Literature DB >> 21212365

Evaluation of clinical breast MR imaging performed with prototype computer-aided diagnosis breast MR imaging workstation: reader study.

Akiko Shimauchi1, Maryellen L Giger, Neha Bhooshan, Li Lan, Lorenzo L Pesce, John K Lee, Hiroyuki Abe, Gillian M Newstead.   

Abstract

PURPOSE: To evaluate a computer-aided diagnosis (CADx) system for dynamic contrast material-enhanced magnetic resonance (MR) imaging and compare it with a currently used clinical method of interpreting breast MR image findings that includes the use of commercially available automated software for kinetic image data processing and visualization.
MATERIALS AND METHODS: In this HIPAA-compliant, institutional review board-approved study, a training set of 121 breast lesions (77 malignant, 44 benign) was used to train the CADx system. After practicing with 10 training cases, six breast imaging radiologists assessed the likelihood of malignancy and the need for biopsy with a separate test set of 60 lesions (30 malignant, 30 benign). Their performances in differentiating between benign and malignant breast lesions both without (conventional lesion viewing, output from commercially available breast MR imaging analysis software) and with the aid of the CADx workstation (with classification yielding an estimation of the probability of malignancy for each lesion) were evaluated with receiver operating characteristic analysis.
RESULTS: When CADx was used, the average performance of the radiologists was significantly improved, as indicated by increases in mean area under the receiver operating characteristic curve (from 0.80 to 0.84, P = .007), mean sensitivity (from 83% to 88%, P = .001), and average number of biopsy recommendations for malignant cases (1.7 more biopsies for malignant lesions with use of CADx, P = .032). Although the mean specificity improved (from 50% to 53%), the improvement was not significant (P = .2).
CONCLUSION: Use of the CADx system improved the radiologists' performance in differentiating between malignant and benign MR imaging-depicted breast lesions. © RSNA, 2011.

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Year:  2011        PMID: 21212365     DOI: 10.1148/radiol.10100409

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  20 in total

1.  Local curvature analysis for classifying breast tumors: Preliminary analysis in dedicated breast CT.

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2.  Potential of computer-aided diagnosis of high spectral and spatial resolution (HiSS) MRI in the classification of breast lesions.

Authors:  Neha Bhooshan; Maryellen Giger; Milica Medved; Hui Li; Abbie Wood; Yading Yuan; Li Lan; Angelica Marquez; Greg Karczmar; Gillian Newstead
Journal:  J Magn Reson Imaging       Date:  2013-09-10       Impact factor: 4.813

3.  Combined Benefit of Quantitative Three-Compartment Breast Image Analysis and Mammography Radiomics in the Classification of Breast Masses in a Clinical Data Set.

Authors:  Karen Drukker; Maryellen L Giger; Bonnie N Joe; Karla Kerlikowske; Heather Greenwood; Jennifer S Drukteinis; Bethany Niell; Bo Fan; Serghei Malkov; Jesus Avila; Leila Kazemi; John Shepherd
Journal:  Radiology       Date:  2018-12-11       Impact factor: 11.105

4.  Fast Temporal Resolution Dynamic Contrast-Enhanced MRI: Histogram Analysis Versus Visual Analysis for Differentiating Benign and Malignant Breast Lesions.

Authors:  Naoko Mori; Federico D Pineda; Keiko Tsuchiya; Shunji Mugikura; Shoki Takahashi; Gregory S Karczmar; Hiroyuki Abe
Journal:  AJR Am J Roentgenol       Date:  2018-07-31       Impact factor: 3.959

5.  Classification of small lesions on dynamic breast MRI: Integrating dimension reduction and out-of-sample extension into CADx methodology.

Authors:  Mahesh B Nagarajan; Markus B Huber; Thomas Schlossbauer; Gerda Leinsinger; Andrzej Krol; Axel Wismüller
Journal:  Artif Intell Med       Date:  2013-11-23       Impact factor: 5.326

6.  Computerized three-class classification of MRI-based prognostic markers for breast cancer.

Authors:  Neha Bhooshan; Maryellen Giger; Darrin Edwards; Yading Yuan; Sanaz Jansen; Hui Li; Li Lan; Husain Sattar; Gillian Newstead
Journal:  Phys Med Biol       Date:  2011-08-22       Impact factor: 3.609

7.  Fuzzy c-means segmentation of major vessels in angiographic images of stroke.

Authors:  Christopher W Haddad; Karen Drukker; Rebecca Gullett; Timothy J Carroll; Gregory A Christoforidis; Maryellen L Giger
Journal:  J Med Imaging (Bellingham)       Date:  2018-01-04

8.  Optimal reconstruction and quantitative image features for computer-aided diagnosis tools for breast CT.

Authors:  Juhun Lee; Robert M Nishikawa; Ingrid Reiser; John M Boone
Journal:  Med Phys       Date:  2017-04-13       Impact factor: 4.071

Review 9.  Using quantitative image analysis to classify axillary lymph nodes on breast MRI: a new application for the Z 0011 Era.

Authors:  David V Schacht; Karen Drukker; Iris Pak; Hiroyuki Abe; Maryellen L Giger
Journal:  Eur J Radiol       Date:  2014-12-15       Impact factor: 3.528

10.  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

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