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

An Automatic System to Discriminate Malignant from Benign Massive Lesions on Mammograms

Mammography is widely recognized as the most reliable technique for early detection of breast cancers. Automated or semi-automated computerized classification schemes can be very useful in assisting radiologists with a second opinion about the visual diagnosis of breast lesions, thus leading to a reduction in the number of unnecessary biopsies. We present a computer-aided diagnosis (CADi) system for the characterization of massive lesions in mammograms, whose aim is to distinguish malignant from benign masses. The CADi system we realized is based on a three-stage algorithm: a) a segmentation technique extracts the contours of the massive lesion from the image; b) sixteen features based on size and shape of the lesion are computed; c) a neural classifier merges the features into an estimated likelihood of malignancy. A dataset of 226 massive lesions (109 malignant and 117 benign) has been used in this study. The system performances have been evaluated terms of the receiver-operating characteristic (ROC) analysis, obtaining A_z = 0.80+-0.04 as the estimated area under the ROC curve.Comment: 6 pages, 3 figures; Proceedings of the ITBS 2005, 3rd International Conference on Imaging Technologies in Biomedical Sciences, 25-28 September 2005, Milos Island, Greec