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Purpose To investigate whether radiomics features extracted from magnetic resonance imaging (MRI) of patients with biopsy-proven atypical ductal hyperplasia (ADH) coupled with machine learning can differentiate high-risk lesions that will upgrade to malignancy at surgery from those that will not, and to determine if qualitatively and semi-quantitatively assessed imaging features, clinical factors, and image-guided biopsy technical factors are associated with upgrade rate. Methods This retrospective study included 127 patients with 139 breast lesions yielding ADH at biopsy who were assessed with multiparametric MRI prior to biopsy. Two radiologists assessed all lesions independently and with a third reader in consensus according to the BI-RADS lexicon. Univariate analysis and multivariate modeling were performed to identify significant radiomic features to be included in a machine learning model to discriminate between lesions that upgraded to malignancy on surgery from those that did not. Results Of 139 lesions, 28 were upgraded to malignancy at surgery, while 111 were not upgraded. Diagnostic accuracy was 53.6%, specificity 79.2%, and sensitivity 15.3% for the model developed from pre-contrast features, and 60.7%, 86%, and 22.8% for the model developed from delta radiomics datasets. No significant associations were found between any radiologist-assessed lesion parameters and upgrade status. There was a significant correlation between the number of specimens sampled during biopsy and upgrade status (p = 0.003). Conclusion Radiomics analysis coupled with machine learning did not predict upgrade status of ADH. The only significant result from this analysis is between the number of specimens sampled during biopsy procedure and upgrade status at surgery.

ObjectivesTo assess DWI for tumor visibility and breast cancer detection by the addition of different synthetic b-values.MethodsEighty-four consecutive women who underwent a breast-multiparametric-MRI (mpMRI) with enhancing lesions on DCE-MRI (BI-RADS 2–5) were included in this IRB-approved retrospective study from September 2018 to March 2019. Three readers evaluated DW acquired b-800 and synthetic b-1000, b-1200, b-1500, and b-1800 s/mm2images for lesion visibility and preferred b-value based on lesion conspicuity. Image quality (1–3 scores) and breast composition (BI-RADS) were also recorded. Diagnostic parameters for DWI were determined using a 1–5 malignancy score based on qualitative imaging parameters (acquired + preferred synthetic b-values) and ADC values. BI-RADS classification was used for DCE-MRI and quantitative ADC values + BI-RADS were used for mpMRI.ResultsSixty-four malignant (average = 23 mm) and 39 benign (average = 8 mm) lesions were found in 80 women. Although b-800 achieved the best image quality score, synthetic b-values 1200–1500 s/mm2were preferred for lesion conspicuity, especially in dense breast. b-800 and synthetic b-1000/b-1200 s/mm2values allowed the visualization of 84–90% of cancers visible with DCE-MRI performing better than b-1500/b-1800 s/mm2. DWI was more specific (86.3% vs 65.7%,p< 0.001) but less sensitive (62.8% vs 90%,p< 0.001) and accurate (71% vs 80.7%,p= 0.003) than DCE-MRI for breast cancer detection, where mpMRI was the most accurate modality accounting for less false positive cases.ConclusionThe addition of synthetic b-values enhances tumor conspicuity and could potentially improve tumor visualization particularly in dense breast. However, its supportive role for DWI breast cancer detection is still not definite.Key Points•The addition of synthetic b-values (1200–1500 s/mm2) to acquired DWI afforded a better lesion conspicuity without increasing acquisition time and was particularly useful in dense breasts.•Despite the use of synthetic b-values, DWI was less sensitive and accurate than DCE-MRI for breast cancer detection.•A multiparametric MRI modality still remains the best approach having the highest accuracy for breast cancer detection and thus reducing the number of unnecessary biopsies.

Background To investigate if baseline and/or changes in contralateral background parenchymal enhancement (BPE) and fibroglandular tissue (FGT) measured on magnetic resonance imaging (MRI) and mammographic breast density (MD) can be used as imaging biomarkers for overall and recurrence-free survival in patients with invasive lobular carcinomas (ILCs) undergoing adjuvant endocrine treatment. Methods Women who fulfilled the following inclusion criteria were included in this retrospective HIPAA-compliant IRB-approved study: unilateral ILC, pre-treatment breast MRI and/or mammography from 2000 to 2010, adjuvant endocrine treatment, follow-up MRI, and/or mammography 1–2 years after treatment onset. BPE, FGT, and mammographic MD of the contralateral breast were independently graded by four dedicated breast radiologists according to BI-RADS. Associations between the baseline levels and change in levels of BPE, FGT, and MD with overall survival and recurrence-free survival were assessed using Kaplan–Meier survival curves and Cox regression analysis. Results Two hundred ninety-eight patients (average age = 54.1 years, range = 31–79) fulfilled the inclusion criteria. The average follow-up duration was 11.8 years (range = 2–19). Baseline and change in levels of BPE, FGT, and MD were not significantly associated with recurrence-free or overall survival. Recurrence-free and overall survival were affected by histological subtype (p p p = 0.01), and adjuvant endocrine treatment duration (p Conclusions Qualitative evaluation of BPE, FGT, and mammographic MD changes cannot predict which patients are more likely to benefit from adjuvant endocrine treatment.

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.

Multiple synchronous (multifocal or multicentric) ipsilateral breast cancers with heterogeneous histopathology are a rare clinical occurrence, however, their incidence is increasing due to the use of MRI for breast cancer screening and staging. Some studies have demonstrated poorer clinical outcomes for this pattern of breast cancer, but there is no evidence to guide clinical practice. In this multidisciplinary review, we reflect on pathology and molecular characteristics, imaging findings, surgical management including conservation and reconstructive options and approach to the axilla, and the role of chemotherapy and radiotherapy. Multidisciplinary discussions appear decisive in planning an appropriate surgical choice and defining the correct systemic treatment tailored to each clinical condition.

In patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy (NAC), some patients achieve a complete pathologic response (pCR), some achieve a partial response, and some do not respond at all or even progress. Accurate prediction of treatment response has the potential to improve patient care by improving prognostication, enabling de-escalation of toxic treatment that has little benefit, facilitating upfront use of novel targeted therapies, and avoiding delays to surgery. Visual inspection of a patient’s tumor on multiparametric MRI is insufficient to predict that patient’s response to NAC. However, machine learning and deep learning approaches using a mix of qualitative and quantitative MRI features have recently been applied to predict treatment response early in the course of or even before the start of NAC. This is a novel field but the data published so far has shown promising results. We provide an overview of the machine learning and deep learning models developed to date, as well as discuss some of the challenges to clinical implementation., Highlights • Machine and deep learning have shown potential in predicting neoadjuvant treatment outcomes using multiparametric MRI data. • Machine and deep learning-based early identification of chemotherapy non-responders could improve patient management. • Deep learning techniques using CNN may prove to be more powerful and more robust than traditional machine learning classifiers.

This multicenter retrospective study compared the performance of radiomics analysis coupled with machine learning (ML) with that of radiologists for the classification of breast tumors. A total of 93 consecutive women (mean age: 49 ± 12 years) with 104 histopathologically verified enhancing lesions (mean size: 22.8 ± 15.1 mm), classified as suspicious on multiparametric breast MRIs were included. Two experienced breast radiologists assessed all of the lesions, assigning a Breast Imaging Reporting and Database System (BI-RADS) suspicion category, providing a diffusion-weighted imaging (DWI) score based on lesion signal intensity, and determining the apparent diffusion coefficient (ADC). Ten predictive models for breast lesion discrimination were generated using radiomic features extracted from the multiparametric MRI. The area under the receiver operating curve (AUC) and the accuracy were compared using McNemar’s test. Multiparametric radiomics with DWI score and BI-RADS (accuracy = 88.5%; AUC = 0.93) and multiparametric radiomics with ADC values and BI-RADS (accuracy= 88.5%; AUC = 0.96) models showed significant improvements in diagnostic accuracy compared to the multiparametric radiomics (DWI + DCE data) model (p = 0.01 and p = 0.02, respectively), but performed similarly compared to the multiparametric assessment by radiologists (accuracy = 85.6%; AUC = 0.03; p = 0.39). In conclusion, radiomics analysis coupled with the ML of multiparametric MRI could assist in breast lesion discrimination, especially for less experienced readers of breast MRIs.

Purpose: To develop a deep network architecture that would achieve fully automated radiologist-level segmentation of cancers at breast MRI. Materials and Methods: In this retrospective study, 38229 examinations (composed of 64063 individual breast scans from 14475 patients) were performed in female patients (age range, 12-94 years; mean age, 52 years +/- 10 [standard deviation]) who presented between 2002 and 2014 at a single clinical site. A total of 2555 breast cancers were selected that had been segmented on two-dimensional (2D) images by radiologists, as well as 60108 benign breasts that served as examples of noncancerous tissue; all these were used for model training. For testing, an additional 250 breast cancers were segmented independently on 2D images by four radiologists. Authors selected among several three-dimensional (3D) deep convolutional neural network architectures, input modalities, and harmonization methods. The outcome measure was the Dice score for 2D segmentation, which was compared between the network and radiologists by using the Wilcoxon signed rank test and the two one-sided test procedure. Results: The highest-performing network on the training set was a 3D U-Net with dynamic contrast-enhanced MRI as input and with intensity normalized for each examination. In the test set, the median Dice score of this network was 0.77 (interquartile range, 0.26). The performance of the network was equivalent to that of the radiologists (two one-sided test procedures with radiologist performance of 0.69-0.84 as equivalence bounds, P

To perform a survey among all European Society of Breast Imaging (EUSOBI) radiologist members to gather representative data regarding the clinical use of breast DWI.An online questionnaire was developed by two board-certified radiologists, reviewed by the EUSOBI board and committees, and finally distributed among EUSOBI active and associated (not based in Europe) radiologist members. The questionnaire included 20 questions pertaining to technical preferences (acquisition time, magnet strength, breast coils, number of b values), clinical indications, imaging evaluation, and reporting. Data were analyzed using descriptive statistics, the Chi-square test of independence, and Fisher's exact test.Of 1411 EUSOBI radiologist members, 275/1411 (19.5%) responded. Most (222/275, 81%) reported using DWI as part of their routine protocol. Common indications for DWI include lesion characterization (using an ADC threshold of 1.2-1.3 × 10The utilization of DWI in clinical practice among EUSOBI radiologists who responded to the survey is generally in line with international recommendations, with the main application being the differentiation of benign and malignant enhancing lesions, treatment response assessment, and prediction of response to chemotherapy. Report integration of qualitative and quantitative DWI data is not uniform.• Clinical performance of breast DWI is in good agreement with the current recommendations of the EUSOBI International Breast DWI working group. • Breast DWI applications in clinical practice include the differentiation of benign and malignant enhancing, treatment response assessment, and prediction of response to chemotherapy. • Report integration of DWI results is not uniform.

Simple Summary To our knowledge, this is the first study assessing radiomics coupled with machine learning from MRI-derived features to predict PD-L1 expression status in biopsy-proven triple negative breast cancers and comparing the performance of this approach with the performance of qualitative assessment by two radiologists. This pilot study shows that radiomics analysis coupled with machine learning of DCE-MRI is a promising approach to derive prognostic and predictive information and to select patients who could benefit from anti-PD-1/PD-L1 treatment. This technique could also be used to monitor PD-L1 expression, as it can vary over time and between different regions of the tumor, thus avoiding repeated biopsies. Abstract The purpose of this retrospective study was to assess whether radiomics analysis coupled with machine learning (ML) based on standard-of-care dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can predict PD-L1 expression status in patients with triple negative breast cancer, and to compare the performance of this approach with radiologist review. Patients with biopsy-proven triple negative breast cancer who underwent pre-treatment breast MRI and whose PD-L1 status was available were included. Following 3D tumor segmentation and extraction of radiomic features, radiomic features with significant differences between PD-L1+ and PD-L1− patients were determined, and a final predictive model to predict PD-L1 status was developed using a coarse decision tree and five-fold cross-validation. Separately, all lesions were qualitatively assessed by two radiologists independently according to the BI-RADS lexicon. Of 62 women (mean age 47, range 31–81), 27 had PD-L1− tumors and 35 had PD-L1+ tumors. The final radiomics model to predict PD-L1 status utilized three MRI parameters, i.e., variance (FO), run length variance (RLM), and large zone low grey level emphasis (LZLGLE), for a sensitivity of 90.7%, specificity of 85.1%, and diagnostic accuracy of 88.2%. There were no significant associations between qualitative assessed DCE-MRI imaging features and PD-L1 status. Thus, radiomics analysis coupled with ML based on standard-of-care DCE-MRI is a promising approach to derive prognostic and predictive information and to select patients who could benefit from anti-PD-1/PD-L1 treatment.

To investigate the diagnostic value of multiparametric MRI (mpMRI) including dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) in non-mass enhancing breast tumors.Patients who underwent mpMRI, who were diagnosed with a suspicious non-mass enhancement (NME) on DCE-MRI (BI-RADS 4/5), and who subsequently underwent image-guided biopsy were retrospectively included. Two radiologists independently evaluated all NMEs, on both DCE-MR images and high-b-value DW images. Different mpMRI reading approaches were evaluated: 1) with a fixed apparent diffusion coefficient (ADC) threshold (1.3 malignant, ≥1.3 benign) based on the recommendation by the European Society of Breast Imaging (EUSOBI); 2) with a fixed ADC threshold (1.5 malignant, ≥1.5 benign) based on recently published trial data; 3) with an ADC threshold adapted to the assigned BI-RADS classification using a previously published reading method; and 4) with individually determined best thresholds for each reader.The final study sample consisted of 66 lesions in 66 patients. DCE-MRI alone had the highest sensitivity for breast cancer detection (94.8-100 %), outperforming all mpMRI reading approaches (R1 74.4-87.1 %, R2 71.7-94.8 %) and DWI alone (R1 74.4 %, R2 79.4 %). The adapted approach achieved the best specificity for both readers (85.1 %), resulting in the best diagnostic accuracy for R1 (86.5 %) but a moderate diagnostic accuracy for R2 (77.2 %).mpMRI has limited added diagnostic value to DCE-MRI in the assessment of NME.

Purpose Non-specific lymphadenopathy is increasingly being reported especially given the COVID-19 vaccination campaign and is a diagnostic dilemma especially in oncology patients. The purpose of this study was to evaluate the diagnostic accuracy and discordance rate between fine-needle aspiration (FNA) cytology and flow cytometry (FC) immunophenotyping in axillary FNA in patients with morphologically abnormal axillary lymph nodes on imaging and no concurrent diagnosis of primary breast malignancy. Methods This retrospective study included 222 patients who underwent screening or diagnostic axillary ultrasound that yielded suspicious lymphadenopathy without concurrent or recent prior diagnosis of breast cancer and who had subsequent image-guided axillary FNA and FC. Diagnostic accuracy, sensitivity, specificity, and positive and negative predictive value (PPV and NPV) were reported for FNA with cytology alone, and FC alone, and in combination. Discordance rate between FNA cytology and FC was assessed. Discordant cases were evaluated with histology or clinical and imaging follow-up. Results Diagnostic sensitivity, specificity, PPV, NPV, and diagnostic accuracy were 88%, 92%, 77%, 96%, and 91%, for FNA alone, 98%, 98%, 92%, 99%, and 98% for FC alone, and 100%, 92%, 79%, 100%, and 94% when combined. The overall discordance rate between FNA and FC was 7% (16/222). 7/16 (44%) patients with discordant results were diagnosed with lymphoma, while 9/16 (56%) patients with discordant results had benign findings. Conclusion With a diagnostic accuracy of 91%, FNA with cytology is sufficient to screen patients with indeterminate and incidental lymphadenopathy. Flow cytometry could be initially deferred in patients with low pretest probability of lymphoma.

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.

Diffusion-weighted imaging is a non-invasive functional imaging modality for breast tumor characterization through apparent diffusion coefficients. Yet, it has so far been unable to intuitively inform on tissue microstructure. In this IRB-approved prospective study, we applied novel multidimensional diffusion (MDD) encoding across 16 patients with suspected breast cancer to evaluate its potential for tissue characterization in the clinical setting. Data acquired via custom MDD sequences was processed using an algorithm estimating non-parametric diffusion tensor distributions. The statistical descriptors of these distributions allow us to quantify tissue composition in terms of metrics informing on cell densities, shapes, and orientations. Additionally, signal fractions from specific cell types, such as elongated cells (bin1), isotropic cells (bin2), and free water (bin3), were teased apart. Histogram analysis in cancers and healthy breast tissue showed that cancers exhibited lower mean values of “size” (1.43 ± 0.54 × 10−3 mm2/s) and higher mean values of “shape” (0.47 ± 0.15) corresponding to bin1, while FGT (fibroglandular breast tissue) presented higher mean values of “size” (2.33 ± 0.22 × 10−3 mm2/s) and lower mean values of “shape” (0.27 ± 0.11) corresponding to bin3 (p &lt, 0.001). Invasive carcinomas showed significant differences in mean signal fractions from bin1 (0.64 ± 0.13 vs. 0.4 ± 0.25) and bin3 (0.18 ± 0.08 vs. 0.42 ± 0.21) compared to ductal carcinomas in situ (DCIS) and invasive carcinomas with associated DCIS (p = 0.03). MDD enabled qualitative and quantitative evaluation of the composition of breast cancers and healthy glands.

Breast fibromatosis, also referred to as desmoid tumor or aggressive fibromatosis, is a very rare, locally aggressive disease that does not metastasize. Bilateral lesions are extremely rare and are found in only 4% of patients with breast fibromatosis. Tumor recurrence following surgery occurs in 18%-29% of patients, most often within the first 2 years after surgery. In this report, we discuss a case of breast fibromatosis, mimicking a breast carcinoma both clinically and radiologically, that presented clinically with dimpling of the skin of the left breast in a 31-year-old woman. The patient relapsed a few months after surgery, with a multicentric and bilateral disease.

Significant advances in imaging analysis and the development of high-throughput methods that can extract and correlate multiple imaging parameters with different clinical outcomes have led to a new direction in medical research. Radiomics and artificial intelligence (AI) studies are rapidly evolving and have many potential applications in breast imaging, such as breast cancer risk prediction, lesion detection and classification, radiogenomics, and prediction of treatment response and clinical outcomes. AI has been applied to different breast imaging modalities, including mammography, ultrasound, and magnetic resonance imaging, in different clinical scenarios. The application of AI tools in breast imaging has an unprecedented opportunity to better derive clinical value from imaging data and reshape the way we care for our patients. The aim of this study is to review the current knowledge and future applications of AI-enhanced breast imaging in clinical practice.

Cardiothoracic diseases result in substantial morbidity and mortality. Chest computed tomography (CT) has been an imaging modality of choice for assessing a host of chest diseases, and technologic advances have enabled the emergence of coronary CT angiography as a robust noninvasive test for cardiac imaging. Technologic developments in CT have also enabled the application of dual-energy CT scanning for assessing pulmonary vascular and neoplastic processes. Concerns over increasing radiation dose from CT scanning are being addressed with introduction of more dose-efficient wide-area detector arrays and iterative reconstruction techniques. This review article discusses the technologic innovations in CT and their effect on cardiothoracic applications.

To assess the role of radiomic features in distinguishing squamous and adenocarcinoma subtypes of nonsmall cell lung cancers (NSCLC) and predict EGFR mutations. Institution Review Board-approved study included chest CT scans of 93 consecutive patients (43 men, 50 women, mean age 60 ± 11 years) with biopsy-proven squamous and adenocarcinoma lung cancers greater than 1 cm. All cancers were evaluated for epidermal growth factor receptor (EGFR) mutation. The clinical parameters such as age, sex, and smoking history and standard morphology-based CT imaging features such as target lesion longest diameter (LD), longest perpendicular diameter (LPD), density, and presence of cavity were recorded. The radiomics data was obtained using commercial CT texture analysis (CTTA) software. The CTTA was performed on a single image of the dominant lung lesion. The predictive value of clinical history, standard imaging features, and radiomics was assessed with multivariable logistic regression and receiver operating characteristic (ROC) analyses. Between adenocarcinoma and squamous cell carcinomas, ROC analysis showed significant difference in 3/11 radiomic features (entropy, normalized SD, total) [AUC 0.686–0.744, P = .006 to

Background To use clinical and MRI radiomic features coupled with machine learning to assess HER2 expression level and predict pathologic response (pCR) in HER2 overexpressing breast cancer patients receiving neoadjuvant chemotherapy (NAC). Methods This retrospective study included 311 patients. pCR was defined as no residual invasive carcinoma in the breast or axillary lymph nodes (ypT0/isN0). Radiomics/statistical analysis was performed using MATLAB and CERR software. After ROC and correlation analysis, selected radiomics parameters were advanced to machine learning modelling alongside clinical MRI-based parameters (lesion type, multifocality, size, nodal status). For predicting pCR, the data was split into a training and test set (80:20). Findings The overall pCR rate was 60.5% (188/311). The final model to predict HER2 heterogeneity utilised three MRI parameters (two clinical, one radiomic) for a sensitivity of 99.3% (277/279), specificity of 81.3% (26/32), and diagnostic accuracy of 97.4% (303/311). The final model to predict pCR included six MRI parameters (two clinical, four radiomic) for a sensitivity of 86.5% (32/37), specificity of 80.0% (20/25), and diagnostic accuracy of 83.9% (52/62) (test set); these results were independent of age and ER status, and outperformed the best model developed using clinical parameters only (p=0.029, comparison of proportion Chi-squared test). Interpretation The machine learning models, including both clinical and radiomics MRI features, can be used to assess HER2 expression level and can predict pCR after NAC in HER2 overexpressing breast cancer patients. Funding NIH/NCI (P30CA008748), Susan G. Komen Foundation, Breast Cancer Research Foundation, Spanish Foundation Alfonso Martin Escudero, European School of Radiology.

Multishot multiplexed sensitivity-encoding diffusion-weighted imaging is a feasible and easily implementable routine breast MRI protocol that yields high-quality diffusion-weighted breast images. PURPOSE: To compare multiplexed sensitivity-encoding (MUSE) diffusion-weighted imaging (DWI) and single-shot DWI for lesion visibility and differentiation of malignant and benign lesions within the breast. MATERIALS AND METHODS: In this prospective institutional review board–approved study, both MUSE DWI and single-shot DWI sequences were first optimized in breast phantoms and then performed in a group of patients. Thirty women (mean age, 51.1 years ± 10.1 [standard deviation]; age range, 27–70 years) with 37 lesions were included in this study and underwent scanning using both techniques. Visual qualitative analysis of diffusion-weighted images was accomplished by two independent readers; images were assessed for lesion visibility, adequate fat suppression, and the presence of artifacts. Quantitative analysis was performed by calculating apparent diffusion coefficient (ADC) values and image quality parameters (signal-to-noise ratio [SNR] for lesions and fibroglandular tissue; contrast-to-noise ratio) by manually drawing regions of interest within the phantoms and breast tumor tissue. Interreader variability was determined using the Cohen κ coefficient, and quantitative differences between MUSE DWI and single-shot DWI were assessed using the Mann-Whitney U test; significance was defined at P < .05. RESULTS: MUSE DWI yielded significantly improved image quality compared with single-shot DWI in phantoms (SNR, P = .001) and participants (lesion SNR, P = .009; fibroglandular tissue SNR, P = .05; contrast-to-noise ratio, P = .008). MUSE DWI ADC values showed a significant difference between malignant and benign lesions (P < .001). No significant differences were found between MUSE DWI and single-shot DWI in the mean, maximum, and minimum ADC values (P = .96, P = .28, and P = .49, respectively). Visual qualitative analysis resulted in better lesion visibility for MUSE DWI over single-shot DWI (κ = 0.70). CONCLUSION: MUSE DWI is a promising high-spatial-resolution technique that may enhance breast MRI protocols without the need for contrast material administration in breast screening. Keywords: Breast, MR-Diffusion Weighted Imaging, Oncology Supplemental material is available for this article. © RSNA, 2020

Background There are "blind spots" on chest computed tomography (CT) where pulmonary nodules can easily be overlooked. The number of missed pulmonary nodules can be minimized by instituting a training program with particular focus on the depiction of nodules at blind spots. Purpose The purpose of this study was to assess the variation in lung nodule detection in chest CT based on location, attenuation characteristics, and reader experience. Materials and Methods We selected 18 noncalcified lung nodules (6-8 mm) suspicious of primary and metastatic lung cancer with solid (n = 7), pure ground-glass (6), and part-solid ground-glass (5) attenuation from 12 chest CT scans. These nodules were randomly inserted in chest CT of 34 patients in lung hila, 1st costochondral junction, branching vessels, paramediastinal lungs, lung apices, juxta-diaphragm, and middle and outer thirds of the lungs. Two residents and two chest imaging clinical fellows evaluated the CT images twice, over a 4-month interval. Before the second reading session, the readers were trained and made aware of the potential blind spots. Chi-square test was used to assess statistical significance. Results Pretraining session: Fellows detected significantly more part-solid ground-glass nodules compared to residents (P = 0.008). A substantial number of nodules adjacent to branching vessels and posterior mediastinum were missed. Posttraining session: There was a significant increase in detectability independent of attenuation and location of nodules for all readers (P < 0.0008). Conclusion Dedicated chest CT training improves detection of lung nodules, especially the part-solid ground-glass nodules. Detection of nodules adjacent to branching vessels and the posterior mediastinal lungs is difficult even for fellowship-trained radiologists.

The purpose of our study was to determine accuracy of CT texture analysis (CTTA) for differentiating benign from malignant pulmonary nodules, and well-differentiated from poorly differentiated lung cancers, with histology as the standard of reference. In this IRB-approved study, 175 adult patients (average age 66 ± 12 years; age range 27–89 years, male 82: female 93) who underwent a noncontrast chest CT examination prior to CT-guided biopsy of pulmonary nodules were included. There were 57 benign (24 tumors or tumor-like lesions; 33 inflammatory conditions) and 120 malignant (29 well-differentiated adenocarcinomas, 48 poorly differentiated adenocarcinomas, and 43 squamous cell carcinomas) diagnoses on pathology. CTTA was performed on the prebiopsy noncontrast CT images using a commercially available software (TexRAD limited, UK). The CTCA features analyzed included mean HU values, percent positive pixels (PPP), mean value of positive pixels (MPP), standard deviation (SD), normalized SD, skewness, kurtosis, and entropy. The ROC analyses showed that normalized SD [AUC: 0.63, (CI: 0.55–72), P = .003] had moderate accuracy for differentiating between benign and malignant lesions. For differentiating among well-differentiated and poorly differentiated tumors, the ROC analysis showed that except skewness all other parameters were statistically significant The AUC values of other CTTA parameters were: mean (AUC: 0.73–0.76, P = .001–

Purpose Assessment of body size at autopsy is important for interpreting organ weight measurements and in some cases body identification. The reliability of post-mortem body size measurements, the causes for perturbations in these measurements from their corresponding pre-mortem values, and the impact of such perturbations on heart weight interpretation have not been fully explored. Methods Autopsy body length and weight measurements and pre-mortem height and body weight measurements were compared in 132 autopsies. Clinical records were evaluated for peripheral edema and serum albumin levels. Causes of death, body cavity fluid collections, and heart weights were obtained from the autopsy reports. A subset of patients underwent quantitative post-mortem computed tomography assessment of anasarca. Results At autopsy, body weight differed from the pre-mortem value by 11 ± 1 %, compared with -0.2 ± 0.3 % for body length (P

Recent studies have demonstrated that dual-energy computed tomography (CT) can provide useful information in several chest-related clinical indications. Compared with single-energy CT, dual-energy CT of the chest is feasible with the use of a radiation-dose-neutral scanning protocol. This article highlights the different types of images that can be generated by using dual-energy CT protocols such as virtual monochromatic, virtual unenhanced (ie, water), and pulmonary blood volume (ie, iodine) images. The physical basis of dual-energy CT and material decomposition are explained. The advantages of the use of virtual low-monochromatic images include reduced volume of intravenous contrast material and improved contrast resolution of images. The use of virtual high-monochromatic images can reduce beam hardening and contrast streak artifacts. The pulmonary blood volume images can help differentiate various parenchymal abnormalities, such as infarcts, atelectasis, and pneumonias, as well as airway abnormalities. The pulmonary blood volume images allow quantitative and qualitative assessment of iodine distribution. The estimation of iodine concentration (quantitative assessment) provides objective analysis of enhancement. The advantages of virtual unenhanced images include differentiation of calcifications, talc, and enhanced thoracic structures. Dual-energy CT has applications in oncologic imaging, including diagnosis of thoracic masses, treatment planning, and assessment of response to treatment. Understanding the concept of dual-energy CT and its clinical application in the chest are the goals of this article.

Interstitial fluid accumulation can occur in pleural, pericardial, and peritoneal spaces, and subcutaneous tissue planes. The purpose of the study was to assess if whole body CT examination in a postmortem setting could help determine the presence and severity of third space fluid accumulation in the body. Our study included 41 human cadavers (mean age 61 years, 25 males and 16 females) who had whole-body postmortem CT prior to autopsy. All bodies were maintained in the morgue in the time interval between death and autopsy. Two radiologists reviewed the whole-body CT examinations independently to grade third space fluid in the pleura, pericardium, peritoneum, and subcutaneous space using a 5-point grading system. Qualitative CT grading for third space fluid was correlated with the amount of fluid found on autopsy and the quantitative CT fluid volume, estimated using a dedicated software program (Volume, Syngo Explorer, Siemens Healthcare). Moderate and severe peripheral edema was seen in 16/41 and 7/41 cadavers respectively. It is not possible to quantify anasarca at autopsy. Correlation between imaging data for third space fluid and the quantity of fluid found during autopsy was 0.83 for pleural effusion, 0.4 for pericardial effusion and 0.9 for ascites. The degree of anasarca was significantly correlated with the severity of ascites (p

The purposes of this study were to retrospectively assess the frequency of acute aortic intramural hematoma and evaluate whether the elimination of the unenhanced imaging acquisition series from the dual-phase MDCT angiography (CTA) protocol for chest pain might affect diagnostic accuracy in detecting intramural hematoma and justify the reduced radiation dose.From October 2006 to November 2012, 306 patients (mean age, 65.0 years) with acute chest pain underwent emergency CTA with a 64-MDCT scanner. Two experienced cardiovascular radiologists, blinded to the diagnosis, assessed the images in two different sessions in which enhanced (single-phase CTA) and combined unenhanced and contrast-enhanced (dual-phase CTA) findings were evaluated. Sensitivity, specificity, and accuracy along with 95% CIs were calculated. Surgical and pathologic diagnoses, including findings at clinical follow-up and any subsequent imaging modality, were used as reference standards.Thirty-six patients were suspected of having intramural hematoma; 16 patients underwent both surgery and transesophageal echocardiography (TEE), and the remaining 20 underwent TEE. Single-phase CTA showed a higher number of false-negative and false-positive results than dual-phase CTA. With intramural hematoma frequency of 12% (95% CI, 8.38-15.91%), sensitivity, specificity, and accuracy were 94.4% (81.3-99.3%), 99.3% (97.4-99.9%), and 98.7% (96.7-99.6%) for combined dual-phase CTA and 68.4% (51.4-82.5%), 96.3% (93.2-98.2%), and 92.8% (89.3-95.4%) for single-phase CTA. Dual-phase was significantly better than single-phase CTA with respect to sensitivity (p=0.002), specificity (p=0.008), overall accuracy (p0.001), and interrater agreement (p=0.001).The frequency of acute aortic intramural hematoma is similar to that previously reported. The acquisition of unenhanced images during the chest pain dual-phase CTA protocol significantly improves diagnostic accuracy over single-phase CTA.

To assess diagnostic image quality of reduced dose (RD) abdominal computed tomography (CT) with 9 iterative reconstruction techniques (IRTs) from 4 different vendors to the standard of care (SD) CT.In an Institutional Review Board approved study, 66 patients (mean age 60 ± 13 years, 44 men, and 22 women) undergoing routine abdomen CT on multi-detector CT (MDCT) scanners from vendors A, B, and C (≥ 64 row CT scanners) (22 patients each) gave written informed consent for acquisition of an additional RD CT series. Sinogram data of RD CT was reconstructed with two vendor-specific and a vendor-neutral IRTs (A-1, A-2, A-3; B-1, B-2, B-3; and C-1, C-2, C-3) and SD CT series with filtered back projection. Subjective image evaluation was performed by two radiologists for each SD and RD CT series blinded and independently. All RD CT series (198) were assessed first followed by SD CT series (66). Objective image noise was measured for SD and RD CT series. Data were analyzed by Wilcoxon signed rank, kappa, and analysis of variance tests.There were 13/50, 18/57 and 9/40 missed lesions (size 2-7 mm) on RD CT for vendor A, B, and C, respectively. Missed lesions includes liver cysts, kidney cysts and stone, gall stone, fatty liver, and pancreatitis. There were also 5, 4, and 4 pseudo lesions (size 2-3 mm) on RD CT for vendor A, B, and C, respectively. Lesions conspicuity was sufficient for clinical diagnostic performance for 6/24 (RD-A-1), 10/24 (RD-A-2), and 7/24 (RD-A-3) lesions for vendor A; 5/26 (RD-B-1), 6/26 (RD-B-2), and 7/26 (RD-B-3) lesions for vendor B; and 4/20 (RD-C-1) 6/20 (RD-C-2), and 10/20 (RD-C-3) lesions for vendor C (P = 0.9). Mean objective image noise in liver was significantly lower for RD A-1 compared to both RD A-2 and RD A-3 images (P0.001). Similarly, mean objective image noise lower for RD B-2 (compared to RD B-1, RD B-3) and RD C-3 (compared to RD C-1 and C-2) (P = 0.016).Regardless of IRTs and MDCT vendors, abdominal CT acquired at mean CT dose index volume 1.3 mGy is not sufficient to retain clinical diagnostic performance.

Recent studies have reported germline CDH1 mutations in cases of lobular breast cancer (LBC) not associated with the classical hereditary diffuse gastric cancer syndrome. A multidisciplinary workgroup discussed genetic susceptibility, pathophysiology and clinical management of hereditary LBC (HLBC). The team has established the clinical criteria for CDH1 screening and results’ interpretation, and created consensus guidelines regarding genetic counselling, breast surveillance and imaging techniques, clinicopathological findings, psychological and decisional support, as well as prophylactic surgery and plastic reconstruction. Based on a review of current evidence for the identification of HLBC cases/families, CDH1 genetic testing is recommended in patients fulfilling the following criteria: (A) bilateral LBC with or without family history of LBC, with age at onset CDH1 asymptomatic mutant carriers, breast surveillance with clinical examination, yearly mammography, contrast-enhanced breast MRI and breast ultrasonography (US) with 6-month interval between the US and the MRI should be implemented as a first approach. In selected cases with personal history, family history of LBC and CDH1 mutations, prophylactic mastectomy could be discussed with an integrative group of clinical experts. Psychodecisional support also plays a pivotal role in the management of individuals with or without CDH1 germline alterations. Ultimately, the definition of a specific protocol for CDH1 genetic screening and ongoing coordinated management of patients with HLBC is crucial for the effective surveillance and early detection of LBC.