Categorization of non-mass-like breast lesions detected by MRI

Sakamoto, Naomi; Tozaki, Mitsuhiro; Higa, Kuniki; Tsunoda, Yuko; Ogawa, Tomoko; Abe, Satoko; Ozaki, Shinji; Sakamoto, Masaaki; Tsuruhara, Tomoko; Kawano, Nobusuke; Suzuki, Takehiko; Yamashiro, Norie; Fukuma, Eisuke

doi:10.1007/s12282-007-0028-6

Cited by 66 publications

(61 citation statements)

References 18 publications

(24 reference statements)

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“…Liberman and associates 10 reported the highest frequency of carcinoma among the non-mass lesions with segmental enhancement (67z). On the other hand, Sakamoto and colleagues 14 reported no signiˆcant diŠerence between segmental distribution in benign (17z) and malignant lesions (30z), but signiˆcant diŠerence in branching-ductal pattern between benign (10z) and malignant lesions (50 z).…”

Section: Discussionmentioning

confidence: 95%

“…CRE, a clustering of minute ring enhancements, has also been reported to suggest malignancy: 44z vs. 0z 12 ; 63 z vs. 4z 13 ; 100z vs. 3z 14 ; and 74z vs. 0z 24 in malignant vs. benign lesions. CRE is believed to be the enhancement of ductal carcinoma and periductal stroma in DCIS, 12 but in the current series, we observed CRE in DCIS (1/3, 33.3z) and in IDC with some DCIS component (4/13, 30.8z).…”

Section: Discussionmentioning

confidence: 96%

“…1,2,4,9,10,13,14,17,18 In addition, no standardized method for interpreting the evaluation of non-mass-like enhancement has been established. [6][7][8]10,[12][13][14] Because many cases of DCIS or invasive ductal carcinoma with a predominantly intraductal component tend to show non-mass-like enhancement on MR imaging, diagnosis can be di‹cult. Previous studies investigated the diagnostic values of enhancing patterns on DCE-MRI or of kinetic curve analysis, but the diagnostic values of these methods are controversial.…”

Section: Discussionmentioning

confidence: 99%

“…Two studies showed CRE in benign lesions in a small number of cases (one of 26 cases and one of 83 cases). 13,14 The reason for this false-positive result has not been fully analyzed, but the pathological features corresponding to CRE, periductal stroma surrounding dilated ducts or cystic lesions, 12,13 may be seen in mastopathy.…”

Section: Discussionmentioning

confidence: 99%

“…[6][7][8][9][10][11] In contrast, such diŠerentiation is often di‹cult with nonmass-like enhancement because there is no standardized method of interpreting non-mass-like enhancement lesions. [6][7][8]10,[12][13][14] Several studies have reported the utility of evaluating apparent diŠusion coe‹cient (ADC) values derived from diŠusion-weighted imaging (DWI) for diŠerentiating benign and malignant lesions. [15][16][17][18][19][20] DWI obtains MR signal based on the thermal diŠu-sion of water molecules (Brownian motion), and the rate of water diŠusion is expressed as the ADC value.…”

Section: Introductionmentioning

confidence: 99%

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Diagnostic Performance of ADC for Non-mass-like Breast Lesions on MR Imaging

et al. 2010

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We assessed the usefulness and limitations of utilizing apparent diŠusion coe‹cient (ADC) values on diŠusion-weighted imaging (DWI) for the diŠerential diagnosis of benign and malignant non-mass-like breast lesions. We retrospectively reviewed 27 such lesions (16 malignant, 11 benign) detected on magnetic resonance (MR) imaging and analyzed the enhancing patterns of dynamic contrast-enhanced DCE-MRI (distribution and internal enhancement), kinetic curve patterns, and ADC values. All images were obtained with a 1.5-tesla MR unit, with patients supine. On DCE-MRI, malignant lesions tended to show either segmental or branching-ductal distribution, and when lesions with these patterns were considered malignant, sensitivity was 68.8z; speciˆcity, 63.6z; positive predictive value (PPV), 73.3z; negative predictive value (NPV), 58.3z; and accuracy, 66.7z. Kinetic curve analysis did not reliably diŠerentiate benign and malignant non-mass-like lesions. There was no signiˆcant diŠerence between the mean ADC value of the malignant lesions, 0.968×10 -3 mm 2 /s at b＝1000 s/mm 2 , and that of benign lesions, 1.207×10 -3 mm 2 /s (P ＝0.109). Receiver operating characteristic (ROC) analysis revealed the most eŠective threshold of ADC value for diŠerentiating tumors as 1.1×10 -3 mm 2 /s; values lower than this were observed more often in malignant than benign lesions (P＝0.054). Us of this threshold yielded sensitivity of 68.8z; speciˆcity, 72.7z; PPV, 78.6z; NPV, 61.5z; and accuracy, 70.4z. Combining the ADC value criteria with the analysis of DCE-MRI pattern increased sensitivity to 93.8z, negative predictive value (NPV) to 85.7z, and accuracy to 77.8z but decreased speciˆcity to 54.5z. Use of ADC values does not adequately improve DCE-MRI performance for diŠerential diagnosis of non-mass-like breast lesions, but adding the ADC value criteria to the DCE-MRI pattern analysis improves sensitivity, NPV, and accuracy.

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Section: Discussionmentioning

confidence: 95%

Section: Discussionmentioning

confidence: 96%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Diagnostic Performance of ADC for Non-mass-like Breast Lesions on MR Imaging

et al. 2010

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The diverse pathology and kinetics of mass, nonmass, and focus enhancement on MR imaging of the breast

Jansen

Shimauchi

Zak

et al. 2011

Magnetic Resonance Imaging

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Purpose To compare the pathology and kinetic characteristics of breast lesions with focus, mass and nonmass-like enhancement. Materials and Methods 852 MRI detected breast lesions in 697 patients were selected for an IRB approved review. Patients underwent dynamic contrast enhanced MRI using one pre and three to six post-contrast T1 weighted images. The ‘type’ of enhancement was classified as mass, non-mass or focus, and kinetic curves quantified by the initial enhancement percentage (E1), time to peak enhancement (Tpeak) and signal enhancement ratio (SER). These kinetic parameters were compared between malignant and benign lesions within each morphologic type. Results 552 lesions were classified as mass (396 malignant, 156 benign), 261 as nonmass (212 malignant,49 benign) and 39 as focus (9 malignant,30 benign). The most common pathology of malignant/benign lesions by morphology: for mass, invasive ductal carcinoma/fibroadenoma; for nonmass, ductal carcinoma in situ (DCIS)/fibrocystic change(FCC); for focus, DCIS/FCC. Benign mass lesions exhibited significantly lower E1, longer Tpeak and lower SER compared with malignant mass lesions (p < 0.0001). Benign nonmass lesions exhibited only a lower SER compared to malignant nonmass lesions (p<0.01). Conclusions By considering the diverse pathology and kinetic characteristics of different lesion morphologies, diagnostic accuracy may be improved.

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AI‐Enhanced Diagnosis of Challenging Lesions in Breast MRI: A Methodology and Application Primer

Meyer-Bäse

Morra

Tahmassebi

et al. 2020

Magnetic Resonance Imaging

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Computer‐aided diagnosis (CAD) systems have become an important tool in the assessment of breast tumors with magnetic resonance imaging (MRI). CAD systems can be used for the detection and diagnosis of breast tumors as a “second opinion” review complementing the radiologist's review. CAD systems have many common parts, such as image preprocessing, tumor feature extraction, and data classification that are mostly based on machine‐learning (ML) techniques. In this review article, we describe applications of ML‐based CAD systems in MRI covering the detection of diagnostically challenging lesions of the breast such as nonmass enhancing (NME) lesions, and furthermore discuss how multiparametric MRI and radiomics can be applied to the study of NME, including prediction of response to neoadjuvant chemotherapy (NAC). Since ML has been widely used in the medical imaging community, we provide an overview about the state‐of‐the‐art and novel techniques applied as classifiers to CAD systems. The differences in the CAD systems in MRI of the breast for several standard and novel applications for NME are explained in detail to provide important examples, illustrating: 1) CAD for detection and diagnosis, 2) CAD in multiparametric imaging, 3) CAD in NAC, and 4) breast cancer radiomics. We aim to provide a comparison between these CAD applications and to illustrate a global view on intelligent CAD systems based on machine and deep learning in MRI of the breast. Level of Evidence 2 Technical Efficacy Stage 2

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Categorization of non-mass-like breast lesions detected by MRI

Cited by 66 publications

References 18 publications

Diagnostic Performance of ADC for Non-mass-like Breast Lesions on MR Imaging

Diagnostic Performance of ADC for Non-mass-like Breast Lesions on MR Imaging

The diverse pathology and kinetics of mass, nonmass, and focus enhancement on MR imaging of the breast

AI‐Enhanced Diagnosis of Challenging Lesions in Breast MRI: A Methodology and Application Primer

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