Small Masses on Breast MR

Raza, Sughra; Sekar, Meera; Ong, Eugene M. W.; Birdwell, Robyn L.

doi:10.1016/j.acra.2011.12.014

Cited by 23 publications

(8 citation statements)

References 19 publications

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“…Meissnitzer et al [ 13 ] showed that sub-centimeter invasive breast cancers often present with benign morphologic features such as persistent enhancement (30%) and high T2 signal (17%). Raza et al [ 20 ] demonstrated that breast cancers smaller than 5 mm tend to present with circumscribed margins (71%), benign shape (67%), and benign kinetic characteristics (41%). The presence of a BRCA mutation is an additional confounding factor as breast cancers in this population often present with benign morphologic features (e.g., oval shape and well-defined margins) on MRI and can resemble a fibroadenoma or a cyst in 23–38% of cases [ 12 , 20 ].…”

Section: Discussionmentioning

confidence: 99%

“…Raza et al [ 20 ] demonstrated that breast cancers smaller than 5 mm tend to present with circumscribed margins (71%), benign shape (67%), and benign kinetic characteristics (41%). The presence of a BRCA mutation is an additional confounding factor as breast cancers in this population often present with benign morphologic features (e.g., oval shape and well-defined margins) on MRI and can resemble a fibroadenoma or a cyst in 23–38% of cases [ 12 , 20 ]. Yet, these cancers are more aggressive with fast growth rates and a short lead time [ 20 ].…”

Section: Discussionmentioning

confidence: 99%

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Improved characterization of sub-centimeter enhancing breast masses on MRI with radiomics and machine learning in BRCA mutation carriers

et al. 2020

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Objectives To investigate whether radiomics features extracted from MRI of BRCA-positive patients with sub-centimeter breast masses can be coupled with machine learning to differentiate benign from malignant lesions using model-free parameter maps. Methods In this retrospective study, BRCA-positive patients who had an MRI from November 2013 to February 2019 that led to a biopsy (BI-RADS 4) or imaging follow-up (BI-RADS 3) for sub-centimeter lesions were included. Two radiologists assessed all lesions independently and in consensus according to BI-RADS. Radiomics features were calculated using open-source CERR software. Univariate analysis and multivariate modeling were performed to identify significant radiomics features and clinical factors to be included in a machine learning model to differentiate malignant from benign lesions. Results Ninety-six BRCA mutation carriers (mean age at biopsy = 45.5 ± 13.5 years) were included. Consensus BI-RADS classification assessment achieved a diagnostic accuracy of 53.4%, sensitivity of 75% (30/40), specificity of 42.1% (32/76), PPV of 40.5% (30/74), and NPV of 76.2% (32/42). The machine learning model combining five parameters (age, lesion location, GLCM-based correlation from the pre-contrast phase, first-order coefficient of variation from the 1st post-contrast phase, and SZM-based gray level variance from the 1st post-contrast phase) achieved a diagnostic accuracy of 81.5%, sensitivity of 63.2% (24/38), specificity of 91.4% (64/70), PPV of 80.0% (24/30), and NPV of 82.1% (64/78). Conclusions Radiomics analysis coupled with machine learning improves the diagnostic accuracy of MRI in characterizing sub-centimeter breast masses as benign or malignant compared with qualitative morphological assessment with BI-RADS classification alone in BRCA mutation carriers. Key Points • Radiomics and machine learning can help differentiate benign from malignant breast masses even if the masses are small and morphological features are benign. • Radiomics and machine learning analysis showed improved diagnostic accuracy, specificity, PPV, and NPV compared with qualitative morphological assessment alone.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Improved characterization of sub-centimeter enhancing breast masses on MRI with radiomics and machine learning in BRCA mutation carriers

et al. 2020

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“…16 However, invasive malignancy has been reported, and the likelihood of finding cancer on biopsy of a solitary enhancing focus ranges from 0.6% to 2.3%, including one study demonstrating this likelihood is significantly higher with increasing proximity to known cancer. 8,16,21,22 As the spatial resolution increases in MRI, it has been suggested to consider foci measuring 4-5 mm as small masses and apply lesion morphological criteria such as unsharp margin and low signal on T 2 weighted images. 12,22 However, according to our data, morphological characteristics, T 2 signal and kinetics in malignant foci and masses #5 mm were not useful in determining if these were benign or malignant.…”

Section: Discussionmentioning

confidence: 99%

MRI appearance of invasive subcentimetre breast carcinoma: benign characteristics are common

Meissnitzer

Dershaw

Feigin

et al. 2017

BJR

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Benign characteristics are present in most invasive cancers ≤5 mm. Small cancers on MRI may need to demonstrate growth to determine need for biopsy. Advances in knowledge: MR lesion characteristics may not be helpful in determining whether small lesions on MR are benign or malignant. However, as 97% of cancers in our study showed interval change when a prior MR for comparison was available, new lesions or increasing size should lead to consideration of biopsy.

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“…Of note, we obtained a malignancy rate well within the lower limits of the range presented in the literature for the general population [ 12 , 14 ]. There is a significant variability regarding the malignancy rate of foci (from 0.6 to 23 %) [ 12 – 14 , 17 – 21 ]. This high variability can have various explanations the more relevant being case selection and the definition of focus.…”

Section: Discussionmentioning

confidence: 99%

“…This high variability can have various explanations the more relevant being case selection and the definition of focus. In some studies [ 14 , 21 , 22 ], lesions visible on pre-contrast T1w or T2w sequences, and thus amenable of further characterization, were classified as foci on the basis of their maximum diameter. According to the latter BI-RADS definition [ 11 ], this category of MRI findings was excluded from our analysis.…”

Section: Discussionmentioning

confidence: 99%

Foci on breast magnetic resonance imaging in high-risk women: cancer or not?

et al. 2016

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PurposeTo assess how frequently foci are identified on MRI in high-risk patients, and their association with malignancy, breast density, and background parenchymal enhancement (BPE).Materials and methodsIn this multicentric study, two readers, in consensus, retrospectively reviewed screening breast MRI of 245 high-risk women, performed between 2009 and 2014. Eligible patients had at least two consecutive screening MRI, and a follow-up of at least 1 year after a lesion was first detected; histology was available for all suspicious findings. Breast density, BPE (both using BI-RADS lexicon), presence, and changes at follow-up for foci were evaluated. Clinical history of the patients was reviewed. Chi-square test was used to define significant correlations.Results166 women (mean age 43 years), who underwent a median of 4 MRI (range 2–6) during the study period, were included. 68 foci were found in 58 women [34.9 %, 95 % confidence interval (CI) 28.1–42.5 %]. Foci were more frequent in dense breasts (P = 0.079) and with moderate or marked BPE (P < 0.001). During follow-up, two foci increased in size (2.9 %, 95 % CI 0.8–10.1 %) and at biopsy, a cancer was found (1 high-grade ductal carcinoma in situ, 1 tubular carcinoma). Breast cancer was diagnosed in the other three cases, not initially appearing as foci, and it was more frequent in women with dense breasts (P = 0.04); no correlation between cancer and BPE was found (P = 0.145).ConclusionsFoci are relatively frequent in screening MRI, and they are usually benign. An increase in size is the most reliable criteria to suspect malignancy.

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Small Masses on Breast MR

Cited by 23 publications

References 19 publications

Improved characterization of sub-centimeter enhancing breast masses on MRI with radiomics and machine learning in BRCA mutation carriers

Improved characterization of sub-centimeter enhancing breast masses on MRI with radiomics and machine learning in BRCA mutation carriers

MRI appearance of invasive subcentimetre breast carcinoma: benign characteristics are common

Foci on breast magnetic resonance imaging in high-risk women: cancer or not?

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