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

Meissnitzer, Matthias; Dershaw, D. David; Feigin, Kimberly N.; Bernard‐Davila, Blanca; Barra, Filipe Ramos; Morris, Elizabeth A.

doi:10.1259/bjr.20170102

Cited by 24 publications

(20 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With advancements in hardware and software, the spatial resolution of MRI has improved, allowing not only the detection but also the morphologic characterization of small enhancing lesions [19]. 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%).…”

Section: Discussionmentioning

confidence: 99%

“…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%).…”

Section: Discussionmentioning

confidence: 99%

“…As these cancers are also more likely to be high grade and frequently triple negative (hormone receptor and HER-2 negative), the threshold for the recommendation of a biopsy should be low [10,11]. Prior studies [12,13] showed how benign morphology is common in invasive cancers of less than 5 mm in diameter regardless of BRCA mutation status and suggested that all masses representing an interval change as well as lesions increasing in size should lead to a biopsy. Unfortunately, BRCA carriers are also more prone to developing benign tumors of the breast [14,15], resulting in numerous benign biopsies during their life unless prophylactic mastectomy is performed.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2020

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Schlossbauer et al summarized MRI dynamic and morphologic criteria in a diagnostic score and they concluded that score differences between benign and malignant lesions were reduced in lesions smaller than 1 cm in size. Other work indicates that cancers less than 1 cm become more obviously malignant as they enlarged, and that cancers less than 5 mm had benign characteristics . As MRI screening is becoming more widespread for selected high‐risk populations, it has been noted in this population that the majority of invasive carcinomas detected via this route are smaller than 1 cm in size .…”

mentioning

confidence: 99%

“…Other work indicates that cancers less than 1 cm become more obviously malignant as they enlarged, and that cancers less than 5 mm had benign characteristics. 8 As MRI screening is becoming more widespread for selected high-risk populations, it has been noted in this population that the majority of invasive carcinomas detected via this route are smaller than 1 cm in size. 9 Lesions regarded as suspicious (categorized as Breast Imaging-Reporting and Data System [BI-RADS] 4 or 5) are usually recommended for biopsy, 10 and since a higher percentage of smaller lesions are known to be benign, 11 this can potentially lead to a large number of negative biopsies, and thus a low positive predictive value.…”

mentioning

confidence: 99%

Characterization of Sub‐1 cm Breast Lesions Using Radiomics Analysis

Gibbs

Onishi

Sadinski

et al. 2019

Magnetic Resonance Imaging

View full text Add to dashboard Cite

Background Small breast lesions are difficult to visually categorize due to the inherent lack of morphological and kinetic detail. Purpose To assess the efficacy of radiomics analysis in discriminating small benign and malignant lesions utilizing model free parameter maps. Study Type Retrospective, single center. Population In all, 149 patients, with a total of 165 lesions scored as BI‐RADS 4 or 5 on MRI, with an enhancing volume of <0.52 cm3. Field Strength/Sequence Higher spatial resolution T1‐weighted dynamic contrast‐enhanced imaging with a temporal resolution of ~90 seconds performed at 3.0T. Assessment Parameter maps reflecting initial enhancement, overall enhancement, area under the enhancement curve, and washout were generated. Heterogeneity measures based on first‐order statistics, gray level co‐occurrence matrices, run length matrices, size zone matrices, and neighborhood gray tone difference matrices were calculated. Data were split into a training dataset (~75% of cases) and a test dataset (~25% of cases). Statistical Tests Comparison of medians was assessed using the nonparametric Mann–Whitney U‐test. The Spearman rank correlation coefficient was utilized to determine significant correlations between individual features. Finally, a support vector machine was employed to build multiparametric predictive models. Results Univariate analysis revealed significant differences between benign and malignant lesions for 58/133 calculated features (P < 0.05). Support vector machine analysis resulted in areas under the curve (AUCs) ranging from 0.75–0.81. High negative (>89%) and positive predictive values (>83%) were found for all models. Data Conclusion Radiomics analysis of small contrast‐enhancing breast lesions is of value. Texture features calculated from later timepoints on the enhancement curve appear to offer limited additional value when compared with features determined from initial enhancement for this patient cohort. Level of Evidence: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1468–1477.

show abstract

Breast Cancer Growth on Serial MRI: Volume Doubling Time Based on 3‐Dimensional Tumor Volume Assessment

Wang

Luo

Chen

et al. 2023

Magnetic Resonance Imaging

View full text Add to dashboard Cite

BackgroundThe volume doubling time (VDT) of breast cancer was most frequently calculated using the two‐dimensional (2D) diameter, which is not reliable for irregular tumors. It was rarely investigated using three‐dimensional (3D) imaging with tumor volume on serial magnetic resonance imaging (MRI).PurposeTo investigate the VDT of breast cancer using 3D tumor volume assessment on serial breast MRIs.Study TypeRetrospective.SubjectsSixty women (age at diagnosis: 57 ± 10 years) with breast cancer, assessed by two or more breast MRI examinations. The median interval time was 791 days (range: 70–3654 days).Field Strength/Sequence3‐T, fast spin‐echo T2‐weighted imaging (T2WI), single‐shot echo‐planar diffusion‐weighted imaging (DWI), and gradient echo dynamic contrast‐enhanced imaging.AssessmentThree radiologists independently reviewed the morphological, DWI, and T2WI features of lesions. The whole tumor was segmented to measure the volume on contrast‐enhanced images. The exponential growth model was fitted in the 11 patients with at least three MRI examinations. The VDT of breast cancer was calculated using the modified Schwartz equation.Statistical TestsMann–Whitney U test, Kruskal–Wallis test, Chi‐squared test, intraclass correlation coefficients, and Fleiss kappa coefficients. A P‐value <0.05 was considered statistically significant. The exponential growth model was evaluated using the adjusted R2 and root mean square error (RMSE).ResultsThe median tumor diameter was 9.7 mm and 15.2 mm on the initial and final MRI, respectively. The median adjusted R2 and RMSE of the 11 exponential models were 0.97 and 15.8, respectively. The median VDT was 540 days (range: 68–2424 days). For invasive ductal carcinoma (N = 33), the median VDT of the non‐luminal type was shorter than that of the luminal type (178 days vs. 478 days). On initial MRI, breast cancer manifesting as a focus or mass lesion showed a shorter VDT than that of a non‐mass enhancement (NME) lesion (median VDT: 426 days vs. 665 days).Data ConclusionA shorter VDT was observed in breast cancer manifesting as focus or mass as compared to an NME lesion.Level of Evidence3Technical EfficacyStage 2

show abstract

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

Cited by 24 publications

References 22 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

Characterization of Sub‐1 cm Breast Lesions Using Radiomics Analysis

Breast Cancer Growth on Serial MRI: Volume Doubling Time Based on 3‐Dimensional Tumor Volume Assessment

Contact Info

Product

Resources

About