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Magnetic resonance imaging (MRI)-detected lesions are often category 2 or 3 lesions on initial ultrasound examination. In addition, in the case of new non-mass lesions detected on MRI, one would expect to find lesions with ductal dilatation with minimal secretory accumulation, single short lesions with ductal dilatation, cyst-like lesions less than 5 mm in size, mammary gland-like lesions less than 8 mm in size, and very indistinct lesions. Detection is expected to be even more difficult. Currently, there are no clear uniform criteria for the indication of second-look ultrasonography (US) for MRI-detected lesions, so it is not possible to make a general comparison, but recent studies have indicated that the ratio of mass to non-mass MRI-detected lesions is 7:3. And it has been pointed out that the percentage of malignancy is about 30% for each. Before about 2012, the US detection rate was about 70%, and MRI-guided biopsies of undetected lesions showed a small percentage of malignant lesions. Therefore, some observers believe that lesions not detected on US should be followed up, while others believe that MRI-guided biopsy should be performed. Recently, however, the use of surrounding anatomical structures as landmarks for second-look US has increased the detection rate to as high as 87–99%, and the percentage of malignancy remains the same. In addition, recent surveillance of high-risk breast cancer requires careful management of MRI-detected lesions. In this review, we will discuss the literature on MRI-detected lesions and describe ultrasound techniques to accurately detect small lesions and reliably reveal pale lesions based on their structural differences from their surroundings.

Purpose Strain elastography for imaging lesion stiffness is being used as a diagnostic aid in the malignant/benign discrimination of breast diseases. While acquiring elastography in addition to B-mode images has been reported to help avoid performing unnecessary biopsies, intraductal lesions are difficult to discriminate whether they are malignant or benign using elastography. An objective evaluation of strain in lesions was performed in this study by measuring the elasticity index (E-index) and elasticity ratio (E-ratio) of lesions as semi-quantitative numerical indicators of the color distribution of strain. We examined whether ductal carcinoma in situ (DCIS) and intraductal papilloma could be distinguished using these semi-quantitative numerical indicators. Methods In this study, 170 ultrasonographically detected mass lesions in 162 cases (106 malignant lesions and 64 benign lesions)—in which tissue biopsy by core needle biopsy and vacuum-assisted biopsy, or surgically performed histopathological diagnosis, was performed—were selected as subjects from among 1978 consecutive cases (from January 2014 to December 2016) in which strain elastography images were acquired, in addition to standard B-mode breast ultrasonography, by measuring the E-index and E-ratio. Results The cut-off values for E-index and E-ratio in the malignant/benign discrimination of breast lesions were determined to be optimal values at 3.5 and 4.2, respectively, based on receiver operating characteristic (ROC) curve analysis. E-index sensitivity, specificity, accuracy, and AUC value (area under the curve) were 85%, 86%, 85%, and 0.860, respectively, while those for E-ratio were 78%, 74%, 74%, and 0.780, respectively. E-index yielded superior results in all aspects of sensitivity, specificity, accuracy, and AUC values, compared to those of E-ratio. The mean E-index values for malignant tumors and benign tumors were 4.46 and 2.63, respectively, indicating a significant difference (P Conclusion E-index in strain elastography yielded better results than E-ratio in the malignant/benign discrimination of breast diseases. On the other hand, E-index has a high false-negative rate and false-positive rate for intraductal lesions, a factor which should be taken into account when making ultrasound diagnoses.

Background Second-look ultrasonography (US) is commonly performed for breast lesions detected using magnetic resonance imaging (MRI), but the identification rate of these lesions remains low. We investigated if US methods using anatomical breast structures can improve the lesion identification rate of MR-detected lesions and evaluated the diagnostic performance of fine-needle aspiration cytology (FNAC) of the second-look US using the above-mentioned method. Methods We retrospectively assessed 235 breast lesions (hereinafter, “targets”) subjected to second-look US following MRI between January 2013 and September 2015. US was employed using the conventional methods, and this assessment measured the positional relationships of lesions with regard to surrounding anatomical breast structures (glandular pattern, Cooper’s ligaments, adipose morphology, and vascular routes). Associations were assessed among the following variables: the MRI findings, target size, identification rate, and main US indicators that led to identifying the target; FNAC results and MRI findings; MRI findings and histopathological findings; and FNAC results and histopathological findings. Moreover, the sensitivity and specificity of FNAC were determined. Results The identification rate was 99%. The main US indicators leading to identification were a glandular pattern (28–30% of lesions) and other breast structures (~ 25% of lesions). FNAC was performed for 232 targets with the following results: sensitivity of 85.7%, specificity of 91.6%, PPV of 94.1%, NPV of 92.9%, false-negative rate of 14.3%, false-positive rate of 2.1%, and accuracy of 89.7%. Conclusions Second-look US using anatomical breast structures as indicators and US-guided FNAC are useful for refining the diagnosis of suspicious breast lesions detected using MRI.

The use of sentinel node biopsy (SNB) following neoadjuvant chemotherapy (NAC) for patients with cN1 breast cancer is controversial. Improvements of negative predictive value (NPV) by axillary ultrasound (AUS), which corresponds to the accurate prediction rate of node-negative status after NAC, would lead to decreased FNR of SNB following NAC. In this study, we retrospectively investigated the accurate prediction rate of NPV by AUS after NAC in patients with cytologically node-positive breast cancer treated between January 2012 and December 2016. Of 279 eligible patients, the NPV was 49.2% in all patients, but varied significantly by tumor subtype (p p = 0.0003). Of the 23 patients with clinically node negative (ycN0) by AUS and clinical complete response in primary lesion by MRI, the NPV was 100% in patients with HR±/HER2+ or HR−/HER2− breast cancer. In conclusion, regarding FNR reduction post-NAC, it will be of clinical value to take tumor subtype and primary tumor response using MRI into account to identify patients for SNB after NAC.

Purpose To evaluate the association between tumor shrinkage patterns shown with magnetic resonance (MR) imaging during neoadjuvant chemotherapy (NAC) and prognosis in patients with low-grade luminal breast cancer. Materials and Methods This retrospective study was approved by the institutional review board and informed consent was obtained from all subjects. The low-grade luminal breast cancer was defined as hormone receptor-positive and human epidermal growth factor receptor 2-negative with nuclear grades 1 or 2. The patterns of tumor shrinkage as revealed at MR imaging were categorized into two types: concentric shrinkage (CS) and non-CS. Among 854 patients who had received NAC in a single institution from January 2000 to December 2009, 183 patients with low-grade luminal breast cancer were retrospectively evaluated for the development set. Another data set from 292 patients who had received NAC in the same institution between January 2010 and December 2012 was used for the validation set. Among these 292 patients, 121 patients with low-grade luminal breast cancer were retrospectively evaluated. Results In the development set, the median observation period was 67.9 months. Recurrence was observed in 31 patients, and 16 deaths were related to breast cancer. There were statistically significant differences in both the disease-free survival (DFS) and overall survival (OS) rates between patterns of tumor shrinkage (P < .001 and P < .001, respectively). Multivariate analysis demonstrated that the CS pattern had the only significant independent association with DFS (P = .001) and OS (P = .009) rate. In the validation set, the median follow-up period was 56.9 months. Recurrence was observed in 20 patients (16.5%) and eight (6.6%) deaths were related to breast cancer. DFS rate was significantly longer in patients with the CS pattern (72.8 months; 95% confidence interval [CI]: 69.9, 75.6 months) than in those with the non-CS pattern (56.0 months; 95% CI: 49.1, 62.9 months; P ≤ .001). The CS pattern was associated with an excellent prognosis (median OS, 80.6 months; 95% CI: 79.3, 81.8 months vs 65.0 months; 95% CI: 60.1, 69.8 months; P = .004). Multivariate analysis demonstrated that the CS pattern had the only significant independent association with DFS (P = .007) and OS (P = .037) rates. Conclusion The CS pattern as revealed at MR imaging during NAC had the only significant independent association with prognosis in patients with low-grade luminal breast cancer. © RSNA, 2017.

This paper presents a method of segmenting mammary gland tissue for breast ultrasound images. In order to improve the quality of diagnoses for breast ultrasound screening, Computer Aided Detection (CADe) systems have been developed by a number of researchers. However, such systems suffer in terms of making many false positives for non-mammary gland tissue where no cancer exists. Accordingly, it is necessary to accurately determine the area of mammary gland tissue within ultrasound images for CADe systems. In this study, we propose a method of segmenting mammary gland tissue from ultrasound images for breasts. Within ultrasound images, brightness is attenuated as ultrasound waves move from the ultrasound probe to internal tissue. Accordingly, we correct intensity (brightness) using zero-phase component analysis (ZCA) whitening in order to reduce such brightness attenuation. Moreover, we apply a Conditional Random Field (CRF) which can render as smooth the region boundary of ultrasound images. The results of an experiment, conducted with five target patients to verify the effectiveness of the proposed methods, demonstrate that the method is capable of segmenting mammary gland tissue from breast ultrasound images.