Evaluating the role of strain ratio elastography in determining malignancy potential and calculating objective BIRADS US scores using ultrasonography and elastography features

Purpose: The aims of this study were, firstly, to evaluate solid breast masses based on their malignancy potential and to determine whether the strain elastography ratio (SER) can contribute to classical grey-scale ultrasonography findings, and secondly, to define objective BIRADS US scores using ultrasound (US) and SER findings. Material and methods: A total of 280 patients and 297 solid breast masses were evaluated using sonographic and elastographic data. The SER was measured for each lesion. Results: The positive predictive values (PPV) for each criterion was calculated to be between 35% and 83.3%. The lowest PPV was obtained from hypoechogenicity (35%) and the highest PPV was obtained for anti-parallel features (83.3%). The difference between the mean SER of benign and malignant lesions was statistically significant. After ROC analysis, the SER cut-off value was calculated to be 3.1 for determining if the mass was benign or malignant. Mass scores were calculated for each solid breast mass based on positive predictive values, and BIRADS US score was defined as the sum of mass scores. Conclusions: SER findings can be used as malignancy criteria in evaluating solid breast masses. BIRADS US score can be objectively determined based on US and elastography features instead of doing subjective scoring. As an additional result, all solid breast masses have the possibility to be malignant, even though US and elastography findings indicate the opposite

Can high-frequency ultrasound predict metastatic lymph nodes in patients with invasive breast cancer?

Aim To determine whether high-frequency ultrasound can predict the presence of metastatic axillary lymph nodes, with a high specificity and positive predictive value, in patients with invasive breast cancer. The clinical aim is to identify patients with axillary disease requiring surgery who would not normally, on clinical grounds, have an axillary dissection, so potentially improving outcome and survival rates. Materials and methods The ipsilateral and contralateral axillae of 42 consecutive patients with invasive breast cancer were scanned prior to treatment using a B-mode frequency of 13 MHz and a Power Doppler frequency of 7 MHz. The presence or absence of an echogenic centre for each lymph node detected was recorded, and measurements were also taken to determine the L/S ratio and the widest and narrowest part of the cortex. Power Doppler was also used to determine vascularity. The contralateral axilla was used as a control for each patient. Results In this study of patients with invasive breast cancer, ipsilateral lymph nodes with a cortical bulge ≥3 mm and/or at least two lymph nodes with absent echogenic centres indicated the presence of metastatic axillary lymph nodes (10 patients). The sensitivity and specificity were 52.6% and 100%, respectively, positive and negative predictive values were 100% and 71.9%, respectively, the P value was 0.001 and the Kappa score was 0.55.\ud Conclusion This would indicate that high-frequency ultrasound can be used to accurately predict metastatic lymph nodes in a proportion of patients with invasive breast cancer, which may alter patient management

Selection of diagnostic features on breast MRI to differentiate between malignant and benign lesions using computer-aided diagnosis: differences in lesions presenting as mass and non-mass-like enhancement

Purpose: To investigate methods developed for the characterisation of the morphology and enhancement kinetic features of both mass and non-mass lesions, and to determine their diagnostic performance to differentiate between malignant and benign lesions that present as mass versus non-mass types. Methods: Quantitative analysis of morphological features and enhancement kinetic parameters of breast lesions were used to differentiate among four groups of lesions: 88 malignant (43 mass, 45 non-mass) and 28 benign (19 mass, 9 non-mass). The enhancement kinetics was measured and analysed to obtain transfer constant (Ktrans) and rate constant (kep). For each mass eight shape/margin parameters and 10 enhancement texture features were obtained. For the lesions presenting as nonmass-like enhancement, only the texture parameters were obtained. An artificial neural network (ANN) was used to build the diagnostic model. Results: For lesions presenting as mass, the four selected morphological features could reach an area under the ROC curve (AUC) of 0.87 in differentiating between malignant and benign lesions. The kinetic parameter (kep) analysed from the hot spot of the tumour reached a comparable AUC of 0.88. The combined morphological and kinetic features improved the AUC to 0.93, with a sensitivity of 0.97 and a specificity of 0.80. For lesions presenting as non-mass-like enhancement, four texture features were selected by the ANN and achieved an AUC of 0.76. The kinetic parameter kepfrom the hot spot only achieved an AUC of 0.59, with a low added diagnostic value. Conclusion: The results suggest that the quantitative diagnostic features can be used for developing automated breast CAD (computer-aided diagnosis) for mass lesions to achieve a high diagnostic performance, but more advanced algorithms are needed for diagnosis of lesions presenting as non-mass-like enhancement. © The Author(s) 2009

Digital Mammography, Ultrasound and Magnetic Resonance Imaging Characteristics in Differential Diagnosis of Papillary Carcinoma Subtypes of the Breast and Diagnostic Challenges

Objective: We aimed to investigate mammography (MG), ultrasound (US), and magnetic resonance imaging (MRI) findings of papillary breast carcinoma subtypes and to compare the diagnostic features and performance of the imaging method in distinguishing subtypes. Materials and Methods: Forty-two patients presenting with pathological diagnosis of 45 papillary carcinoma lesions, between 2014 and 2019, were included. Cases were assigned to five subgroups according to the latest World Health Organization (WHO) classification. The clinical characteristics (n = 45) and imaging features of each pathological subgroup were retrospectively related to imaging findings from US (n = 45), MG (n = 37), and breast MRI (n = 23), and further compared. Results: The finding of a palpable mass in all subgroups was more common than nipple discharge on clinical breast evaluation, and no significant difference was found between the subgroups. Irregular shape on MG (10/12, 83.3%, p = 0.039) and US (11/12, 91.7%, p = 0.039) was found more frequently in invasive micropapillary carcinoma (IMPC) compared to other subgroups. Circumscribed margins (4/5, 80%, p = 0.002) occurred more frequently in papillary ductal carcinoma in situ (pDCIS) and encapsulated papillary carcinoma (EPC) than in other subgroups (6/8, 75%, p = 0.002). Lower apparent diffusion coefficient (ADC) values were found in solid papillary cancer (SPC) than in other subgroups (ADC = 0.35 x 10(-3), p = 0.017). Conclusion: Radiological findings of papillary carcinomas overlap with each other. US and MRI are complementary when revealing specific morphological characteristics

A simple classification system (the Tree flowchart) for breast MRI can reduce the number of unnecessary biopsies in MRI-only lesions.

OBJECTIVES: To assess whether using the Tree flowchart obviates unnecessary magnetic resonance imaging (MRI)-guided biopsies in breast lesions only visible on MRI. METHODS: This retrospective IRB-approved study evaluated consecutive suspicious (BI-RADS 4) breast lesions only visible on MRI that were referred to our institution for MRI-guided biopsy. All lesions were evaluated according to the Tree flowchart for breast MRI by experienced readers. The Tree flowchart is a decision rule that assigns levels of suspicion to specific combinations of diagnostic criteria. Receiver operating characteristic (ROC) curve analysis was used to evaluate diagnostic accuracy. To assess reproducibility by kappa statistics, a second reader rated a subset of 82 patients. RESULTS: There were 454 patients with 469 histopathologically verified lesions included (98 malignant, 371 benign lesions). The area under the curve (AUC) of the Tree flowchart was 0.873 (95% CI: 0.839-0.901). The inter-reader agreement was almost perfect (kappa: 0.944; 95% CI 0.889-0.998). ROC analysis revealed exclusively benign lesions if the Tree node was ≤2, potentially avoiding unnecessary biopsies in 103 cases (27.8%). CONCLUSIONS: Using the Tree flowchart in breast lesions only visible on MRI, more than 25% of biopsies could be avoided without missing any breast cancer. KEY POINTS: • The Tree flowchart may obviate >25% of unnecessary MRI-guided breast biopsies. • This decrease in MRI-guided biopsies does not cause any false-negative cases. • The Tree flowchart predicts 30.6% of malignancies with >98% specificity. • The Tree's high specificity aids in decision-making after benign biopsy results