Explore the vast range of titles available.

PurposeGiven that a pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) is an important prognostic factor, evaluating pretreatment imaging findings is important. Outcomes for triple negative breast cancer (TNBC) vary with the histological classification, indicating that this classification is clinically significant. In this study, we focus on the most common histological subtype of TNBC, invasive carcinoma of no special type (NST), to evaluate whether intramammary edema (intra-E) and intratumoral necrosis (intra-N) on T2-weighted magnetic resonance imaging (T2WI) is a useful predictor of pCR.MethodWe retrospectively included patients with biopsy-diagnosed TNBC-NST who received NAC between January 2014 and December 2017. Intra-E and intra-N were evaluated on T2WI before NAC. We grouped intra-E into no edema, peritumoral edema, prepectoral edema, and subcutaneous edema, and we defined intra-N as water-like signal intensity without enhancement on T2WI. We also evaluated tumor size, Ki-67 expression, and histological/nuclear grade, as well as their correlation with intra-E and intra-N.ResultsFifty-seven patients with TNBC-NST were enrolled. There was no correlation with the rate of pCR and the presence of either intra-E or intra-N before NAC. Only intra-E and tumor size showed a positive correlation.ConclusionsIn patients with TNBC-NST, intra-E and intra-N did not correlate with pCR, but intra-E did positively correlate with tumor size. NST may exhibit a greater response to NAC, regardless of whether intra-E or intra-N is present or not on the pretreatment MRI.Key Points• Pathological complete response in TNBC-NST had no correlation with intramammary edema or intratumoral necrosis.• NAC may be justified in TNBC-NST even in the presence of edema or necrosis.• The extension of edema correlated with tumor size of TNBC-NST.

Noninvasive measurement of the distribution and oxygenation state of hemoglobin (Hb) inside the tissue is strongly required to analyze the tumor-associated vasculatures. We developed a photoacoustic imaging (PAI) system with a hemispherical-shaped detector array (HDA). Here, we show that PAI system with HDA revealed finer vasculature, more detailed blood-vessel branching structures, and more detailed morphological vessel characteristics compared with MRI by the use of breast shape deformation of MRI to PAI and their fused image. Morphologically abnormal peritumoral blood vessel features, including centripetal photoacoustic signals and disruption or narrowing of vessel signals, were observed and intratumoral signals were detected by PAI in breast cancer tissues as a result of the clinical study of 22 malignant cases. Interestingly, it was also possible to analyze anticancer treatment-driven changes in vascular morphological features and function, such as improvement of intratumoral blood perfusion and relevant changes in intravascular hemoglobin saturation of oxygen. This clinical study indicated that PAI appears to be a promising tool for noninvasive analysis of human blood vessels and may contribute to improve cancer diagnosis.

Conventional breast cancer detection techniques including X-ray mammography, magnetic resonance imaging, and ultrasound scanning suffer from shortcomings such as excessive cost, harmful radiation, and inconveniences to the patients. These challenges motivated researchers to investigate alternative methods including the use of microwaves. This article focuses on reviewing the background of microwave techniques for breast tumour detection. In particular, this study reviews the recent advancements in active microwave imaging, namely microwave tomography and radar-based techniques. The main objective of this paper is to provide researchers and physicians with an overview of the principles, techniques, and fundamental challenges associated with microwave imaging for breast cancer detection. Furthermore, this study aims to shed light on the fact that until today, there are very few commercially available and cost-effective microwave-based systems for breast cancer imaging or detection. This conclusion is not intended to imply the inefficacy of microwaves for breast cancer detection, but rather to encourage a healthy debate on why a commercially available system has yet to be made available despite almost 30 years of intensive research.

PurposeTo compare annotation segmentation approaches and to assess the value of radiomics analysis applied to diffusion-weighted imaging (DWI) for evaluation of breast cancer receptor status and molecular subtyping.ProceduresIn this IRB-approved HIPAA-compliant retrospective study, 91 patients with treatment-naïve breast malignancies proven by image-guided breast biopsy, (luminal A, n = 49; luminal B, n = 8; human epidermal growth factor receptor 2 [HER2]-enriched, n = 11; triple negative [TN], n = 23) underwent multiparametric magnetic resonance imaging (MRI) of the breast at 3 T with dynamic contrast-enhanced MRI, T2-weighted and DW imaging. Lesions were manually segmented on high b-value DW images and segmentation ROIS were propagated to apparent diffusion coefficient (ADC) maps. In addition in a subgroup (n = 79) where lesions were discernable on ADC maps alone, these were also directly segmented there. To derive radiomics signatures, the following features were extracted and analyzed: first-order histogram (HIS), co-occurrence matrix (COM), run-length matrix (RLM), absolute gradient, autoregressive model (ARM), discrete Haar wavelet transform (WAV), and lesion geometry. Fisher, probability of error and average correlation, and mutual information coefficients were used for feature selection. Linear discriminant analysis followed by k-nearest neighbor classification with leave-one-out cross-validation was applied for pairwise differentiation of receptor status and molecular subtyping. Histopathologic results were considered the gold standard.ResultsFor lesion that were segmented on DWI and segmentation ROIs were propagated to ADC maps the following classification accuracies > 90% were obtained: luminal B vs. HER2-enriched, 94.7 % (based on COM features); luminal B vs. others, 92.3 % (COM, HIS); and HER2-enriched vs. others, 90.1 % (RLM, COM). For lesions that were segmented directly on ADC maps, better results were achieved yielding the following classification accuracies: luminal B vs. HER2-enriched, 100 % (COM, WAV); luminal A vs. luminal B, 91.5 % (COM, WAV); and luminal B vs. others, 91.1 % (WAV, ARM, COM).ConclusionsRadiomic signatures from DWI with ADC mapping allows evaluation of breast cancer receptor status and molecular subtyping with high diagnostic accuracy. Better classification accuracies were obtained when breast tumor segmentations could be performed on ADC maps.

Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a rapid, unenhanced imaging technique that measures the motion of water molecules within tissues and provides information regarding the cell density and tissue microstructure. DW MRI has demonstrated the potential to improve the specificity of breast MRI, facilitate the evaluation of tumor response to neoadjuvant chemotherapy and can be employed in unenhanced MRI screening. However, standardization of the acquisition and interpretation of DW MRI is challenging. Recently, the European Society of Breast Radiology issued a consensus statement, which described the acquisition parameters and interpretation of DW MRI. The current article describes the basic principles, standardized acquisition protocols and interpretation guidelines, and the clinical applications of DW MRI in breast imaging.

PurposeWe aimed to use deep learning with convolutional neural network (CNN) to discriminate between benign and malignant breast mass images from ultrasound.Materials and MethodsWe retrospectively gathered 480 images of 96 benign masses and 467 images of 144 malignant masses for training data. Deep learning model was constructed using CNN architecture GoogLeNet and analyzed test data: 48 benign masses, 72 malignant masses. Three radiologists interpreted these test data. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were calculated.ResultsThe CNN model and radiologists had a sensitivity of 0.958 and 0.583–0.917, specificity of 0.925 and 0.604–0.771, and accuracy of 0.925 and 0.658–0.792, respectively. The CNN model had equal or better diagnostic performance compared to radiologists (AUC = 0.913 and 0.728–0.845, p = 0.01–0.14).ConclusionDeep learning with CNN shows high diagnostic performance to discriminate between benign and malignant breast masses on ultrasound.

PurposeTo assess the additional value of quantitative tCho evaluation to diagnose malignancy and lymph node metastases in suspicious lesions on multiparametric breast MRI (mpMRI, BI-RADS 4, and BI-RADS 5).MethodsOne hundred twenty-one patients that demonstrated suspicious multiparametric breast MRI lesions using DCE, T2w, and diffusion-weighted (DW) images were prospectively enrolled in this IRB-approved study. All underwent single-voxel proton MR spectroscopy (1H-MRS, point-resolved spectroscopy sequence, TR 2000 ms, TE 272 ms) with and without water suppression. The total choline (tCho) amplitude was measured and normalized to millimoles/liter according to established methodology by two independent readers (R1, R2). ROC-analysis was employed to predict malignancy and lymph node status by tCho results.ResultsOne hundred three patients with 74 malignant and 29 benign lesions had full 1H-MRS data. The area under the ROC curve (AUC) for prediction of malignancy was 0.816 (R1) and 0.809 (R2). A cutoff of 0.8 mmol/l tCho could diagnose malignancy with a sensitivity of > 95%. For prediction of lymph node metastases, tCho measurements achieved an AUC of 0.760 (R1) and 0.788 (R2). At tCho levels < 2.4 mmol/l, no metastatic lymph nodes were found.ConclusionQuantitative tCho evaluation from 1H-MRS allowed diagnose malignancy and lymph node status in breast lesions suspicious on multiparametric breast MRI. tCho therefore demonstrated the potential to downgrade suspicious mpMRI lesions and stratify the risk of lymph node metastases for improved patient management.Key Points• Quantitative tCho evaluation can distinguish benign from malignant breast lesions suspicious after multiparametric MRI assessment.• Quantitative tCho levels are associated with lymph node status in breast cancer.• Quantitative tCho levels are higher in hormonal receptor positive compared to hormonal receptor negative lesions.

To investigate whether a computer-aided diagnosis (CAD) system based on a deep learning framework (deep learning-based CAD) improves the diagnostic performance of radiologists in differentiating between malignant and benign masses on breast ultrasound (US). B-mode US images were prospectively obtained for 253 breast masses (173 benign, 80 malignant) in 226 consecutive patients. Breast mass US findings were retrospectively analyzed by deep learning-based CAD and four radiologists. In predicting malignancy, the CAD results were dichotomized (possibly benign vs. possibly malignant). The radiologists independently assessed Breast Imaging Reporting and Data System final assessments for two datasets (US images alone or with CAD). For each dataset, the radiologists' final assessments were classified as positive (category 4a or higher) and negative (category 3 or lower). The diagnostic performances of the radiologists for the two datasets (US alone vs. US with CAD) were compared. When the CAD results were added to the US images, the radiologists showed significant improvement in specificity (range of all radiologists for US alone vs. US with CAD: 72.8-92.5% vs. 82.1-93.1%; < 0.001), accuracy (77.9-88.9% vs. 86.2-90.9%; = 0.038), and positive predictive value (PPV) (60.2-83.3% vs. 70.4-85.2%; = 0.001). However, there were no significant changes in sensitivity (81.3-88.8% vs. 86.3-95.0%; = 0.120) and negative predictive value (91.4-93.5% vs. 92.9-97.3%; = 0.259). Deep learning-based CAD could improve radiologists' diagnostic performance by increasing their specificity, accuracy, and PPV in differentiating between malignant and benign masses on breast US.

To investigate the value of ultrasound (US) microflow assessment in distinguishing malignant from benign solid breast masses as well as the association between US parameters and histologic microvessel density (MVD). Ninety-eight breast masses (57 benign and 41 malignant) were examined using Superb Microvascular Imaging (SMI) and contrast-enhanced US (CEUS) before biopsy. Two radiologists evaluated the quantitative and qualitative vascular parameters on SMI (vascular index, morphology, distribution, and penetration) and CEUS (time-intensity curve analysis and enhancement characteristics). US parameters were compared between benign and malignant masses and the diagnostic performance was compared between SMI and CEUS. Subgroup analysis was performed according to lesion size. The effect of vascular parameters on downgrading Breast Imaging Reporting and Data System (BI-RADS) category 4A masses was evaluated. The association between histologic MVD and US parameters was analyzed. Malignant masses were associated with a higher vascular index (15.1 ± 7.3 vs. 5.9 ± 5.6), complex vessel morphology (82.9% vs. 42.1%), central vascularity (95.1% vs. 59.6%), penetrating vessels (80.5% vs. 31.6%) on SMI (all, < 0.001), as well as higher peak intensity (37.1 ± 25.7 vs. 17.0 ± 15.8, < 0.001), slope (10.6 ± 11.2 vs. 3.9 ± 4.2, = 0.001), area (1035.7 ± 726.9 vs. 458.2 ± 410.2, < 0.001), hyperenhancement (95.1% vs. 70.2%, = 0.005), centripetal enhancement (70.7% vs. 45.6%, = 0.023), penetrating vessels (65.9% vs. 22.8%, < 0.001), and perfusion defects (31.7% vs. 3.5%, < 0.001) on CEUS ( ≤ 0.023). The areas under the receiver operating characteristic curve (AUCs) of SMI and CEUS were 0.853 and 0.841, respectively ( = 0.803). In 19 masses measuring < 10 mm, central vascularity on SMI was associated with malignancy (100% vs. 38.5%, = 0.018). Considering all benign SMI parameters on the BI-RADS assessment, unnecessary biopsies could be avoided in 12 category 4A masses with improved AUCs (0.500 vs. 0.605, < 0.001). US vascular parameters associated with malignancy showed higher MVD ( ≤ 0.016). MVD was higher in malignant masses than in benign masses, and malignant masses negative for estrogen receptor or positive for Ki67 had higher MVD ( < 0.05). US microflow assessment using SMI and CEUS is valuable in distinguishing malignant from benign solid breast masses, and US vascular parameters are associated with histologic MVD.