Dynamic Contrast-enhanced Breast MRI Research Articles

A quantitative heterogeneity analysis approach to molecular subtype recognition of breast cancer in dynamic contrast-enhanced magnetic imaging images from radiomics data.

Breast cancer is a major cause of mortality among women worldwide. Dynamic contrast-enhanced breast magnetic resonance imaging (DCE-MRI) is a good imaging technique that can show temporal information about the kinetics of the contrast agent in suspicious breast lesions as well as acceptable spatial resolution. Computer-aided detection systems assist in the detection of lesions through medical image processing techniques combined with computerized analysis and calculation, which in turn helps radiologists recognize molecular subtypes of breast lesions that will be beneficial for better treatment plan decisions. In this paper, a computer-aided diagnosis method is proposed to automatically locate breast cancer lesions and identify molecular subtypes of breast cancer with heterogeneity analysis from radiomics data. A fast region-based convolutional network (Faster R-CNN) framework is first applied to images to detect breast cancer lesions. Then, the heterogeneous regions of every breast cancer lesion are extracted. Based on the multiple visual and kinetic radiomics features extracted from the heterogeneous regions, a temporal bag of visual word model is proposed, which takes into account the dynamic characteristics of both lesion and heterogeneous regions in images over time. The recognition task of molecular subtypes of breast lesions is realized based on a stacking classification model. At the genetic level, breast cancer is divided into four molecular subtypes, namely, luminal epithelial type A (Luminal A), luminal epithelial type B (Luminal B), HER-2 overexpression and basal cell type. The experimental results show that the precision of the four subtypes is 93%, 94%, 83%, 86%; the recall is 96%, 80%, 91%, 94%; and the F1-score is 95%, 86%, 87%. The experimental results denote the influence of heterogeneous regions on the recognition task. The DCE-MRI-based approach to identify molecular typing of breast cancer for noninvasive diagnosis will contribute to the development of breast cancer treatment, improved outcomes and reduced mortality.

Breast DWI Analyzed Before and After Gadolinium Contrast Administration-An Intrapatient Analysis on 1.5 T and 3.0 T.

Diffusion-weighted magnetic resonance imaging (MRI) is gaining popularity as an addition to standard dynamic contrast-enhanced breast MRI. Although adding diffusion-weighted imaging (DWI) to the standard protocol design would require increased scanning-time, implementation during the contrast-enhanced phase could offer a multiparametric MRI protocol without any additional scanning time. However, gadolinium within a region of interest (ROI) might affect assessments of DWI. This study aims to determine if acquiring DWI postcontrast, incorporated in an abbreviated MRI protocol, would statistically significantly affect lesion classification. In addition, the effect of postcontrast DWI on breast parenchyma was studied. Screening or preoperative MRIs (1.5 T/3 T) were included for this study. Diffusion-weighted imaging was acquired with single-shot spin echo-echo planar imaging before and at approximately 2 minutes after gadoterate meglumine injection. Apparent diffusion coefficients (ADCs) based on 2-dimensional ROIs of fibroglandular tissue, as well as benign and malignant lesions at 1.5 T/3.0 T, were compared with a Wilcoxon signed rank test. Diffusivity levels were compared between precontrast and postcontrast DWI with weighted κ. An overall P ≤ 0.05 was considered statistically significant. No significant changes were observed in ADC mean after contrast administration in 21 patients with 37 ROI of healthy fibroglandular tissue and in the 93 patients with 93 (malignant and benign) lesions. This effect remained after stratification on B 0 . In 18% of all lesions, a diffusion level shift was observed, with an overall weighted κ of 0.75. This study supports incorporating DWI at 2 minutes postcontrast when ADC is calculated based on b150-b800 with 15 mL 0.5 M gadoterate meglumine in an abbreviated multiparametric MRI protocol without requiring extra scan time.

A Soft-Attention Guidance Stacked neural Network for neoadjuvant chemotherapy’s pathological response diagnosis using breast dynamic contrast-enhanced MRI

Pathological complete response (pCR) to Neoadjuvant chemotherapy (NACT)is a significant clinical indicator for diagnosing patient outcomes and overall survival. However, the current diagnosis of pCR suffers from two limitations: firstly, it requires an invasive biopsy, intensifying the patient’s pain and risk of infection. Secondly, current radiomics-based computer methods inefficiently utilize intrinsic characteristics of medical images. In this paper, we present a Soft-Attention Guidance Stacked neural Network (SAGS-Net) to address these limitations and predict the NACT responses from breast dynamic contrast-enhanced MRI (DCE-MRI). Particularly, We first design a self-adapting feature selection strategy to extract peritumoral features while excluding outside noises accurately. Then, SAGS-Net is built by stacking position-based spatial models that generate discriminative feature representation. Each stacked model inside has its semantic feature branch as a control gate for pCR-related feature selection. The semantic feature branch combined with the residual learning mechanism as a feature selector enhances valuable information and suppresses redundant information, simulating the process of radiologists making a clinical diagnosis based on domain knowledge. Finally, the incremental stacked network architecture assisted with the soft-attention strategy can gradually refine attention-aware features in complex DCE-MRI sequences. Experimental results based on real-world clinical datasets confirmed that the proposed SAGS-Net obtain superior performance with 93% AUC, and it provides a new way that leverage DCE-MRI sequences to predict NACT responses.

Pharmacokinetic Analysis of Enhancement-Constrained Acceleration (ECA) reconstruction-based high temporal resolution breast DCE-MRI.

The high spatial and temporal resolution of dynamic contrast-enhanced MRI (DCE-MRI) can improve the diagnostic accuracy of breast cancer screening in patients who have dense breasts or are at high risk of breast cancer. However, the spatiotemporal resolution of DCE-MRI is limited by technical issues in clinical practice. Our earlier work demonstrated the use of image reconstruction with enhancement-constrained acceleration (ECA) to increase temporal resolution. ECA exploits the correlation in k-space between successive image acquisitions. Because of this correlation, and due to the very sparse enhancement at early times after contrast media injection, we can reconstruct images from highly under-sampled k-space data. Our previous results showed that ECA reconstruction at 0.25 seconds per image (4 Hz) can estimate bolus arrival time (BAT) and initial enhancement slope (iSlope) more accurately than a standard inverse fast Fourier transform (IFFT) when k-space data is sampled following a Cartesian based sampling trajectory with adequate signal-to-noise ratio (SNR). In this follow-up study, we investigated the effect of different Cartesian based sampling trajectories, SNRs and acceleration rates on the performance of ECA reconstruction in estimating contrast media kinetics in lesions (BAT, iSlope and Ktrans) and in arteries (Peak signal intensity of first pass, time to peak, and BAT). We further validated ECA reconstruction with a flow phantom experiment. Our results show that ECA reconstruction of k-space data acquired with 'Under-sampling with Repeated Advancing Phase' (UnWRAP) trajectories with an acceleration factor of 14, and temporal resolution of 0.5 s/image and high SNR (SNR ≥ 30 dB, noise standard deviation (std) < 3%) ensures minor errors (5% or 1 s error) in lesion kinetics. Medium SNR (SNR ≥ 20 dB, noise std ≤ 10%) was needed to accurately measure arterial enhancement kinetics. Our results also suggest that accelerated temporal resolution with ECA with 0.5 s/image is practical.

Identification of triple-negative breast cancer and androgen receptor expression based on histogram and texture analysis of dynamic contrast-enhanced MRI

BackgroundTriple-negative breast cancer (TNBC) is highly malignant and has a poor prognosis due to the lack of effective therapeutic targets. Androgen receptor (AR) has been investigated as a possible therapeutic target. This study quantitatively assessed intratumor heterogeneity by histogram analysis of pharmacokinetic parameters and texture analysis on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to discriminate TNBC from non-triple-negative breast cancer (non-TNBC) and to identify AR expression in TNBC.MethodsThis retrospective study included 99 patients with histopathologically proven breast cancer (TNBC: 36, non-TNBC: 63) who underwent breast DCE-MRI before surgery. The pharmacokinetic parameters of DCE-MRI (Ktrans, Kep and Ve) and their corresponding texture parameters were calculated. The independent t-test, or Mann-Whitney U-test was used to compare quantitative parameters between TNBC and non-TNBC groups, and AR-positive (AR+) and AR-negative (AR-) TNBC groups. The parameters with significant difference between two groups were further involved in logistic regression analysis to build a prediction model for TNBC. The ROC analysis was conducted on each independent parameter and the TNBC predicting model for evaluating the discrimination performance. The area under the ROC curve (AUC), sensitivity and specificity were derived.ResultsThe binary logistic regression analysis revealed that Kep_Range (p = 0.032) and Ve_SumVariance (p = 0.005) were significantly higher in TNBC than in non-TNBC. The AUC of the combined model for identifying TNBC was 0.735 (p < 0.001) with a cut-off value of 0.268, and its sensitivity and specificity were 88.89% and 52.38%, respectively. The value of Kep_Compactness2 (p = 0.049), Kep_SphericalDisproportion (p = 0.049), and Ve_GlcmEntropy (p = 0.008) were higher in AR + TNBC group than in AR-TNBC group.ConclusionHistogram and texture analysis of breast lesions on DCE-MRI showed potential to identify TNBC, and the specific features can be possible predictors of AR expression, enhancing the ability to individualize the treatment of patients with TNBC.

Weakly supervised breast lesion detection in DCE-MRI using self-transfer learning.

Breast cancer is a typically diagnosed and life-threatening cancer in women. Thus, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is increasingly used for breast lesion detection and diagnosis because of the high resolution of soft tissues. Moreover, supervised detection methods have been implemented for breast lesion detection. However, these methods require substantial time and specialized staff to develop the labeled training samples. To investigate the potential of weakly supervised deep learning models for breast lesion detection. A total of 1003 breast DCE-MRI studies were collected, including 603 abnormal cases with 770 breast lesions and 400 normal subjects. The proposed model was trained using breast DCE-MRI considering only the image-level labels (normal and abnormal) and optimized for classification and detection sub-tasks simultaneously. Ablation experiments were performed to evaluate different convolutional neural network (CNN) backbones (VGG19 and ResNet50) as shared convolutional layers, as well as to evaluate the effect of the preprocessing methods. Our weakly supervised model performed better with VGG19 than with ResNet50 (p < 0.05). The average precision (AP) of the classification sub-task was 91.7% for abnormal cases and 88.0% for normal samples. The area under the receiver operating characteristic (ROC) curve (AUC) was 0.939 (95% confidence interval [CI]: 0.920-0.941). The weakly supervised detection task AP was 85.7%, and the correct location (CorLoc) was 90.2%. A sensitivity of 84.0% at two-false positives per image was assessed based on free-response ROC (FROC) curve. The results confirm that a weakly supervised CNN based on self-transfer learning is an effective and promising auxiliary tool for detecting breast lesions.

Clinical evaluation of cylindrical regional suppression in dynamic contrast-enhanced breast MRI: An intra-individual comparison study on image quality and lesion conspicuity

PurposeTo evaluate the effect of a cylindrical regional-suppression technique (CREST) on image quality and lesion conspicuity in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast. MethodThis was a comparative study of 67 women with 44 lesions who underwent breast DCE-MRI with CREST (CREST-DCE) and had a previous DCE-MRI without CREST (conv-DCE) available. Two radiologists assessed image quality parameters and lesion conspicuity using five-point Likert scales. In an intra-individual comparison, the effects of CREST on image quality (strong degradation to strong improvement) were assessed. Moreover, both radiologists identified the post-contrast phase, which benefited the most from using CREST in direct comparison. The statistical analysis included the Wilcoxon signed-rank test. ResultsCardiac motion-rated artefacts were significantly reduced in CREST-DCE compared to conv-DCE (3.6 ± 1.2 [CREST-DCE] vs 2.1 ± 0.8 [conv-DCE], p < 0.001). At the axilla, the visualisation of anatomical structures (3.9 ± 1.0 vs 2.3 ± 1.2, p < 0.001) and the skin contour (4.3 ± 0.8 vs 3.0 ± 1.1, p < 0.001) were significantly improved in CREST-DCE, whereas ghosting artefacts were significantly less pronounced (3.8 ± 1.1 vs 2.4 ± 1.0, p < 0.001). The parasternal region was similarly assessable using both techniques (4.3 ± 1.1 vs 4.2 ± 1.2, p = 0.47). In direct comparison, CREST-DCE images were classified as “improved” in 54/67 and “equivalent” in 13/67 exams. The effects of CREST were found to be most pronounced in the very early post-contrast phase (32/67). The lesion conspicuity was rated similar for CREST and conv-DCE (4.7 ± 0.7 vs 4.8 ± 0.2, p = 0.18). ConclusionsCREST appears to be an effective tool to reduce cardiac motion-related artefacts and, therefore, may improve image quality in breast DCE-MRI without impairing lesion conspicuity.

Development and validation of a four-dimensional registration technique for DCE breast MRI

BackgroundPatient motion can degrade image quality of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) due to subtraction artifacts. By objectively and subjectively assessing the impact of principal component analysis (PCA)-based registration on pretreatment DCE-MRIs of breast cancer patients, we aim to validate four-dimensional registration for DCE breast MRI.ResultsAfter applying a four-dimensional, PCA-based registration algorithm to 154 pretreatment DCE-MRIs of histopathologically well-described breast cancer patients, we quantitatively determined image quality in unregistered and registered images. For subjective assessment, we ranked motion severity in a clinical reading setting according to four motion categories (0: no motion, 1: mild motion, 2: moderate motion, 3: severe motion with nondiagnostic image quality). The median of images with either moderate or severe motion (median category 2, IQR 0) was reassigned to motion category 1 (IQR 0) after registration. Motion category and motion reduction by registration were correlated (Spearman’s rho: 0.83, p < 0.001). For objective assessment, we performed perfusion model fitting using the extended Tofts model and calculated its volume transfer coefficient Ktrans as surrogate parameter for motion artifacts. Mean Ktrans decreased from 0.103 (± 0.077) before registration to 0.097 (± 0.070) after registration (p < 0.001). Uncertainty in perfusion quantification was reduced by 7.4% after registration (± 15.5, p < 0.001).ConclusionsFour-dimensional, PCA-based image registration improves image quality of breast DCE-MRI by correcting for motion artifacts in subtraction images and reduces uncertainty in quantitative perfusion modeling. The improvement is most pronounced when moderate-to-severe motion artifacts are present.

An Investigation into Race Bias in Random Forest Models Based on Breast DCE-MRI Derived Radiomics Features.

Recent research has shown that artificial intelligence (AI) models can exhibit bias in performance when trained using data that are imbalanced by protected attribute(s). Most work to date has focused on deep learning models, but classical AI techniques that make use of hand-crafted features may also be susceptible to such bias. In this paper we investigate the potential for race bias in random forest (RF) models trained using radiomics features. Our application is prediction of tumour molecular subtype from dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) of breast cancer patients. Our results show that radiomics features derived from DCE-MRI data do contain race-identifiable information, and that RF models can be trained to predict White and Black race from these data with 60-70% accuracy, depending on the subset of features used. Furthermore, RF models trained to predict tumour molecular subtype using race-imbalanced data seem to produce biased behaviour, exhibiting better performance on test data from the race on which they were trained.

Dynamic Contrast-Enhanced Breast MRI Findings Predictive of Malignancy in MRI-Only Breast Lesions Examined by MR-Guided Biopsy: A Single-Center Experience

Dynamic Contrast-Enhanced Breast MRI Findings Predictive of Malignancy in MRI-Only Breast Lesions Examined by MR-Guided Biopsy: A Single-Center Experience

A radiomic model to classify response to neoadjuvant chemotherapy in breast cancer

BackgroundMedical image analysis has evolved to facilitate the development of methods for high-throughput extraction of quantitative features that can potentially contribute to the diagnostic and treatment paradigm of cancer. There is a need for further improvement in the accuracy of predictive markers of response to neo-adjuvant chemotherapy (NAC). The aim of this study was to develop a radiomic classifier to enhance current approaches to predicting the response to NAC breast cancer.MethodsData on patients treated for breast cancer with NAC prior to surgery who had a pre-NAC dynamic contrast enhanced breast MRI were included. Response to NAC was assessed using the Miller–Payne system on the excised tumor. Tumor segmentation was carried out manually under the supervision of a consultant breast radiologist. Features were selected using least absolute shrinkage selection operator regression. A support vector machine learning model was used to classify response to NAC.Results74 patients were included. Patients were classified as having a poor response to NAC (reduction in cellularity < 90%, n = 44) and an excellent response (> 90% reduction in cellularity, n = 30). 4 radiomics features (discretized kurtosis, NGDLM contrast, GLZLM_SZE and GLZLM_ZP) were identified as pertinent predictors of response to NAC. A SVM model using these features stratified patients into poor and excellent response groups producing an AUC of 0.75. Addition of estrogen receptor status improved the accuracy of the model with an AUC of 0.811.ConclusionThis study identified a radiomic classifier incorporating 4 radiomics features to augment subtype based classification of response to NAC in breast cancer.

Differentiation of breast lesions on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using deep transfer learning based on DenseNet201.

In order to achieve better performance, artificial intelligence is used in breast cancer diagnosis. In this study, we evaluated the efficacy of different fine-tuning strategies of deep transfer learning (DTL) based on the DenseNet201 model to differentiate malignant from benign lesions on breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). We collected 4260 images of benign lesions and 4140 images of malignant lesions of the breast pertaining to pathologically confirmed cases. The benign and malignant groups was randomly divided into a training set and a testing set at a ratio of 9:1. A DTL model based on the DenseNet201 model was established, and the effectiveness of 4 fine-tuning strategies (S0: strategy 0, S1: strategy; S2: strategy; and S3: strategy) was compared. Additionally, DCE-MRI images of 48 breast lesions were selected to verify the robustness of the model. Ten images were obtained for each lesion. The classification was considered correct if more than 5 images were correctly classified. The metrics for model performance evaluation included accuracy (Ac) in the training and testing sets, precision (Pr), recall rate (Rc), f1 score (f1), and area under the receiver operating characteristic curve (AUROC) in the validation set. The Ac of the 4 fine-tuning strategies reached 100.00% in the training set. The S2 strategy exhibited good convergence in the testing set. The Ac of S2 was 98.01% in the testing set, which was higher than those of S0 (93.10%), S1 (90.45%), and S3 (93.90%). The average classification Pr, Rc, f1, and AUROC of S2 in the validation set were (89.00%, 80.00%, 0.81, and 0.79, respectively) higher than those of S0 (76.00%, 67.00%, 0.69, and 0.65, respectively), S1 (60.00%, 60.00%, 0.60, 0.66, and respectively), and S3 (77.00%, 73.00%, 0.74, 0.72, respectively). The degree of coincidence between S2 and the histopathological method for differentiating between benign and malignant breast lesions was high (κ = 0.749). The S2 strategy can improve the robustness of the DenseNet201 model in relatively small breast DCE-MRI datasets, and this is a reliable method to increase the Ac of discriminating benign from malignant breast lesions on DCE-MRI.

MRI can accurately diagnose breast cancer during lactation.

To test the diagnostic performance of breast dynamic contrast-enhanced (DCE) MRI during lactation. Datasets of 198 lactating patients, including 66 pregnancy-associated breast cancer (PABC) patients and 132 controls, who were scanned by DCE on 1.5-T MRI, were retrospectively evaluated. Six blinded, expert radiologists independently read a single DCE maximal intensity projection (MIP) image for each case and were asked to determine whether malignancy was suspected and the background-parenchymal-enhancement (BPE) grade. Likewise, computer-aided diagnosis CAD MIP images were independently read by the readers. Contrast-to-noise ratio (CNR) analysis was measured and compared among four consecutive acquisitions of DCE subtraction images. For MIP-DCE images, the readers achieved the following means: sensitivity 93.3%, specificity 80.3%, positive-predictive-value 70.4, negative-predictive-value 96.2, and diagnostic accuracy of 84.6%, with a substantial inter-rater agreement (Kappa = 0.673, p value < 0.001). Most false-positive interpretations were attributed to either the MIP presentation, an underlying benign lesion, or an asymmetric appearance due to prior treatments. CAD's derived diagnostic accuracy was similar (p = 0.41). BPE grades were significantly increased in the healthy controls compared to the PABC cohort (p < 0.001). CNR significantly decreased by 11-13% in each of the four post-contrast images (p < 0.001). Breast DCE MRI maintains its high efficiency among the lactating population, probably due to a vascular-steal phenomenon, which causes a significant reduction of BPE in cancer cases. Upon validation by prospective, multicenter trials, this study could open up the opportunity for breast MRI to be indicated in the screening and diagnosis of lactating patients, with the aim of facilitating an earlier diagnosis of PABC. • A single DCE MIP image was sufficient to reach a mean sensitivity of 93.3% and NPV of 96.2%, to stress the high efficiency of breast MRI during lactation. • Reduction in BPE among PABC patients compared to the lactating controls suggests that several factors, including a possible vascular steal phenomenon, may affect cancer patients. • Reduction in CNR along four consecutive post-contrast acquisitions highlights the differences in breast carcinoma and BPE kinetics and explains the sufficient conspicuity on the first subtracted image.

CNN-Based Approaches with Different Tumor Bounding Options for Lymph Node Status Prediction in Breast DCE-MRI.

Simple SummaryBreast cancer represents the most frequent cancer in women in the world. The state of the axillary lymph node is considered an independent prognostic factor and is currently evaluated only with invasive methods. Deep learning approaches, especially the ones based on convolutional neural networks, offer a valid, non-invasive alternative, allowing extraction of large amounts of the quantitative data that are used to build predictive models. The aim of our work is to evaluate the influence of the peritumoral parenchyma through different bounding box techniques on the prediction of the axillary lymph node in breast cancer patients using a deep learning artificial intelligence approach.Background: The axillary lymph node status (ALNS) is one of the most important prognostic factors in breast cancer (BC) patients, and it is currently evaluated by invasive procedures. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), highlights the physiological and morphological characteristics of primary tumor tissue. Deep learning approaches (DL), such as convolutional neural networks (CNNs), are able to autonomously learn the set of features directly from images for a specific task. Materials and Methods: A total of 155 malignant BC lesions evaluated via DCE-MRI were included in the study. For each patient’s clinical data, the tumor histological and MRI characteristics and axillary lymph node status (ALNS) were assessed. LNS was considered to be the final label and dichotomized (LN+ (27 patients) vs. LN− (128 patients)). Based on the concept that peritumoral tissue contains valuable information about tumor aggressiveness, in this work, we analyze the contributions of six different tumor bounding options to predict the LNS using a CNN. These bounding boxes include a single fixed-size box (SFB), a single variable-size box (SVB), a single isotropic-size box (SIB), a single lesion variable-size box (SLVB), a single lesion isotropic-size box (SLIB), and a two-dimensional slice (2DS) option. According to the characteristics of the volumes considered as inputs, three different CNNs were investigated: the SFB-NET (for the SFB), the VB-NET (for the SVB, SIB, SLVB, and SLIB), and the 2DS-NET (for the 2DS). All the experiments were run in 10-fold cross-validation. The performance of each CNN was evaluated in terms of accuracy, sensitivity, specificity, the area under the ROC curve (AUC), and Cohen’s kappa coefficient (K). Results: The best accuracy and AUC are obtained by the 2DS-NET (78.63% and 77.86%, respectively). The 2DS-NET also showed the highest specificity, whilst the highest sensibility was attained by the VB-NET based on the SVB and SIB as bounding options. Conclusion: We have demonstrated that a selective inclusion of the DCE-MRI’s peritumoral tissue increases accuracy in the lymph node status prediction in BC patients using CNNs as a DL approach.

Radiomic Signature Based on Dynamic Contrast-Enhanced MRI for Evaluation of Axillary Lymph Node Metastasis in Breast Cancer.

Background To construct and validate a radiomic-based model for estimating axillary lymph node (ALN) metastasis in patients with breast cancer by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Methods In this retrospective study, a radiomic-based model was established in a training cohort of 236 patients with breast cancer. Radiomic features were extracted from breast DCE-MRI scans. A method named the least absolute shrinkage and selection operator (LASSO) was applied to select radiomic features based on highly reproducible features. A radiomic signature was built by a support vector machine (SVM). Multivariate logistic regression analysis was adopted to establish a clinical characteristic-based model. The performance of models was analysed through discrimination ability and clinical benefits. Results The radiomic signature comprised 6 features related to ALN metastasis and showed significant differences between the patients with ALN metastasis and without ALN metastasis (P < 0.001). The area under the curve (AUC) of the radiomic model was 0.990 and 0.858, respectively, in the training and validation sets. The clinical feature-based model, including MRI-reported status and palpability, performed slightly worse, with an AUC of 0.784 in the training cohort and 0.789 in the validation cohort. The radiomic signature was confirmed to provide more clinical benefits by decision curve analysis. Conclusions The radiomic-based model developed in this study can successfully diagnose the status of lymph nodes in patients with breast cancer, which may reduce unnecessary invasive clinical operations.

Evaluation of the effects of neoadjuvant chemotherapy on probably benign breast lesions with MRI: Report of two cases

In cases with breast cancer, probably benign lesions can also be seen in the other areas of the breast. It is known that neoadjuvant chemotherapy causes morphological changes in the normal breast tissue and the benign lesions, in addition to effects on malignant mass. In cases with complete response with neoadjuvant chemotherapy, probably benign lesions without tissue diagnosis cause a dilemma in the breast conserving surgery plan, when they shrink or disappear at the end of the treatment. We aimed to demonstrate the effects of neoadjuvant chemotherapy in cases with probably benign breast lesions via dynamic contrast enhanced breast MRI.

Robustness Evaluation of a Deep Learning Model on Sagittal and Axial Breast DCE-MRIs to Predict Pathological Complete Response to Neoadjuvant Chemotherapy.

To date, some artificial intelligence (AI) methods have exploited Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) to identify finer tumor properties as potential earlier indicators of pathological Complete Response (pCR) in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). However, they work either for sagittal or axial MRI protocols. More flexible AI tools, to be used easily in clinical practice across various institutions in accordance with its own imaging acquisition protocol, are required. Here, we addressed this topic by developing an AI method based on deep learning in giving an early prediction of pCR at various DCE-MRI protocols (axial and sagittal). Sagittal DCE-MRIs refer to 151 patients (42 pCR; 109 non-pCR) from the public I-SPY1 TRIAL database (DB); axial DCE-MRIs are related to 74 patients (22 pCR; 52 non-pCR) from a private DB provided by Istituto Tumori “Giovanni Paolo II” in Bari (Italy). By merging the features extracted from baseline MRIs with some pre-treatment clinical variables, accuracies of 84.4% and 77.3% and AUC values of 80.3% and 78.0% were achieved on the independent tests related to the public DB and the private DB, respectively. Overall, the presented method has shown to be robust regardless of the specific MRI protocol.

BRCANet: A deep hybrid network in predicting BRCA1/2 gene mutation of breast cancer with dynamic contrast-enhanced breast MRI.

e13576 Background: BRCA1/BRCA2, representing as an important genetic biomarker of breast cancer (BCA), can provide clinically significant implications for personalized risk assessment, effective treatment option, and prognostic prediction. Methods: We here proposed the BRCANet, a novel end-to-end convolutional neural network for noninvasively determining BRCA1/BRCA2 mutation by integrating clinical, radiomics and deep learning of dynamic contrast-enhanced (DCE) MRI. BRCANet accepts different forms of medical data including clinicopathologic identifications, high throughput radiomics and deep imaging features of breast MRI using a deep hybrid neural network for data/feature integration. Model training and cross-validation was performed in 132 case-controlled BCA patients from two in two tertiary care hospitals, in which clinicopathologic, genomic and image data of BCA lesions were available and center-standardized for study analysis. Results: Results show that a BRCANet-Plus model, embedded with clinicopathologic, radiomics and deep MRI features achieves an arear under curve of (0.783; 95% confidence intervals [CIs], 0.704 - 0.848) for predicting BRCA1/2 mutation, outperforming the compared state-of-the-art methods, i.e., BRCANet derived from image-only data (0.743; 95% CIs, 0.659 - 0.815; p = 0.037), and BRCARad derived from radiomics-only data (0.734; 95% CIs, 0.649-0.807; p = 0.031). After net benefit evaluation, the proposed BRCANet-Plus shows promise to improve diagnostic performance against conventional clinical or image approaches. Conclusions: Therefore, we concluded the presented deep hybrid approach by integrating multimodal clinical-imaging data, especially breast MRI, have a great potential to predict BRCA1/2 mutational status of BCA. This proof-of-concept strategy can be utilized for studying similar clinical questions.

Sigmoid model analysis of breast dynamic contrast-enhanced MRI: Distinguishing between benign and malignant breast masses and breast cancer subtype prediction.

Dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) is performed to distinguish between benign and malignant lesions by evaluating the changes in signal intensity of the acquired image (kinetic curve). This study aimed to verify whether the existing breast DCE‐MRI analyzed by the sigmoid model can accurately distinguish between benign and invasive ductal carcinoma (IDC) and predict the subtype.A total of 154 patients who underwent breast MRI for detailed breast mass examinations were included in this study (38 with benign masses and 116 with IDC. The sigmoid model involved the acquisition of images at seven timepoints in 1‐min intervals to determine the change in signal intensity before and after contrast injection. From this curve, the magnitude of the increase in signal intensity in the early phase, the time to reach the maximum increase, and the slopes in the early and late phases were calculated. The Mann–Whitney U‐test was used for the statistical analysis.The IDC group exhibited a significantly larger and faster signal increase in the early phase and a significantly smaller rate of increase in the late phase than the benign group (P < 0.001). The luminal A‐like group demonstrated a significantly longer time to reach the maximum signal increase rate than other IDC subtypes (P < 0.05).The sigmoid model analysis of breast DCE‐MRI can distinguish between benign lesions and IDC and may also help in predicting luminal A‐like breast cancer.

Background parenchymal enhancement in contrast-enhanced MR imaging suggests systemic effects of intrauterine contraceptive devices

ObjectivesLevonorgestrel-releasing intrauterine contraceptive devices (LNG-IUDs) are designed to exhibit only local hormonal effects. There is an ongoing debate on whether LNG-IUDs can have side effects similar to systemic hormonal medication. Benign background parenchymal enhancement (BPE) in dynamic contrast–enhanced (DCE) MRI has been established as a sensitive marker of hormonal stimulation of the breast. We investigated the association between LNG-IUD use and BPE in breast MRI to further explore possible systemic effects of LNG-IUDs.MethodsOur hospital database was searched to identify premenopausal women without personal history of breast cancer, oophorectomy, and hormone replacement or antihormone therapy, who had undergone standardized DCE breast MRI at least twice, once with and without an LNG-IUD in place. To avoid confounding aging-related effects on BPE, half of included women had their first MRI without, the other half with, LNG-IUD in place. Degree of BPE was analyzed according to the ACR categories. Wilcoxon-matched-pairs signed-rank test was used to compare the distribution of ACR categories with vs. without LNG-IUD.ResultsForty-eight women (mean age, 46 years) were included. In 24/48 women (50% [95% CI: 35.9–64.1%]), ACR categories did not change with vs. without LNG-IUDs. In 23/48 women (48% [33.9–62.1%]), the ACR category was higher with vs. without LNG-IUDs; in 1/48 (2% [0–6%]), the ACR category was lower with vs. without LNG-IUDs. The change of ACR category depending on the presence or absence of an LNG-IUD proved highly significant (p < 0.001).ConclusionThe use of an LNG-IUD can be associated with increased BPE in breast MRI, providing further evidence that LNG-IUDs do have systemic effects.Key Points• The use of levonorgestrel-releasing intrauterine contraceptive devices is associated with increased background parenchymal enhancement in breast MRI.• This suggests that hormonal effects of these devices are not only confined to the uterine cavity, but may be systemic.• Potential systemic effects of levonorgestrel-releasing intrauterine contraceptive devices should therefore be considered.