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

Magnetic resonance imaging of the breast: Could it be used as a screening test?

OBJECTIVES

To investigate whether magnetic resonance imaging (MRI) best detects early malignancy in high-risk women.

METHODS

A retrospective, cross-sectional study, carried out at King Abdulaziz University Hospital, Jeddah, Saudi Arabia, included 419 female breast cancer patients aged 16-84 years (mean age of 49). Data were collected from the radiological department's database to compare the MRI, ultrasound (US), and mammography results, with or without tissue biopsy.

RESULTS

In diagnosing benign versus malignant lesions, MRI showed significant agreement with tissue biopsy, with high sensitivity (70%) and specificity (87%); its positive predictive value (PPV) was 92% and negative predictive value (NPV) was 56%. While US has a PPV of 84% and NPV of 63%; with a sensitivity (79%) and specificity (71%). In patients without tissue biopsy, there was little difference between mammography and US compared with MRI results.

CONCLUSION

Magnetic resonance imaging is more effective than US and mammography for early detection of BC. It showed high sensitivity in detecting breast lesions and high specificity in characterizing their nature when correlated with pathological results. Ultrasound screening followed by MRI is suggested for undetected or suspected lesions. This will increase the breast lesion detection rate, reduce unneeded tissue biopsies, and enhance the disease's survival rate.

Comparison of Diagnostic Performance between Classic and Modified Abbreviated Breast MRI and the MRI Features Affecting Their Diagnostic Performance

Abbreviated breast magnetic resonance imaging (AB-MRI) has emerged as a supplementary screening tool, though protocols have not been standardized. The purpose of this study was to compare the diagnostic performance of modified and classic AB-MRI and determine MRI features affecting their diagnostic performance. Classic AB-MRI included one pre- and two post-contrast T1-weighted imaging (T1WI) scans, while modified AB-MRI included a delayed post-contrast axial T1WI scan and an axial T2-weighted interpolated scan obtained between the second and third post-contrast T1WI scans. Four radiologists (two specialists and two non-specialists) independently categorized the lesions. The MRI features investigated were lesion size, lesion type, and background parenchymal enhancement (BPE). The Wilcoxon rank-sum test, Fisher's exact test, and bootstrap-based test were used for statistical analysis. The average area under the curve (AUC) for modified AB-MRI was significantly greater than that for classic AB-MRI (0.76 vs. 0.70, p = 0.010) in all reader evaluations, with a similar trend in specialist evaluations (0.83 vs. 0.76, p = 0.004). Modified AB-MRI demonstrated increased AUCs and better diagnostic performance than classic AB-MRI, especially for lesion size > 10 mm (p = 0.018) and mass lesion type (p = 0.014) in specialist evaluations and lesion size > 10 mm (p = 0.003) and mild (p = 0.026) or moderate BPE (p = 0.010) in non-specialist evaluations.

Clinical and Imaging Features of MRI Screen-Detected Breast Cancer

BACKGROUND

Supplemental screening with breast MRI is recommended annually for patients who have greater than 20% lifetime risk for breast cancer. While there is robust data regarding features of mammographic screen-detected breast cancers, there is limited data regarding MRI-screen-detected cancers.

PATIENTS AND METHODS

Screening breast MRIs performed between August 1, 2016 and July 30, 2022 identified 50 screen-detected breast cancers in 47 patients. Clinical and imaging features of all eligible cancers were recorded.

RESULTS

During the study period, 50 MRI-screen detected cancers were identified in 47 patients. The majority of MRI-screen detected cancers (32/50, 64%) were invasive. Pathology revealed ductal carcinoma in situ (DCIS) in 36% (18/50), invasive ductal carcinoma (IDC) in 52% (26/50), invasive lobular carcinoma in 10% (5/50), and angiosarcoma in 2% (1/50). The majority of patients (43/47, 91%) were stage 0 or 1 at diagnosis and there were no breast cancer-related deaths during the follow-up periods. Cancers presented as masses in 50% (25/50), nonmass enhancement in 48% (25/50), and a focus in 2% (1/50). DCIS was more likely to present as nonmass enhancement (94.4%, 17/18), whereas invasive cancers were more likely to present as masses (75%, 24/32) (P < .001). All cancers that were stage 2 at diagnosis were detected either on a baseline exam or more than 4 years since the prior MRI exam.

CONCLUSION

MRI screen-detected breast cancers were most often invasive cancers. Cancers detected by MRI screening had an excellent prognosis in our study population. Invasive cancers most commonly presented as a mass.

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

BACKGROUND

The 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).

PURPOSE

To investigate the VDT of breast cancer using 3D tumor volume assessment on serial breast MRIs.

STUDY TYPE

Retrospective.

SUBJECTS

Sixty 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/SEQUENCE

3-T, fast spin-echo T2-weighted imaging (T2WI), single-shot echo-planar diffusion-weighted imaging (DWI), and gradient echo dynamic contrast-enhanced imaging.

ASSESSMENT

Three 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 TESTS

Mann-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).

RESULTS

The 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 CONCLUSION

A shorter VDT was observed in breast cancer manifesting as focus or mass as compared to an NME lesion.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.

Quantitative apparent diffusion coefficient metrics for MRI-only suspicious breast lesions: any added clinical value?

Background

Suspicious breast lesions [Breast Imaging Reporting and Data System (BI-RADS) category 4 or 5] detected only by magnetic resonance imaging (MRI) and invisible on other initial imaging modalities (MRI-only lesions) are usually small and poorly characterized in previous literature, thus making diagnosis and management difficult. This study aimed to investigate the clinical significance of quantitative apparent diffusion coefficient (ADC) metrics derived from conventional diffusion-weighted imaging (DWI) on evaluating MRI-only lesions.

Methods

A total of 90 suspicious MRI-only lesions were evaluated, including 51 malignant and 39 benign lesions. Morphological and kinetic characteristics of all lesions (termed BI-RADS parameters) were described according to the BI-RADS lexicon on dynamic contrast-enhanced (DCE) imaging. Minimum, maximum, and mean ADC values (ADCmin, ADCmax, ADCmean) were obtained by measuring the ADC map of DWI. ADCheterogeneity was then obtained by the following formula: ADCheterogeneity = (ADCmax - ADCmin)/ADCmean. Diagnostic performance of these parameters was assessed and compared using the receiver operating characteristic (ROC) curve.

Results

Of the 90 MRI-only lesions, there were 45 masses and 45 non-mass lesions. Among BI-RADS parameters, only two different kinetic patterns were significantly different between benign and malignant groups (P=0.005 and P<0.001, respectively). The area under the ROC curve (AUC) of combined significant ADC parameters (ADCmin, ADCmean, and ADCmax, all P≤0.001) was significantly higher than that of the two different kinetic patterns (P=0.006 for both). For MRI-only masses, only ADCmean and ADCmax, among all BI-RADS and ADC parameters, had diagnostic value (combined AUC =0.833). For non-mass lesions, size, distribution, ADCmin, and ADCmean were significantly different between benign and malignant groups (P=0.004, P<0.001, P=0.001, and P<0.001, respectively). In addition, ADCmean had the highest diagnostic performance among all ADC parameters, regardless of mass or non-mass (AUC =0.825 and 0.812, respectively). ADCheterogeneity showed no significant differences, no matter in mass or non-mass groups (P=0.62 and 0.43, respectively).

Conclusions

In differentiating MRI-only suspicious lesions, quantitative ADC metrics generally performed better than BI-RADS parameters, and ADCmean is still the best ADC parameter to distinguish MRI-only lesions.

'Earlier than Early' Detection of Breast Cancer in Israeli BRCA Mutation Carriers Applying AI-Based Analysis to Consecutive MRI Scans

Female BRCA1/BRCA2 (=BRCA) pathogenic variants (PVs) carriers are at a substantially higher risk for developing breast cancer (BC) compared with the average risk population. Detection of BC at an early stage significantly improves prognosis. To facilitate early BC detection, a surveillance scheme is offered to BRCA PV carriers from age 25-30 years that includes annual MRI based breast imaging. Indeed, adherence to the recommended scheme has been shown to be associated with earlier disease stages at BC diagnosis, more in-situ pathology, smaller tumors, and less axillary involvement. While MRI is the most sensitive modality for BC detection in BRCA PV carriers, there are a significant number of overlooked or misinterpreted radiological lesions (mostly enhancing foci), leading to a delayed BC diagnosis at a more advanced stage. In this study we developed an artificial intelligence (AI)-network, aimed at a more accurate classification of enhancing foci, in MRIs of BRCA PV carriers, thus reducing false-negative interpretations. Retrospectively identified foci in prior MRIs that were either diagnosed as BC or benign/normal in a subsequent MRI were manually segmented and served as input for a convolutional network architecture. The model was successful in classification of 65% of the cancerous foci, most of them triple-negative BC. If validated, applying this scheme routinely may facilitate 'earlier than early' BC diagnosis in BRCA PV carriers.

Design of a novel aptamer/molecularly imprinted polymer hybrid modified Ag-Au@Insulin nanoclusters/Au-gate-based MoS2 nanosheet field-effect transistor for attomolar detection of BRCA1 gene

Early detection of breast cancer, the first main cause of death in women, with robust assay platforms using appropriate biomarkers is of great importance for diagnosis and follow-up of the disease progression. This paper introduces an extra selective and sensitive label-free aptasensor for the screening of BRCA1 gene biomarker by taking advantage of a gate modified with aptamer and molecularly imprinted polymer hybrid (MIP) as a new synthetic receptor film coupled with an electrolyte-gated molybdenum disulfide (MoS2) field-effect transistor (FET). The Au gate surface of FET was modified with insulin stabilized bimetallic Ag-Au@nanoclusters (Ag-Au@InsNCs), after which, the immobilization of the hybridized aptamer and o-phenylenediamine was electropolymerized to form an aptamer-MIP hybrid receptor. The output characteristics of Apta-MIP hybrid modified Au gate MoS2 FET device were followed as a result of change in electrical double layer capacitance of electrolye-gate interface. The magnitude of decrease in the drain current showed a linear response over a wide concentration range of 10 aM to 1 nM of BRCA1 ssDNA with a sensitivity as high as 0.4851 μA/decade of concentration and a limit of detection (LOD) of 3.0 aM while very low responses observed for non-imprinted polymer. The devised aptasensor not only was capable to the discrimination of the complementary versus one-base mismatch BRCA1 ssDNA sequence, but also it could detect the complementary BRCA1 ssDNA in spiked human serum samples over a wide concentration range of 10 aM to 1.0 nM with a low LOD of 6.4 aM and a high sensitivity 0.3718 μA/decade.

BI-RADS 3 Assessment on MRI: A Lesion-Based Review for Breast Radiologists

Unlike mammography and US, limited data exist to establish well-defined criteria for MRI findings that have a ≤2% likelihood of malignancy. Therefore, determining which findings are appropriate for a BI-RADS 3 assessment on MRI remains challenging and variable among breast radiologists. Emerging data suggest that BI-RADS 3 should be limited to baseline MRI examinations (or examinations with less than two years of prior comparisons) performed for high-risk screening and only used for masses with all of the typical morphological and kinetic features suggestive of a fibroadenoma or dominant enhancing T2 hypointense foci that is distinct from background parenchymal enhancement and without suspicious kinetics. This article presents an updated discussion of BI-RADS 3 assessment (probably benign) for breast MRI using current evidence.

The Influence of Data-Driven Compressed Sensing Reconstruction on Quantitative Pharmacokinetic Analysis in Breast DCE MRI

Radial acquisition with MOCCO reconstruction has been previously proposed for high spatial and temporal resolution breast DCE imaging. In this work, we characterize MOCCO across a wide range of temporal contrast enhancement in a digital reference object (DRO). Time-resolved radial data was simulated using a DRO with lesions in different PK parameters. The under sampled data were reconstructed at 5 s temporal resolution using the data-driven low-rank temporal model for MOCCO, compressed sensing with temporal total variation (CS-TV) and more conventional low-rank reconstruction (PCB). Our results demonstrated that MOCCO was able to recover curves with K^trans values ranging from 0.01 to 0.8 min⁻¹ and fixed V_e = 0.3, where the fitted results are within a 10% bias error range. MOCCO reconstruction showed less impact on the selection of different temporal models than conventional low-rank reconstruction and the greater error was observed with PCB. CS-TV showed overall underestimation in both K^trans and V_e. For the Monte-Carlo simulations, MOCCO was found to provide the most accurate reconstruction results for curves with intermediate lesion kinetics in the presence of noise. Initial in vivo experiences are reported in one patient volunteer. Overall, MOCCO was able to provide reconstructed time-series data that resulted in a more accurate measurement of PK parameters than PCB and CS-TV.

Improved value of whole-lesion histogram analysis on DCE parametric maps for diagnosing small breast cancer (≤ 1 cm)

OBJECTIVES

To determine if whole-lesion histogram analysis on dynamic contrast-enhanced (DCE) parametric maps help to improve the diagnostic accuracy of small suspicious breast lesions (≤ 1 cm).

METHODS

This retrospective study included 99 female patients with 114 lesions (40 malignant and 74 benign lesions) suspicious on magnetic resonance imaging (MRI).Two radiologists reviewed all lesions and descripted the morphologic and kinetic characteristics according to BI-RADS by consensus. Whole lesions were segmented on DCE parametric maps (washin and washout), and quantitative histogram features were extracted. Univariate analysis and multivariate logistic regression analysis with forward stepwise covariate selection were performed to identify significant variables. Diagnostic performance was assessed and compared with that of qualitative BI-RADS assessment and quantitative histogram analysis by ROC analysis.

RESULTS

For malignancy defined as a washout or plateau pattern, the qualitative kinetic pattern showed a significant difference between the two groups (p = 0.023), yielding an AUC of 0.603 (95% confidence interval [CI]: 0.507, 0.694). The mean and median of washout were independent quantitative predictors of malignancy (p = 0.002, 0.010), achieving an AUC of 0.796 (95% CI: 0. 709, 0.865). The AUC of the quantitative model was better than that of the qualitative model (p < 0.001).

CONCLUSIONS

Compared with the qualitative BI-RADS assessment, quantitative whole-lesion histogram analysis on DCE parametric maps was better to discriminate between small benign and malignant breast lesions (≤ 1 cm) initially defined as suspicious on DCE-MRI.

KEY POINTS

• For malignancy defined as a washout or plateau, the kinetic pattern may provide information to diagnose small breast cancer. • The mean and median of washout map were significantly lower for small malignant breast lesions than for benign lesions. • Quantitative histogram analysis on MRI parametric maps improves diagnostic accuracy for small breast cancer, which may obviate unnecessary biopsy.

Radiomics and Machine Learning with Multiparametric Breast MRI for Improved Diagnostic Accuracy in Breast Cancer Diagnosis

The purpose of this multicenter retrospective study was to evaluate radiomics analysis coupled with machine learning (ML) of dynamic contrast-enhanced (DCE) and diffusion-weighted imaging (DWI) radiomics models separately and combined as multiparametric MRI for improved breast cancer detection. Consecutive patients (Memorial Sloan Kettering Cancer Center, January 2018-March 2020; Medical University Vienna, from January 2011-August 2014) with a suspicious enhancing breast tumor on breast MRI categorized as BI-RADS 4 and who subsequently underwent image-guided biopsy were included. In 93 patients (mean age: 49 years ± 12 years; 100% women), there were 104 lesions (mean size: 22.8 mm; range: 7-99 mm), 46 malignant and 58 benign. Radiomics features were calculated. Subsequently, the five most significant features were fitted into multivariable modeling to produce a robust ML model for discriminating between benign and malignant lesions. A medium Gaussian support vector machine (SVM) model with five-fold cross validation was developed for each modality. A model based on DWI-extracted features achieved an AUC of 0.79 (95% CI: 0.70-0.88), whereas a model based on DCE-extracted features yielded an AUC of 0.83 (95% CI: 0.75-0.91). A multiparametric radiomics model combining DCE- and DWI-extracted features showed the best AUC (0.85; 95% CI: 0.77-0.92) and diagnostic accuracy (81.7%; 95% CI: 73.0-88.6). In conclusion, radiomics analysis coupled with ML of multiparametric MRI allows an improved evaluation of suspicious enhancing breast tumors recommended for biopsy on clinical breast MRI, facilitating accurate breast cancer diagnosis while reducing unnecessary benign breast biopsies.

Breast MRI: False-Negative Results and Missed Opportunities

Breast MRI is the most sensitive modality for the detection of breast cancer. However, false-negative cases may occur, in which the cancer is not visualized at MRI and is instead diagnosed with another imaging modality. The authors describe the causes of false-negative breast MRI results, which can be categorized broadly as secondary to perceptual errors or cognitive errors, or nonvisualization secondary to nonenhancement of the tumor. Tips and strategies to avoid these errors are discussed. Perceptual errors occur when an abnormality is not prospectively identified, yet the examination is technically adequate. Careful development of thorough search patterns is critical to avoid these errors. Cognitive errors occur when an abnormality is identified but misinterpreted or mischaracterized as benign. The radiologist may avoid these errors by utilizing all available prior examinations for comparison, viewing images in all planes to better assess the margins and shapes of abnormalities, and appropriately integrating all available information from the contrast-enhanced, T2-weighted, and T1-weighted images as well as the clinical history. Despite this, false-negative cases are inevitable, as certain subtypes of breast cancer, including ductal carcinoma in situ, invasive lobular carcinoma, and certain well-differentiated invasive cancers, may demonstrate little to no enhancement at MRI, owing to differences in angiogenesis and neovascularity. MRI is a valuable diagnostic tool in breast imaging. However, MRI should continue to be used as a complementary modality, with mammography and US, in the detection of breast cancer. ^©RSNA, 2021.

Magnetic Resonance Imaging Features in Different Types of Invasive Breast Cancer: A Systematic Review of the Literature

Breast cancer is the most common malignancy affecting women worldwide, and early diagnosis of breast cancer is the key to its successful and effective treatment. Traditional imaging techniques such as mammography and ultrasound are used to detect and configure breast abnormalities; unfortunately, these modalities have low sensitivity and specificity, particularly in young patients with dense breast tissue, breast implants, or post-surgical scar/architecture distortions. Therefore, breast magnetic resonance imaging (MRI) has been superior in the characterization and detection of breast cancer, especially that with invasive features. This review article explores the importance of breast MRI in the early detection of invasive breast cancer versus traditional tools, including mammography and ultrasound, while also analyzing the use of MRI as a screening tool for high-risk women. We will also discuss the different MRI features for invasive ductal carcinoma and lobular carcinoma and the role of breast MRI in the detection of ductal carcinoma in situ with a focus on the utilization of new techniques, including MR spectroscopy and diffusion-weighted imaging.

The nipple-areolar complex: comprehensive imaging review

The nipple-areolar complex can be affected by a variety of benign and malignant entities that can present with non-specific symptoms. Benign pathologies commonly affecting the nipple-areolar complex include nipple calcifications, nipple adenoma, abscess of Montgomery tubercles, ductal ectasia, periductal mastitis, and papilloma. Malignant pathologies that affect the nipple-areolar complex include Paget's disease of the breast, ductal carcinoma in-situ, and invasive ductal carcinoma. Clinical history and examination, imaging, and tissue sampling when appropriate are co-dependent factors that guide the assessment of nipple-areolar pathologies. This article provides a review of the normal anatomy, common anatomical variants, benign and malignant pathologies, and imaging techniques to guide the diagnostic assessment of the nipple-areolar complex.

The potential utility of abbreviated breast MRI (FAST MRI) as a tool for breast cancer screening: a systematic review and meta-analysis

AIM

To synthesise evidence comparing abbreviated breast magnetic resonance imaging (abMRI) to full-protocol MRI (fpMRI) in breast cancer screening.

MATERIALS AND METHODS

A systematic search was undertaken in multiple databases. Cohort studies without enrichment, presenting accuracy data of abMRI in screening, for any level of risk (population, moderate, high risk) were included. Level of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE). Meta-analyses (bivariate random effects model) were performed for abMRI, with fpMRI and histology from fpMRI-positive cases as reference standard, and with follow-up to symptomatic detection added to the fpMRI. The review also covers evidence comparing abMRI with mammographic techniques.

RESULTS

The title and abstract review retrieved 23 articles. Five studies (six articles) were included (2,763 women, 3,251 screening rounds). GRADE assessment of the evidence was very low because the reference standard was interpreted with knowledge of the index test and biopsy was not obtained for all abMRI positives. The overall sensitivity for abMRI, with fpMRI (and histology for fpMRI positives) as reference standard, was 94.8% (95% confidence interval [CI] 85.5-98.2) and specificity as 94.6% (95% CI: 91.5-96.6). Three studies (1,450 women, 1,613 screening rounds) presented follow-up data, enabling comparison between abMRI and fpMRI. Sensitivities and specificities for abMRI did not differ significantly from those for fpMRI (p=0.83 and p=0.37, respectively).

CONCLUSION

A very low level of evidence suggests abMRI could be accurate for breast cancer screening. Research is required, with follow-up to interval cancer, to determine the effect its use could have on clinical outcome.

Percentage volume of delayed kinetics in computer-aided diagnosis of MRI of the breast to reduce false-positive results and unnecessary biopsies

AIM

To investigate the best cut-off percentage volume of delayed kinetics using magnetic resonance imaging (MRI) with computer-aided diagnosis (CAD) to reduce unnecessary biopsies in patients with newly diagnosed breast cancer.

MATERIALS AND METHODS

Between January 2017 and December 2018, 94 malignant and 56 benign masses were analysed using MRI CAD. All malignant and benign masses measured <2 cm and were confirmed histopathologically. The optimal cut-off values for washout, plateau, and persistent components were determined using the maximum Youden Index. The positive predictive value (PPV) was analysed using morphological descriptors and combining the percentage volume of delayed kinetics.

RESULTS

The area under the curve (AUC) was highest at ≤73% persistent component (AUC=0.759). In the subgroup analyses of masses <1 cm, the AUC was highest a plateau of >26% (AUC=0.697). When the persistent ≤73% criterion was applied to the lesions of C4a, the positive predictive value (PPV) increased from 61.9% to 72.44% with reduced false-negative cases and when applied to the lesions of C4a and C4b, the PPV increased from 61.9% to 78.1% with slightly increased false-negative cases. For subcentimetre lesions, the PPV increased from 46.77% to 54.72% with the same number of false-negative cases, when a plateau of >26% was applied to C4a, and the PPV increased from 46.77% to 61.36% with five false-negative cases when applied to C4a and C4b.

CONCLUSION

The percentage volume of delayed kinetics has the potential to improve the PPV of breast MRI. When suspicious masses <2 cm do not show ≤73% persistence, follow-up rather than biopsy could be considered; however, to avoid increasing false-negative cases, delayed kinetic information should be used with caution and accurate margin assessment is essential.

Outcomes of Foci on Breast MRI: Features Associated With Malignancy

OBJECTIVE. The objective of this study was to determine the outcomes of foci seen on breast MRI and to evaluate imaging features associated with malignancy. MATERIALS AND METHODS. In this institutional review board-approved retrospective study, we reviewed 200 eligible foci in 179 patients that were assigned BI-RADS category of 3 or 4 from December 2004 to August 2018. Clinical and imaging features of all eligible foci were collected, and associations with malignant outcomes were evaluated. Malignancy rates were also calculated. RESULTS. Of 200 eligible foci, 64 were assigned BI-RADS category 3 and 136 were assigned BI-RADS category 4. The malignancy rate was 1.6% (1/64) among BI-RADS 3 foci and 17.6% (24/136) for BI-RADS 4 foci. The majority of malignant foci represented invasive breast cancer (68.0%, 17/25). Focus size and washout kinetics were significantly associated with malignant outcome (p < 0.05). CONCLUSION. Despite the high prevalence of foci on breast MRI, data are limited to guide their management. Foci should not be disregarded, because foci undergoing biopsy had a malignancy rate of 17.6%, with the majority of malignant foci representing invasive cancer. Larger size and washout kinetics were associated with malignancy in our study and should raise the suspicion level for a focus on breast MRI.

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

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.

Electronic supplementary material

The online version of this article (10.1007/s00330-020-06991-7) contains supplementary material, which is available to authorized users.

BI-RADS category 3, 4, and 5 lesions identified at preoperative breast MRI in patients with breast cancer: implications for management

OBJECTIVES

To investigate outcomes and retrospectively evaluate characteristics of additional lesions initially assessed as BI-RADS category 3, 4, and 5 at preoperative MRI to determine appropriate follow-up management.

METHODS

We retrospectively reviewed 429 lesions other than primary cancer initially assessed as BI-RADS category 3, 4, and 5 at preoperative MRI in 391 patients with breast cancer from March 2012 to December 2013. We investigated their malignancy rate and outcome according to BI-RADS category assessments. We also analyzed clinical and imaging characteristics of each lesion. Pathological results and imaging follow-up of at least 2 years were used as reference standards.

RESULTS

Of 429 lesions in 391 patients (mean 48.1 years ± 9.4), the malignancy rate of BI-RADS 3, 4, and 5 lesions was 1.4% (3/213), 17.8% (38/214), and 50% (1/2), respectively. Of BI-RADS 3 lesions or BI-RADS 4 or 5 lesions that were followed up after benign-concordant biopsy (n = 114), two contralateral masses (2/306, 0.7%) were diagnosed as malignancy at 13.3 and 33.2 months after initial detection, within a median follow-up of 63.3 months. None of the NME or foci or lesions followed up after benign-concordant biopsy had a delayed diagnosis of malignancy. Of the 391 patients, 97.4% (381/391) received at least one type of adjuvant therapy.

CONCLUSION

The incidence of delayed cancer diagnosis among additionally detected lesions other than primary cancer is very low and short-term follow-up is unnecessary. Contralateral masses which were not confirmed by biopsy may need annual follow-up.

KEY POINTS

• 1.4% (3/213) of BI-RADS 3 lesions were malignant including 2 delayed diagnoses after 13.2 months and 33.2 months, and 17.8% (38/214) of BI-RADS 4 lesions and 50% (1/2) of BI-RADS 5 lesions were malignant. • The incidence of delayed diagnosis from additional MRI-detected lesions was very low (0.7%, 2/306) during follow-up, which were all T1N0 contralateral cancer. • Annual follow-up might be adequate for preoperative MRI-detected BI-RADS 3 lesions and BI-RADS 4 lesions followed up after benign-concordant biopsy.

Differentiation between subcentimeter carcinomas and benign lesions using kinetic parameters derived from ultrafast dynamic contrast-enhanced breast MRI

OBJECTIVES

This study aims to evaluate ultrafast DCE-MRI-derived kinetic parameters that reflect contrast agent inflow effects in differentiating between subcentimeter BI-RADS 4-5 breast carcinomas and benign lesions.

METHODS

We retrospectively reviewed consecutive 3-T MRI performed from February to October 2017, during which ultrafast DCE-MRI was performed as part of a hybrid clinical protocol with conventional DCE-MRI. In total, 301 female patients with 369 biopsy-proven breast lesions were included. Ultrafast DCE-MRI was acquired continuously over approximately 60 s (temporal resolution, 2.7-7.1 s/phase) starting simultaneously with the start of contrast injection. Four ultrafast DCE-MRI-derived kinetic parameters (maximum slope [MS], contrast enhancement ratio [CER], bolus arrival time [BAT], and initial area under gadolinium contrast agent concentration [IAUGC]) and one conventional DCE-MRI-derived kinetic parameter (signal enhancement ratio [SER]) were calculated for each lesion. Wilcoxon rank sum test or Fisher's exact test was performed to compare kinetic parameters, volume, diameter, age, and BI-RADS morphological descriptors between subcentimeter carcinomas and benign lesions. Univariate/multivariate logistic regression analyses were performed to determine predictive parameters for subcentimeter carcinomas.

RESULTS

In total, 125 lesions (26 carcinomas and 99 benign lesions) were identified as BI-RADS 4-5 subcentimeter lesions. Subcentimeter carcinomas demonstrated significantly larger MS and SER and shorter BAT than benign lesions (p = 0.0117, 0.0046, and 0.0102, respectively). MS, BAT, and age were determined as significantly predictive for subcentimeter carcinoma (p = 0.0208, 0.0023, and < 0.0001, respectively).

CONCLUSIONS

Ultrafast DCE-MRI-derived kinetic parameters may be useful in differentiating subcentimeter BI-RADS 4 and 5 carcinomas from benign lesions.

KEY POINTS

• Ultrafast DCE-MRI can generate kinetic parameters, effectively differentiating breast carcinomas from benign lesions. • Subcentimeter carcinomas demonstrated significantly larger maximum slope and shorter bolus arrival time than benign lesions. • Maximum slope and bolus arrival time contribute to better management of suspicious subcentimeter breast lesions.

Characterization of Sub-1 cm Breast Lesions Using Radiomics Analysis

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.