Meta-Analysis of MR Imaging in the Diagnosis of Breast Lesions

Peters, Niek A.; Rinkes, Inne H.M. Borel; Zuithoff, Nicolaas P. A.; Mali, Willem P.Th.M.; Moons, Karel G.M.; Peeters, Petra H.M.

doi:10.1148/radiol.2461061298

Cited by 471 publications

(293 citation statements)

References 76 publications

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“…Using the QDA based on the change in signal intensity from 0 to 2 min and With our CAD scheme, the classification accuracy, sensitivity, and specificity were 85.6 %, 87.1 %, and 82.1 %, respectively. These results were comparable to the performance of radiologists in the context of differential diagnosis in DCE-MRI examinations [5][6][7][8][9][10]. Although the classification accuracy of our CAD scheme might have to be further improved in order to aid experienced radiologists, it could nevertheless be useful for less experienced radiologists.…”

Section: Discussionsupporting

confidence: 67%

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Computer-Aided Diagnosis Scheme for Distinguishing Between Benign and Malignant Masses in Breast DCE-MRI

et al. 2015

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Our purpose in this study was to develop a computer-aided diagnosis (CAD) scheme for distinguishing between benign and malignant breast masses in dynamic contrast material-enhanced magnetic resonance imaging (DCE-MRI). Our database consisted 90 DCE-MRI examinations, each of which contained four sequential phase images; this database included 28 benign masses and 62 malignant masses. In our CAD scheme, we first determined 11 objective features of masses by taking into account the image features and the dynamic changes in signal intensity that experienced radiologists commonly use for describing masses in DCE-MRI. Quadratic discriminant analysis (QDA) was employed to distinguish between benign and malignant masses. As the input of the QDA, a combination of four objective features was determined among the 11 objective features according to a stepwise method. These objective features were as follows: (i) the change in signal intensity from 2 to 5 min; (ii) the change in signal intensity from 0 to 2 min; (iii) the irregularity of the shape; and (iv) the smoothness of the margin. Using this approach, the classification accuracy, sensitivity, and specificity were shown to be 85.6 % (77 of 90), 87.1 % (54 of 62), and 82.1 % (23 of 28), respectively. Furthermore, the positive and negative predictive values were 91.5 % (54 of 59) and 74.2 % (23 of 31), respectively. Our CAD scheme therefore exhibits high classification accuracy and is useful in the differential diagnosis of masses in DCE-MRI images.

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Section: Discussionsupporting

confidence: 67%

“…The specificity of breast DCE-MRI, which is typically between 67 and 72 %, is lower than that of mammography. Such a low specificity results in a larger number of false positives and higher biopsy rates [8][9][10]. Furthermore, DCE-MRI requires a significant amount of time for image interpretation.…”

Section: Introductionmentioning

confidence: 99%

Computer-Aided Diagnosis Scheme for Distinguishing Between Benign and Malignant Masses in Breast DCE-MRI

et al. 2015

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“…Even if reported to be useful in the detection of breast carcinoma [7,8], CT has the drawback of high radiation dose levels. Contrast-enhanced MRI seems to be currently the most sensitive breast cancer detection technique [9], but have high false positive rate and still carries the burden of higher costs and lower availability.…”

Section: Introductionmentioning

confidence: 99%

Dual-energy contrast-enhanced digital mammography: initial clinical results

Dromain

Thibault

Muller³

et al. 2010

Eur Radiol

242

158

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Objective To assess the diagnostic accuracy of Dual-Energy Contrast-Enhanced Digital Mammography (CEDM) as an adjunct to mammography (MX) versus MX alone and versus mammography plus ultrasound (US). Materials and methods 120 women with 142 suspect findings on MX and/or US underwent CEDM. A pair of low-and high-energy images was acquired using a modified full-field digital mammography system. Exposures were taken in MLO at 2 min and in CC at 4 min after the injection of 1.5 ml/kg of an iodinated contrast agent. One reader evaluated MX, US and CEDM images during 2 sessions 1 month apart. Sensitivity, specificity, and area under the ROC curve were estimated. Results The results from pathology and follow-up identified 62 benign and 80 malignant lesions. Areas under the ROC curves were significantly superior for MX+CEDM than it was for MX alone and for MX+US using BI-RADS. Sensitivity was higher for MX+CEDM than it was for MX (93% vs. 78%; p<0.001) with no loss in specificity. The lesion size was closer to the histological size for CEDM. All 23 multifocal lesions were correctly detected by MX+CEDM vs. 16 and 15 lesions by MX and US respectively. Conclusion Initial clinical results show that CEDM has better diagnostic accuracy than mammography alone and mammography+ultrasound.

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“…In the last two decades, several experimental studies have demonstrated that quantitative methods (based on tracer kinetics modelling) can be more specific in distinguishing benign from malignant breast disease, because of the capability to derive parameters strictly related to tissue microvasculature without any operator dependency [19,[20][21][22][23][24][28][29][30][31][32][33][34][35][36][37][38][39][40][41]. However, as there is not yet sufficient standardisation of quantitative methods, semi-quantitative approaches have been used because they could represent a compromise between qualitative and quantitative approaches.…”

Section: Discussionmentioning

confidence: 99%

Can semi-quantitative evaluation of uncertain (type II) time-intensity curves improve diagnosis in breast DCE-MRI?

Fusco¹,

Filice²,

Granata³

et al. 2013

JBiSE

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Objective/Background: Qualitative assessment of uncertain (type II) time-intensity curves (TICs) in breast DCE-MRI is problematic and operator dependent. The aim of this work is to evaluate if a semi-quantitative assessment of uncertain TICs could improve overall diagnostic performance. Methods: In this study 49 lesions from 44 patients were retrospectively analysed. Per each lesion one region-of-interest (ROI)-averaged TIC was qualitatively evaluated by two radiologists in consensus: all the ROIs resulted in type II (uncertain) TIC. The same TICs were semi-quantitatively re-classified on the basis of the difference between the signal intensities of the last-time-point and of the peak: this difference was classified according to two different cut-off ranges (±5% and ±3%). All patients were cytological or histological biopsy proven. Fisher test and McNemar test were performed to evaluate if results were statistically significant (p < 0.05). Results: Using ±5% cut-off 16 TICs were reclassified as type III and 12 as type I while 21 were reclassified again as type II. Using ±3% 22 TICs were reclassified as type III and 16 as type I while 11 were reclassified again as type II. The semi-quantitative classification was compared to the histologicalcytological results: the sensitivity, specificity, positive and negative predictive values obtained with ±3% were 77%, 91%, 91% and 78% respectively while using ±5% were 58%, 96%, 94% and 68% respectively. Using the ±5% cut-off 26/28 (93%) TICs were correctly reclassified while using the ±3% cut-off 34/38 (90%) TICs were correctly reclassified (p < 0.05). Conclusions: Semi-quantitative methods in kinetic curve assessment on DCE-MRI could improve classification of qualitatively uncertain TICs, leading to a more accurate classification of suspicious breast lesions.

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Meta-Analysis of MR Imaging in the Diagnosis of Breast Lesions

Abstract: http://radiology.rsnajnls.org/cgi/content/full/2461061298/DC1.

Cited by 471 publications

References 76 publications

Computer-Aided Diagnosis Scheme for Distinguishing Between Benign and Malignant Masses in Breast DCE-MRI

Computer-Aided Diagnosis Scheme for Distinguishing Between Benign and Malignant Masses in Breast DCE-MRI

Dual-energy contrast-enhanced digital mammography: initial clinical results

Can semi-quantitative evaluation of uncertain (type II) time-intensity curves improve diagnosis in breast DCE-MRI?

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