Evaluation of Clinical Breast MR Imaging Performed with Prototype Computer-aided Diagnosis Breast MR Imaging Workstation: Reader Study

Shimauchi, Akiko; Giger, Maryellen L.; Bhooshan, Neha; Li, Lan; Pesce, Lorenzo L.; Lee, John K.; Abé, Hiroyuki; Newstead, Gillian M.

doi:10.1148/radiol.10100409

Cited by 39 publications

(29 citation statements)

References 29 publications

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“…Our findings are similar to those reported by Shimauchi et al [29]. In that study, 50 benign and 50 malignant lesions were reviewed with a specialized CAD program that provided six radiologists with a malignancy probability score.…”

Section: Discussionsupporting

confidence: 91%

Accuracy and Interpretation Time of Computer-Aided Detection Among Novice and Experienced Breast MRI Readers

Lehman

Blume

DeMartini

et al. 2013

American Journal of Roentgenology

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OBJECTIVE The purpose of this study was to compare the diagnostic accuracy and interpretation times of breast MRI with and without use of a computer-aided detection (CAD) system by novice and experienced readers. SUBJECTS AND METHODS A reader study was undertaken with 20 radiologists, nine experienced and 11 novice. Each radiologist participated in two reading sessions spaced 6 months apart that consisted of 70 cases (27 benign, 43 malignant), read with and without CAD assistance. Sensitivity, specificity, negative predictive value, positive predictive value, and overall accuracy as measured by the area under the receiver operating characteristic curve (AUC) were reported for each radiologist. Accuracy comparisons across use of CAD and experience level were examined. Time to interpret and report on each case was recorded. RESULTS CAD improved sensitivity for both experienced (AUC, 0.91 vs 0.84; 95% CI on the difference, 0.04, 0.11) and novice readers (AUC, 0.83 vs 0.77; 95% CI on the difference, 0.01, 0.10). The increase in sensitivity was statistically higher for experienced readers (p = 0.01). Diagnostic accuracy, measured by AUC, for novices without CAD was 0.77, for novices with CAD was 0.79, for experienced readers without CAD was 0.80, and for experienced readers with CAD was 0.83. An upward trend was noticed, but the differences were not statistically significant. There were no significant differences in interpretation times. COCLUSIONS MRI sensitivity improved with CAD for both experienced readers and novices with no overall increase in time to evaluate cases. However, overall accuracy was not significantly improved. As the use of breast MRI with CAD increases, more attention to the potential contributions of CAD to the diagnostic accuracy of MRI is needed.

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Section: Discussionsupporting

confidence: 91%

Accuracy and Interpretation Time of Computer-Aided Detection Among Novice and Experienced Breast MRI Readers

Lehman

Blume

DeMartini

et al. 2013

American Journal of Roentgenology

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“…Prior to the computer extraction of the various image phenotypes, the tumor was segmented on the MRI using the radiologist-indicated tumor center and a computational fuzzy c-means algorithm. 15 Quantitative radiomics analysis was then conducted, [16][17][18][19][20][21][22][23][24][25][26]27 yielding 38 radiomic features characterizing the size, shape, morphology, enhancement texture, kinetics, and variance kinetics of each tumor. These radiomic features can be sorted into six MRI phenotype categories: (1) size, giving the tumor dimensions, such as volume and surface area, (2) shape, characterizing the tumor geometry, such as sphericity and irregularity, (3) morphology, combining tumor shape and margin characteristics, such as spiculation and margin sharpness, (4) enhancement texture, characterizing tumor textural properties based on the gray-level co-occurrence matrix, such as energy, entropy, and contrast, (5) kinetic curve assessment, characterizing the physiological process of the uptake and washout nature of the contrast agent in a breast tumor during the dynamic imaging series, such as uptake rate, washout rate, and signal enhancement ratio, and (6) enhancement-variance kinetic features, characterizing the time course of the spatial variance of the enhancement within a breast tumor, such as variance increase rate and variance decrease rate.…”

Section: Imaging Data and Radiomic Featuresmentioning

confidence: 99%

Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data

et al. 2015

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Abstract. Genomic and radiomic imaging profiles of invasive breast carcinomas from The Cancer Genome Atlas and The Cancer Imaging Archive were integrated and a comprehensive analysis was conducted to predict clinical outcomes using the radiogenomic features. Variable selection via LASSO and logistic regression were used to select the most-predictive radiogenomic features for the clinical phenotypes, including pathological stage, lymph node metastasis, and status of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Cross-validation with receiver operating characteristic (ROC) analysis was performed and the area under the ROC curve (AUC) was employed as the prediction metric. Higher AUCs were obtained in the prediction of pathological stage, ER, and PR status than for lymph node metastasis and HER2 status. Overall, the prediction performances by genomics alone, radiomics alone, and combined radiogenomics features showed statistically significant correlations with clinical outcomes; however, improvement on the prediction performance by combining genomics and radiomics data was not found to be statistically significant, most likely due to the small sample size of 91 cancer cases with 38 radiomic features and 144 genomic features. © 2015 Society of Photo-Optical Instrumentation Engineers (SPIE)

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“…The use of more sophisticated QIA for breast MRI, however, remains an area of active research both for tumor classification [4], and for staging and prognosis [5]. In previous research studies, promising performance was obtained using image-based biomarkers for computer analysis of breast lesions in MRI, whereby the computer performed segmentation, extraction of morphologic and kinetic characteristics (features), and subsequent classification [6-9]. In this study, we investigated whether a QIA scheme utilizing a digital analysis of lymph nodes imaged on breast MRI is able to distinguish between lymph nodes that were positive for metastasis (‘positive’ nodes) and those that were negative for metastasis (‘negative’ nodes).…”

Section: Introductionmentioning

confidence: 99%

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

Schacht

Drukker

Pak

et al. 2015

European Journal of Radiology

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Purpose To assess the performance of computer extracted feature analysis of dynamic contrast enhanced (DCE) magnetic resonance images (MRI) of axillary lymph nodes. To determine which quantitative features best predict nodal metastasis. Methods This institutional board-approved HIPAA compliant study, in which informed patient consent was waived, collected enhanced T1 images of the axilla from patients with breast cancer. Lesion segmentation and feature analysis were performed on 192 nodes using a laboratory-developed quantitative image analysis (QIA) workstation. The importances of 28 features were assessed. Classification used the features as input to a neural net classifier in a leave-one-case-out cross-validation and evaluated with receiver operating characteristic (ROC) analysis. Results The area under the ROC curve (AUC) values for features in the task of distinguishing between positive and negative nodes ranged from just over 0.50 to 0.70. Five features yielded AUCs greater than 0.65: two morphological and three textural features. In cross-validation, the neural net classifier obtained an AUC of 0.88 (SE 0.03) for the task of distinguishing between positive and negative nodes. Conclusion QIA of DCE MRI demonstrated promising performance in discriminating between positive and negative axillary nodes.

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Evaluation of Clinical Breast MR Imaging Performed with Prototype Computer-aided Diagnosis Breast MR Imaging Workstation: Reader Study

Abstract: Use of the CADx system improved the radiologists' performance in differentiating between malignant and benign MR imaging-depicted breast lesions.

Cited by 39 publications

References 29 publications

Accuracy and Interpretation Time of Computer-Aided Detection Among Novice and Experienced Breast MRI Readers

Accuracy and Interpretation Time of Computer-Aided Detection Among Novice and Experienced Breast MRI Readers

Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data

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

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