Develop a fully automated, objective method for evaluating morphology on breast MR and evaluate effectiveness of the new morphological method for detecting breast cancers

Ascher, Susan

Brem, Rachel

Conant, Emily

Lehman, Constance

Morris, Elizabeth

Newstead, Gillian

Penn, Alan

Pisano, Etta

Schnall, Mitchell

Thompson, Scott

Weatherall, Paul

PubMed

Rationale and Objectives: Develop a fully automated, objective method for evaluating morphology on breast magnetic resonance (MR) images and evaluate effectiveness of the new morphologic method for detecting breast cancers. Materials and Methods: We present a new automated method (morphologic blooming) for identifying and classifying breast lesions on MR that measures margin sharpness, a characteristic related to blooming, defined as rapid enhancement, with a border that is initially sharp but becomes unsharp after 7 minutes. Independent training sets (98 biopsy-proven lesions) and testing sets (179 breasts, 127 patients, acquired at five institutions) were used. Morphologic blooming was evaluated as a stand-alone feature and as an adjunct to kinetics using free-response receiver operating characteristic and sensitivity analysis. Dependence of false-positive (FP) rates on acquisition times and pathologies of contralateral breasts were evaluated. Results: Sensitivity of morphologic blooming was 80% with 2.46 FP per noncancerous breast: FPs did not vary significantly by acquisition times. FPs varied significantly by pathologies of contralateral breasts (cancerous contralateral: 4.29 FP/breast; noncancerous contralateral: 0.48 FP/breast; P \u3c .0001). Evaluation of 45 cancers showed suspicious morphologies on 10/15 (67%) cancers with benign-like kinetics and suspicious kinetics on 5/10 (50%) cancers with benign-like morphologies. Conclusion: We present a new, fully automated method of identifying and classifying margin sharpness of breast lesions on MR that can be used to direct radiologists\u27 attention to lesions with suspicious morphologies. Morphologic blooming may have important utility for assisting radiologists in identifying cancers with benign-like kinetics and discriminating normal tissues that exhibit cancer-like enhancement curves and for improving the performance of computer-aided detection systems. © 2006 AUR

George Washington University: Health Sciences Research Commons (HSRC)

Morphologic Blooming in Breast MRI as a Characterization of Margin for Discriminating Benign from Malignant Lesions

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https://cdr.lib.unc.edu/downloads/g445ck49q

Morphologic Blooming in Breast MRI as a Characterization of Margin for Discriminating Benign from Malignant Lesions

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