jia; Hyun K Kim; Uwe J Netz; Sabine Blaschke; Gerhard A Müller; Andreas H Hielscher
Journal:  J Biomed Opt       Date:  2013-07       Impact factor: 3.170

2.  Current Status and New Developments in Breast MRI.

Authors:  Katja C Siegmann; Bernhard Krämer; Claus Claussen
Journal:  Breast Care (Basel)       Date:  2011-04-29       Impact factor: 2.860

3.  Size assessment of breast lesions by means of a computer-aided detection (CAD) system for magnetic resonance mammography.

Authors:  G Levrini; R Sghedoni; C Mori; A Botti; R Vacondio; A Nitrosi; M Iori; F Nicoli
Journal:  Radiol Med       Date:  2011-03-19       Impact factor: 3.469

4.  Comparing performance of the CADstream and the DynaCAD breast MRI CAD systems : CADstream vs. DynaCAD in breast MRI.

Authors:  Joann Pan; Basak E Dogan; Selin Carkaci; Lumarie Santiago; Elsa Arribas; Scott B Cantor; Wei Wei; R Jason Stafford; Gary J Whitman
Journal:  J Digit Imaging       Date:  2013-10       Impact factor: 4.056

5.  Accuracy and interpretation time of computer-aided detection among novice and experienced breast MRI readers.

Authors:  Constance D Lehman; Jeffrey D Blume; Wendy B DeMartini; Nola M Hylton; Benjamin Herman; Mitchell D Schnall
Journal:  AJR Am J Roentgenol       Date:  2013-06       Impact factor: 3.959

6.  Computerized image analysis for identifying triple-negative breast cancers and differentiating them from other molecular subtypes of breast cancer on dynamic contrast-enhanced MR images: a feasibility study.

Authors:  Shannon C Agner; Mark A Rosen; Sarah Englander; John E Tomaszewski; Michael D Feldman; Paul Zhang; Carolyn Mies; Mitchell D Schnall; Anant Madabhushi
Journal:  Radiology       Date:  2014-03-10       Impact factor: 11.105

Review 7.  Computer-aided detection in breast MRI: a systematic review and meta-analysis.

Authors:  Monique D Dorrius; Marijke C Jansen-van der Weide; Peter M A van Ooijen; Ruud M Pijnappel; Matthijs Oudkerk
Journal:  Eur Radiol       Date:  2011-03-15       Impact factor: 5.315

8.  A Clinical Assessment of a Magnetic Resonance Computer-Aided Diagnosis System in the Detection of Pathological Complete Response After Neoadjuvant Chemotherapy in Breast Cancer.

Authors:  Haiyong Peng; Shaolei Yan; Xiaodan Chen; Jiahang Hu; Kaige Chen; Ping Wang; Hongxia Zhang; Xiushi Zhang; Wei Meng
Journal:  Front Oncol       Date:  2022-03-03       Impact factor: 6.244

  8 in total

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