Interpretation of Breast MRI Utilizing the BI-RADS Fifth Edition Lexicon: How Are We Doing and Where Are We Headed?

Shin, Kyung-Min; Phalak, Kanchan; Hamame, Anthony; Whitman, Gary J.

doi:10.1067/j.cpradiol.2015.12.001

Cited by 13 publications

(5 citation statements)

References 17 publications

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“…The images were assessed independently and any disagreements were resolved by achieving consensus. All lesions were assessed using The Breast Imaging Reporting and Data System (BI-RADS) ( 15 ). BI-RADS category 1 (negative) and category 2 (benign) denote an essentially 0% likelihood of cancer.…”

Section: Methodsmentioning

confidence: 99%

Discrimination of malignant and benign breast masses using automatic segmentation and features extracted from dynamic contrast‑enhanced and diffusion‑weighted MRI

et al. 2018

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Magnetic resonance imaging exhibits high sensitivity but low specificity for breast cancer. The present study aimed to investigate whether combining morphology, texture features and kinetic features with diffusion-weighted imaging using quantitative analysis improves the accuracy of discriminating malignant from benign breast masses. In total, 104 and 171 malignant lesions in 205 women were included. Additionally, 13 texture and 11 morphology features were computed from each lesion using a semi-automated segmentation method. To increase prediction accuracy, a newly designed classification model, difference-weighted local hyperplane, was used for statistical analysis of the combined effects of the features for predicting lesion type. The mean apparent diffusion coefficient (ADC) value for each lesion was calculated. Diagnostic performances of morphology and texture features, kinetic features and ADC alone and the combination of them were evaluated using receiver operating characteristics analysis. Malignant lesions had lower mean ADCs than benign lesions. By using 10-fold cross validation scheme, combined morphological and kinetic features achieved a diagnostic average accuracy of 0.87. Adding an ADC threshold of 1.37×10−3 mm2/sec increased the overall averaged accuracy to 0.90. A multivariate model combining ADC values with 6 morphological and kinetic parameters best discriminated malignant from benign lesions. Incorporating morphology and texture features, kinetic features and ADC into a multivariable diagnostic model improves the discriminatory power of breast lesions.

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Section: Methodsmentioning

confidence: 99%

Discrimination of malignant and benign breast masses using automatic segmentation and features extracted from dynamic contrast‑enhanced and diffusion‑weighted MRI

et al. 2018

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“…They were blinded to the patients’ information and unaware of histopathological diagnosis. All lesions were classified according to the Breast Imaging Reporting and Data Systems (BI-RADS) 5 th edition categories 21 , 22 .…”

Section: Methodsmentioning

confidence: 99%

“…In every case, a standard mediolateral oblique and craniocaudal view was made. The ACR breast density types of mammographic images was classified according to the BI-RADS ® lexicon 21 , 22 .…”

Section: Methodsmentioning

confidence: 99%

Radiological imaging characteristics of intramammary hematological malignancies: results from a german multicenter study

Wienbeck

Meyer

Uhlig

et al. 2017

Sci Rep

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To assess radiological procedures and imaging characteristics in patients with intramammary hematological malignancies (IHM). Radiological imaging studies of histopathological proven IHM cases from ten German University affiliated breast imaging centers from 1997-2012 were retrospectively evaluated. Imaging modalities included ultrasound (US), mammography and magnetic resonance imaging (MRI). Two radiologists blinded to the histopathological diagnoses independently assessed all imaging studies. Imaging studies of 101 patients with 204 intramammary lesions were included. Most patients were women (95%) with a median age of 64 years. IHM were classified as Non Hodgkin lymphoma (77.2%), plasmacytoma (11.9%), leukemia (9.9%), and Hodgkin lymphoma (1%). The mean lesion size was 15.8 ± 10.1 mm. Most IHM presented in mammography as lesions with comparable density to the surrounding tissue, and a round or irregular shape with indistinct margins. On US, most lesions were of irregular shape with complex echo pattern and indistinct margins. MRI shows lesions with irregular or spiculated margins and miscellaneous enhancement patterns. Using US or MRI, IHM were more frequently classified as BI-RADS 4 or 5 than using mammography (96.2% and 89.3% versus 75.3%). IHM can present with miscellaneous radiological patterns. Sensitivity for detection of IHM lesions was higher in US and MRI than in mammography.Intramammary hematological malignancies (IHM) have been described as a inhomogeneous group of breast malignancies, including various subtypes of malignant lymphomas, plasmacytomas and leukemias 1-3 . Overall, breast involvement in malignant hematological diseases is an uncommon manifestation and accounts for approximately 0.04-0.5% of all malignant breast cancers 4,5 . Of all extranodal malignant lymphomas, approximately 0.85-2.2% manifest as primary breast lymphomas 6-8 . Secondary breast involvement in a patient, who has a history of systemic malignant lymphoma, is more common [9][10][11] .Breast involvement by lymphoma can present as a primary breast tumor, or as an extranodal manifestation in systemic disease [4][5][6][7][8] .

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“…B. SNOMED-CT oder LOINC), stellt in diesem Zusammenhang bereits einen wesentlichen Schritt dar, Berichte maschinenverständlicher zu machen [12]. Mit jeder weiteren Strukturierung steigt der Nutzen, der aus der erfolgten Dokumentation gezogen werden kann, wie u. a. in der Radiologie am Beispiel der Mammabefundung mittels BI-RADS ® gezeigt werden konnte [13]. Autotext-Funktionen und vorformulierte Be-schreibungen dürfen daher nicht als unkreativ und stupide abgetan werden, sondern dienen, solange sie einem international akzeptierten Standard folgen, der Digitalisierung von medizinischen Prozessen.…”

Section: Digitalisierung In Der Chirurgieunclassified

Positionspapier „Digitalisierung in der Chirurgie“ – Konsequenzen?

et al. 2020

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in der Chirurgie", das im Auftrag der DGCH und in Mitwirkung durch zahlreiche Experten erstellt wird, beabsichtigt, einen Statusbericht über den aktuellen Transformationsprozess und die eingebundenen Technologien zu geben. Zugleich soll es aber dazu dienen, Handlungsempfehlungen zu generieren, die als Thesen bereits formuliert wurden. Die sich hieraus ableitenden Konsequenzen betreffen verschiedene Teilbereiche unseres Faches und sollen in diesem Manuskript erläutert werden.

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Interpretation of Breast MRI Utilizing the BI-RADS Fifth Edition Lexicon: How Are We Doing and Where Are We Headed?

Cited by 13 publications

References 17 publications

Discrimination of malignant and benign breast masses using automatic segmentation and features extracted from dynamic contrast‑enhanced and diffusion‑weighted MRI

Discrimination of malignant and benign breast masses using automatic segmentation and features extracted from dynamic contrast‑enhanced and diffusion‑weighted MRI

Radiological imaging characteristics of intramammary hematological malignancies: results from a german multicenter study

Positionspapier „Digitalisierung in der Chirurgie“ – Konsequenzen?

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