Dynamic Contrast-enhanced Breast MRI Research Articles

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.

Quantitative Changes in Intratumoral Habitats on MRI Correlate With Pathologic Response in Early-stage ER/PR+ HER2- Breast Cancer Treated With Preoperative Stereotactic Ablative Body Radiotherapy.

To quantitatively evaluate intratumoral habitats on dynamic contrast-enhanced (DCE) breast MRI to predict pathologic breast cancer response to stereotactic ablative body radiotherapy (SABR). Participants underwent SABR treatment (28.5 Gy x3), baseline and post-SABR MRI, and breast-conserving surgery for ER/PR+ HER2- breast cancer. MRI analysis was performed on DCE T1-weighted images. MRI voxels were assigned eight habitats based on high (H) or low (L) maximum enhancement and the sequentially numbered dynamic sequence of maximum enhancement (H1-4, L1-4). MRI response was analyzed by percent tumor volume remaining (%VR = volume post-SABR/volume pre-SABR), and percent habitat makeup (%HM of habitat X = habitat X voxels/total voxels in the segmented volume). These were correlated with percent tumor bed cellularity (%TC) for pathologic response. Sixteen patients completed the trial. The %TC ranged 20%-80%. MRI %VR demonstrated strong correlations with %TC (Pearson R = 0.7-0.89). Pre-SABR tumor %HMs differed significantly from whole breasts (P = 0.005 to <0.00001). Post-SABR %HM of tumor habitat H4 demonstrated the largest change, increasing 13% (P = 0.039). Conversely, combined %HM for H1-3 decreased 17% (P = 0.006). This change correlated with %TC (P < 0.00001) and distinguished pathologic partial responders (≤70 %TC) from nonresponders with 94% accuracy, 93% sensitivity, 100% specificity, 100% positive predictive value, and 67% negative predictive value. In patients undergoing preoperative SABR treatment for ER/PR+ HER2- breast cancer, quantitative MRI habitat analysis of %VR and %HM change correlates with pathologic response.

Abstract P2-12-18: A phase 2 study of neoadjuvant systemic therapy with eribulin followed by doxorubicin and cyclophosphamide for HER2-negative inflammatory breast cancer

Abstract Background: Inflammatory breast cancer (IBC) is unresectable at presentation, thus neoadjuvant systemic therapy (NAS) is the primary treatment for this aggressive disease. Eribulin (E) (Eisai Co., Ltd) is a synthetic analog of Halichondroin B that suppresses microtubule growth. Its properties of inducing a mesenchymal-to-epithelial transition and normalizing the vasculature of mammary tumors are particularly relevant to IBC. Pre-clinical data revealed changes in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters after exposure to E, thought to reflect tumor vasculature remodeling. We sought to examine the efficacy of E-containing NAS specifically for patients (pts) with HER2- IBC. We also characterized changes in breast DCE-MRI and diffusion weighted MRI (DWI) between baseline and 1 week (wk) after NAS. Methods: Pts with newly diagnosed HER2- IBC received 4 cycles of E 1.4 mg/m2 day (D)1, D8 every 21 D followed by 4 cycles of dose-dense (dd) doxorubicin (A) 60 mg/m2 + cyclophosphamide (C) 600mg/m2 every 2 wk followed by modified radical mastectomy (MRM) + postmastectomy radiation (PMRT) on a phase II prospective study (Cohort A). Primary endpoint was pathologic complete response (pCR) defined as no invasive disease in breast + axillary lymph nodes. Based on a Simon 2-stage design, if ≥ 2 of 16 pts had pCR then enrollment would continue to 25, with the regimen worthy of study if pCR ≥ 0.30 and not if pCR ≤ 0.10 (α=0.10, β=0.10). A second cohort was opened, changing the NAS sequence to ddAC followed by E (Cohort B). All pts had 2 research breast biopsies (rbx) for genomic analysis at baseline and 1 wk after first dose of NAS. An imaging study evaluated breast DCE-MRI and DWI at baseline and 1 wk after first dose of NAS. Tofts 2-compartment pharmacokinetic (PK) model calculated Ktrans, Ve, Kep, and iAUC from DCE. ADC was calculated from DWI. The change in Ktrans, Ve, Kep, iAUC, and ADC between baseline and wk 1 was evaluated using a signed-rank test. The magnitude of these changes was compared between Cohort A and Cohort B using a Wilcoxon rank-sum test. All testing was two-tailed and exact p-values were used; p-value &amp;lt; 0.05 were considered statistically significant. Results: 22 pts were enrolled, 16 pts in Cohort A and 6 pts in Cohort B before the study was closed due to slow accrual. Median age was 58 years, 3 pts (13.6%) had triple negative disease. 13 pts (59%) were postmenopausal. All but 1 had stage III disease. All 22 pts completed NAS followed by MRM + PMRT. 19 pts completed full course of NAS, 1 pt received E followed by 3 cycles of AC and 2 pts received 3 cycles of E in addition to 4 cycles of AC. In Cohort A, 1/16 pts had pCR (6.3%; 80% CI 0,22.2%). At a median follow up of 51 months, 3 pts had distant disease recurrence with 1 death; no local-regional recurrence. 7 pts experienced grade (gd) 3 or 4 adverse event (AE); most common were neutropenia and aminotransferase increase. 10 pts participated in the DCE-MRI and DWI substudy: 5 from Cohort A, 5 from Cohort B. Decrease in value from baseline to wk 1 was detected in Ktrans (median -49), Ve (-128), and iAUC (-57). The magnitude of changes was not different when comparing Cohort A to Cohort B. There was no significant change in Kep or ADC. Given the small size of each group, a lack of statistical significance should not be interpreted as suggestive of no effect. Evaluation of changes in angiogenesis- and EMT-related gene expression in rbx + residual disease is ongoing.Conclusion: A regimen of E + ddAC is tolerable NAS for HER2- IBC. All pts underwent curative surgery despite low pCR rate, commonly observed in ER+ IBC. There were changes in some DCE-MRI parameters after exposure to chemotherapy but may not be specific to E. Studies specifically designed for IBC are needed to enhance the understanding of this disease. Clinical trial: NCT02623972 Citation Format: Filipa Lynce, Eren D. Yeh, Meredith M. Regan, Lei Qin, Camden P. Bay, Ian Krop, Beth T Harrison, Faina Nakhlis, Jennifer Bellon, Beth Overmoyer. A phase 2 study of neoadjuvant systemic therapy with eribulin followed by doxorubicin and cyclophosphamide for HER2-negative inflammatory breast cancer [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P2-12-18.

Abstract P4-06-02: Clinically feasible pharmacokinetic model of dynamic contrast-enhanced MRI is prognostic of recurrence free survival in breast cancer patients treated with neoadjuvant therapy

Abstract Background Dynamic contrast-enhanced (DCE) MRI is commonly used for diagnosis and treatment planning of cancers. Its main use is the identification of tumors in soft tissues, where the tumor progression-induced angiogenic changes result in leaky vasculature which is readily highlighted by contrast enhancement. While allowing tumor morphology assessment, DCE MRI also contains rich information on perfusion and permeability which can be modeled as a pharmacokinetic (PK) process, a subject of extensive investigation in the clinical research setting. Several breast DCE MRI studies have suggested that PK parameters might be indicative of response to therapy and recurrence. However, these studies rely on high temporal resolution DCE MRI for accurate PK modeling, which presents a hurdle to adoption in clinical settings where DCE MRI is acquired with low temporal resolution. Methods We present a novel two-parameter modeling framework which circumvents this temporal limitation, allowing kinetic parameters to be extracted from clinical DCE MRI containing 3-6 timepoints spaced 60-90 seconds apart. We sought to examine whether these kinetic parameters (hereafter KP1 and KP2) binned into "high" and "low" values had prognostic value in determining disease recurrence. KP1 reflects the rate of leakiness from vessels into extravascular space, while KP2 reflects the rate of the reverse process. A study was conducted with a cohort of 458 breast cancer patients treated with neoadjuvant therapy (NAT) in which 117 recurrences occurred. Model parameters were extracted from pre-treatment clinical DCE MRI with low temporal resolution. Univariate and multivariate (Cox Proportional Hazard; CPH) analyses were performed to test the prognostic value of covariates in predicting recurrence free survival (RFS). Results A CPH model was constructed that included age, receptor status, cancer stage, kinetic parameters, and pathologic complete response (pCR) as covariates (Table 1). KP2 and pCR were statistically significantly prognostic markers of RFS (p&amp;lt;0.05). KP1 was not a significant prognostic marker of RFS (p=0.055). Interestingly, KP2 had a hazard ratio (HR) similar to pCR (HR: -1.04 [-1.75, -0.33]; p&amp;lt;0.0039 vs. HR: -0.97 [-1.5, -0.44], p&amp;lt;0.00034). Using univariate analysis, we investigated whether KP2 was prognostic of recurrence. Patients were stratified by therapy type, whether they achieved pCR or had residual disease (RD), and by disease subtype. KP2 was found to be predictive of RFS for patients with RD after treatment with NAT chemotherapy (p&amp;lt;0.0004, n=241, log-rank test), but not for patients treated with anti-HER2 NAT (p=0.84, n=124) or patients that achieved pCR (p=0.68, n=139). KP2 was also found to be prognostic in Hormone Receptor+/HER2- patients with RD (p&amp;lt;0.012, n=151) and Hormone Receptor-/HER2+ patients treated with NAT chemotherapy (p&amp;lt;0.001, n=64). The parameter was not found to be prognostic in patients with triple-negative disease. Conclusions We developed a new prognostic imaging marker for disease recurrence in breast cancer patients treated with NAT that relies on only pre-treatment standard-of-care, low temporal resolution DCE MRI. Since the marker was found to have similar prognostic value in terms of hazard ratio as pCR, but in patients with RD, it could prove to be a powerful tool in patient management after surgery. Table 1.Results of Cox Proportional Hazard model.FactorHazard Ratio95% CIp-valueAge-0.01[-0.02, 0.01]0.51ER+-0.35[-0.85, 0.15]0.17HER2+0.02[-0.38, 0.41]0.93PR+0.05[-0.46, 0.56]0.85Stage II0.54[-0.62, 1.7]0.36Stage III1.08[-0.1, 2.27]0.074KP1-0.68[-1.38, 0.01]0.055KP2-1.04[-1.75, -0.33]0.0039pCR-0.97[-1.5, -0.44]0.00034 Citation Format: Joseph R Peterson, Tyler M. Earnest, Wei Huang, Hiroyuki Abe, John A Cole, Jr. Clinically feasible pharmacokinetic model of dynamic contrast-enhanced MRI is prognostic of recurrence free survival in breast cancer patients treated with neoadjuvant therapy [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P4-06-02.

Quantitative Predictors of Response to Neoadjuvant Chemotherapy on Dynamic Contrast-enhanced 3T Breast MRI.

To assess whether changes in quantitative parameters on breast MRI better predict pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer than change in volume. This IRB-approved retrospective study included women with newly diagnosed breast cancer who underwent 3T MRI before and during NAC from January 2013 to December 2019 and underwent surgery at our institution. Clinical data such as age, histologic diagnosis and grade, biomarker status, clinical stage, maximum index cancer dimension and volume, and surgical pathology (presence or absence of in-breast pCR) were collected. Quantitative parameters were calculated using software. Correlations between clinical features and MRI quantitative measures in pCR and non-pCR groups were assessed using univariate and multivariate logistic regression. A total of 182 women with a mean age of 52 years (range, 26-79 years) and 187 cancers were included. Approximately 45% (85/182) of women had pCR at surgery. Stepwise multivariate regression analysis showed statistical significance for changes in quantitative parameters (increase in time to peak and decreases in peak enhancement, wash out, and Kep [efflux rate constant]) for predicting pCR. These variables in combination predicted pCR with 81.2% accuracy and an area under the curve (AUC) of 0.878. The AUCs of change in index cancer volume and maximum dimension were 0.767 and 0.613, respectively. Absolute changes in quantitative MRI parameters between pre-NAC MRI and intra-NAC MRI could help predict pCR with excellent accuracy, which was greater than changes in index cancer volume and maximum dimension.

Ultrafast dynamic contrast-enhanced breast MRI: association with pathologic complete response in neoadjuvant treatment of breast cancer.

The purpose of this study was to investigate whether pretreatment kinetic features from ultrafast DCE-MRI are associated with pathological complete response (pCR) in patients with invasive breast cancer and according to immunohistochemistry (IHC) subtype. Between August 2018 and June 2019, 256 consecutive breast cancer patients (mean age, 50.2years; range, 25-86years) who underwent both ultrafast and conventional DCE-MRI and surgery following neoadjuvant chemotherapy were included. DCE-MRI kinetic features were obtained from pretreatment MRI data. Time-to-enhancement, maximal slope (MS), and volumes at U1 and U2 (U1, time point at which the lesion starts to enhance; U2, subsequent time point after U1) were derived from ultrafast MRI. Logistic regression analysis was performed to identify factors associated with pCR. Overall, 41.4% of all patients achieved pCR. None of the kinetic features was associated with pCR when including all cancers. Among ultrafast DCE-MRI kinetic features, a lower MS (OR, 0.982; p = 0.040) was associated with pCR at univariable analysis in hormone receptor (HR)-positive cancers. In triple-negative cancers, a higher volume ratio U1/U2 was associated with pCR at univariable (OR, 11.787; p = 0.006) and multivariable analysis (OR, 14.811; p = 0.005). Among conventional DCE-MRI kinetic features, a lower peak enhancement (OR, 0.993; p = 0.031) and a lower percentage of washout (OR, 0.904; p = 0.039) was associated with pCR only in HR-positive cancers at univariable analysis. A higher volume ratio of U1/U2 derived from ultrafast DCE-MRI was independently associated with pCR in triple-negative invasive breast cancer. • The ratio of tumor volumes obtained at the first (U1) and second time points (U2) of enhancement was independently associated with pCR in triple-negative invasive breast cancers. • Ultrafast MRI has the potential to improve accuracy in predicting treatment response and personalizing therapy.

Breast DCE-MRI in long-term breast cancer survivors: What lies beneath dystrophic calcifications due to fat necrosis?

Breast cancer becomes a chronic condition due to advances in diagnosis and treatment. This fact emphasizes the awareness of late post-treatment sequelae, such as dystrophic calcifications due to fat necrosis. In the case of extremely numerous, diffuse dystrophic calcifications, a coexistent carcinoma can be masked on the clinical, mammographic and ultrasound examination. Breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a problem solving tool, allowing visualization of soft tissues and detection of angiogenesis.

Diagnosing breast cancer tumors using stacked ensemble model

Breast cancer is the most common cancer that progresses from cells in the breast tissue among women. Early-stage detection could reduce death rates significantly, and the detection-stage determines the treatment process. Mammography is utilized to discover breast cancer at an early stage prior to any physical sign. However, mammography might return false-negative, in which case, if it is suspected that lesions might have cancer of chance greater than two percent, a biopsy is recommended. About 30 percent of biopsies result in malignancy that means the rate of unnecessary biopsies is high. So to reduce unnecessary biopsies, recently, due to its excellent capability in soft tissue imaging, Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) has been utilized to detect breast cancer. Nowadays, DCE-MRI is a highly recommended method not only to identify breast cancer but also to monitor its development, and to interpret tumorous regions. However, in addition to being a time-consuming process, the accuracy depends on radiologists’ experience. Radiomic data, on the other hand, are used in medical imaging and have the potential to extract disease characteristics that can not be seen by the naked eye. Radiomics are hard-coded features and provide crucial information about the disease where it is imaged. Conversely, deep learning methods like convolutional neural networks(CNNs) learn features automatically from the dataset. Especially in medical imaging, CNNs’ performance is better than compared to hard-coded features-based methods. However, combining the power of these two types of features increases accuracy significantly, which is especially critical in medicine. Herein, a stacked ensemble of gradient boosting and deep learning models were developed to classify breast tumors using DCE-MRI images. The model makes use of radiomics acquired from pixel information in breast DCE-MRI images. Prior to train the model, radiomics had been applied to the factor analysis to refine the feature set and eliminate unuseful features. The performance metrics, as well as the comparisons to some well-known machine learning methods, state the ensemble model outperforms its counterparts. The ensembled model’s accuracy is 94.87% and its AUC value is 0.9728. The recall and precision are 1.0 and 0.9130, respectively, whereas F1-score is 0.9545.

A Pipelined Tracer-Aware Approach for Lesion Segmentation in Breast DCE-MRI.

The recent spread of Deep Learning (DL) in medical imaging is pushing researchers to explore its suitability for lesion segmentation in Dynamic Contrast-Enhanced Magnetic-Resonance Imaging (DCE-MRI), a complementary imaging procedure increasingly used in breast-cancer analysis. Despite some promising proposed solutions, we argue that a “naive” use of DL may have limited effectiveness as the presence of a contrast agent results in the acquisition of multimodal 4D images requiring thorough processing before training a DL model. We thus propose a pipelined approach where each stage is intended to deal with or to leverage a peculiar characteristic of breast DCE-MRI data: the use of a breast-masking pre-processing to remove non-breast tissues; the use of Three-Time-Points (3TP) slices to effectively highlight contrast agent time course; the application of a motion-correction technique to deal with patient involuntary movements; the leverage of a modified U-Net architecture tailored on the problem; and the introduction of a new “Eras/Epochs” training strategy to handle the unbalanced dataset while performing a strong data augmentation. We compared our pipelined solution against some literature works. The results show that our approach outperforms the competitors by a large margin (+9.13% over our previous solution) while also showing a higher generalization ability.

Enhancement-constrained acceleration: A robust reconstruction framework in breast DCE-MRI.

In patients with dense breasts or at high risk of breast cancer, dynamic contrast enhanced MRI (DCE-MRI) is a highly sensitive diagnostic tool. However, its specificity is highly variable and sometimes low; quantitative measurements of contrast uptake parameters may improve specificity and mitigate this issue. To improve diagnostic accuracy, data need to be captured at high spatial and temporal resolution. While many methods exist to accelerate MRI temporal resolution, not all are optimized to capture breast DCE-MRI dynamics. We propose a novel, flexible, and powerful framework for the reconstruction of highly-undersampled DCE-MRI data: enhancement-constrained acceleration (ECA). Enhancement-constrained acceleration uses an assumption of smooth enhancement at small time-scale to estimate points of smooth enhancement curves in small time intervals at each voxel. This method is tested in silico with physiologically realistic virtual phantoms, simulating state-of-the-art ultrafast acquisitions at 3.5s temporal resolution reconstructed at 0.25s temporal resolution (demo code available here). Virtual phantoms were developed from real patient data and parametrized in continuous time with arterial input function (AIF) models and lesion enhancement functions. Enhancement-constrained acceleration was compared to standard ultrafast reconstruction in estimating the bolus arrival time and initial slope of enhancement from reconstructed images. We found that the ECA method reconstructed images at 0.25s temporal resolution with no significant loss in image fidelity, a 4x reduction in the error of bolus arrival time estimation in lesions (p < 0.01) and 11x error reduction in blood vessels (p < 0.01). Our results suggest that ECA is a powerful and versatile tool for breast DCE-MRI.

Imaging characteristics of young age breast cancer (YABC) focusing on pathologic correlation and disease recurrence

The purpose of this study is to investigate imaging characteristics of young age breast cancer (YABC) focusing on correlation with pathologic factors and association with disease recurrence. From January 2017 to December 2019, patients under 40 years old who were diagnosed as breast cancer were enrolled in this study. Morphologic analysis of tumor and multiple quantitative parameters were obtained from pre-treatment dynamic contrast enhanced breast magnetic resonance imaging (DCE-MRI). Tumor-stroma ratio (TSR), microvessel density (MVD) and endothelial Notch 1 (EC Notch 1) were investigated for correlation with imaging parameters. In addition, recurrence associated factors were assessed using both clinico-pathologic factors and imaging parameters. A total of 53 patients were enrolled. Several imaging parameters derived from apparent diffusion coefficient (ADC) histogram showed negative correlation with TSR; and there was negative correlation between MVD and Ve in perfusion analysis. There were nine cases of recurrences with median interval of 16 months. Triple negative subtype and low CD34 MVD positivity in Notch 1 hotspots showed significant association with tumor recurrence. Texture parameters reflecting tumor sphericity and homogeneity were also associated with disease recurrence. In conclusion, several quantitative MRI parameters can be used as imaging biomarkers for tumor microenvironment and can predict disease recurrence in YABC.

3D DCE-MRI Radiomic Analysis for Malignant Lesion Prediction in Breast Cancer Patients

To develop and validate a radiomic model, with radiomic features extracted from breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) from a 1.5T scanner, for predicting the malignancy of masses with enhancement. Images were acquired using an 8-channel breast coil in the axial plane. The rationale behind this study is to show the feasibility of a radiomics-powered model that could be integrated into the clinical practice by exploiting only standard-of-care DCE-MRI with the goal of reducing the required image pre-processing (ie, normalization and quantitative imaging map generation). 107 radiomic features were extracted from a manually annotated dataset of 111 patients, which was split into discovery and test sets. A feature calibration and pre-processing step was performed to find only robust non-redundant features. An in-depth discovery analysis was performed to define a predictive model: for this purpose, a Support Vector Machine (SVM) was trained in a nested 5-fold cross-validation scheme, by exploiting several unsupervised feature selection methods. The predictive model performance was evaluated in terms of Area Under the Receiver Operating Characteristic (AUROC), specificity, sensitivity, PPV and NPV. The test was performed on unseen held-out data. The model combining Unsupervised Discriminative Feature Selection (UDFS) and SVMs on average achieved the best performance on the blinded test set: AUROC=0.725±0.091, sensitivity=0.709±0.176, specificity=0.741±0.114, PPV=0.72±0.093, and NPV=0.75±0.114. In this study, we built a radiomic predictive model based on breast DCE-MRI, using only the strongest enhancement phase, with promising results in terms of accuracy and specificity in the differentiation of malignant from benign breast lesions.

Correlations between dynamic contrast-enhanced magnetic resonance imaging parameters and histopathologic factors in breast cancer.

This study aimed to correlate dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters with prognostic factors in breast cancer. A retrospective analysis was performed in 45 patients who had breast DCE-MRI and were diagnosed with invasive ductal breast cancer following surgery. The following DCE-MRI parameters were calculated: percentage of initial peak enhancement (Epeak), time to initial peak enhancement (TTP), initial slope increase (IS), early signal enhancement ratio (ESER), and time-signal intensity curve (TIC) types. Correlations between Epeak, TTP, IS, ESER, and prognostic factors were determined using the Mann-Whitney U test and the Kruskal-Wallis test. The Chi-square test and Fisher's exact test were used to investigate the relationship between kinetic curve types and prognostic factors. Epeak and IS were significantly higher in the Ki-67 high-expression group than in the Ki-67 low-expression group (p = 0.031 and p = 0.012, respectively). ESER was significantly correlated with the histological type and Ki-67 expression level (p = 0.014 and p = 0.047, respectively). The TIC types were significantly correlated with the Ki-67 expression level (p = 0.009). Tumors with plateaus and washout curves were more likely to be associated with the Ki-67 high-expression group than tumors with a persistent curve (P = 0.027 and P = 0.038, respectively). TTP had no correlation with any prognostic factors (P > 0.05). This study showed that the DCE-MRI parameters of breast cancer were correlated with the expression of histopathological prognostic factors and might be useful for predicting prognosis.

A Novel 3-D-CAD for Breast and Lesion Segmentation of Axial Breast DCE-MRI

Abstract The present paper proposes a fully automated three-dimensional (3-D) system for breast and lesion segmentation of Dynamic Contrast Enhanced MRI (DCE-MRI). Such a system as the Computer-Aided Diagnostic system (CAD) can be used to support radiologists by marking suspicious areas. The proposed 3-D-CAD system includes three modules. The first one concerns breast area segmentation based on image content analysis, the Moore-Neighbor tracing algorithm, and the Dijkstra algorithm. The second one concerns the automation of locating and selecting lesions that starts by preprocessing the already segmented breast regions; then, a K-means algorithm allows extraction of regions including suspicious tissues. The third one is the superimposition of all detected lesions from selected slices to create a 3-D view of the lesion. The 3-D reconstruction is based on the Marching Cube algorithm. The validation of breast area segmentation reveals the robustness of the proposed process versus different breast densities, complex forms, and challenging cases. The segmentation of the breast part from 120 slices with the proposed method is achieved in 20.57 ± 5.2 s, which is faster than existing methods. In addition to calculated metrics as Dice similarity coefficient (DSC) and rates of true positives, the module of lesion extraction is validated by experimented radiologists. The proposed workflow of this module shows competitive results compared to the existing methods of automated lesion segmentation and a total ability for eliminating the extra regions to lesions.

Texture Analysis of DCE-MRI Intratumoral Subregions to Identify Benign and Malignant Breast Tumors.

PurposeTo evaluate the potential of the texture features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) intratumoral subregions to distinguish benign from malignant breast tumors.Materials and MethodsA total of 299 patients with pathologically verified breast tumors who underwent breast DCE-MRI examination were enrolled in this study, including 124 benign cases and 175 malignant cases. The whole tumor area was semi-automatically segmented on the basis of subtraction images of DCE-MRI in Matlab 2018b. According to the time to peak of the contrast agent, the whole tumor area was partitioned into three subregions: early, moderate, and late. A total of 467 texture features were extracted from the whole tumor area and the three subregions, respectively. Patients were divided into training (n = 209) and validation (n = 90) cohorts by different MRI scanners. The least absolute shrinkage and selection operator (LASSO) method was used to select the optimal feature subset in the training cohort. The Kolmogorov-Smirnov test was first performed on texture features selected by LASSO to test whether the samples followed a normal distribution. Two machine learning methods, decision tree (DT) and support vector machine (SVM), were used to establish classification models with a 10-fold cross-validation method. The performance of the classification models was evaluated with receiver operating characteristic (ROC) curves.ResultsIn the training cohort, the areas under the ROC curve (AUCs) for the DT_Whole model and SVM_Whole model were 0.744 and 0.806, respectively. In contrast, the AUCs of the DT_Early model (P = 0.004), DT_Late model (P = 0.015), SVM_Early model (P = 0.002), and SVM_Late model (P = 0.002) were significantly higher: 0.863 (95% CI, 0.808–0.906), 0.860 (95% CI, 0.806–0.904), 0.934 (95% CI, 0.891–0.963), and 0.921 (95% CI, 0.876–0.954), respectively. The SVM_Early model and SVM_Late model achieved better performance than the DT_Early model and DT_Late model (P = 0.003, 0.034, 0.008, and 0.026, respectively). In the validation cohort, the AUCs for the DT_Whole model and SVM_Whole model were 0.670 and 0.708, respectively. In comparison, the AUCs of the DT_Early model (P = 0.006), DT_Late model (P = 0.043), SVM_Early model (P = 0.001), and SVM_Late model (P = 0.007) were significantly higher: 0.839 (95% CI, 0.747–0.908), 0.784 (95% CI, 0.601–0.798), 0.890 (95% CI, 0.806–0.946), and 0.865 (95% CI, 0.777–0.928), respectively.ConclusionThe texture features from intratumoral subregions of breast DCE-MRI showed potential in identifying benign and malignant breast tumors.

Breast DCE-MRI segmentation for lesion detection by multi-level thresholding using student psychological based optimization

In recent years, the high prevalence of breast cancer in women has risen dramatically. Therefore, segmentation of breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is a necessary task to assist the radiologist inaccurate diagnosis and detection of breast cancer in breast DCE-MRI. For image segmentation, thresholding is a simple and effective method. In breast DCE-MRI analysis for lesion detection and segmentation, radiologists agree that optimization via multi-level thresholding technique is important to differentiate breast lesions from dynamic DCE-MRI. In this paper, multi-level thresholding using Student Psychology-Based Optimizer (SPBO) is proposed to segment the breast DCE-MR images for lesion detection. First, MR images are denoised using the anisotropic diffusion filter and then, Intensity Inhomogeneities (IIHs) are corrected in the preprocessing step. The preprocessed MR images are segmented using the SPBO algorithm. Finally, the lesions are extracted from the segmented images and localized in the original MR images. The proposed method is applied on 300 Sagittal T2-Weighted DCE-MRI slices of 50 patients, histologically proven, and analyzed. The proposed method is compared with algorithms such as Particle Swarm Optimizer (PSO), Dragonfly Optimization (DA), Slime Mould Optimization (SMA), Multi-Verse Optimization (MVO), Grasshopper Optimization Algorithm (GOA), Hidden Markov Random Field (HMRF), Improved Markov Random Field (IMRF), and Conventional Markov Random Field (CMRF) methods. The high accuracy level of 99.44%, sensitivity 96.84%, and Dice Similarity Coefficient (DSC) 93.41% are achieved using the proposed automatic segmentation method. Both quantitative and qualitative results demonstrate that the proposed method performs better than the eight compared methods.

An automatic Computer-Aided Diagnosis system based on the Multimodal fusion of Breast Cancer (MF-CAD)

The risk of death incurred by breast cancer is rising exponentially, especially among women. The early breast cancer diagnosis before it metastasizes helps medical staff controlling this disease, which decreases the risk of death. This made early breast cancer detection a crucial problem. Different imaging modalities offer complementary information concerning the same lesion helps to increase the performance of thcy fusing several modalities. This paper proposes a computerized features classification of breast cancer lesions through both the Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) and Digital Mammographic images (MGs). This study aims to investigate a Multimodal Fusion-based Computer-Aided Diagnosis (CAD) system, called MF-CAD, based on multivariate analysis of different modalities, for breast cancer mass detection. In this paper, firstly a new local feature descriptor is proposed in feature extraction, namely, the Gradient Local Information Pattern (GLIP), where we consider the gradient magnitude and orientation as well as the local differences as local binary features for DCE-MRI (or MGs) modality. Secondly, the fusion scheme is conducted using the Canonical Correlation Analysis (CCA) to highlight the intrinsic relation between these modalities. Finally, for comparative purposes, several selected machine learning classifiers (K-Nearest Neighbors, Support Vector Machine, Random forests, Artificial Neural Networks and Radial Basis Function Neural Network (RBFNN)) are used to distinguish between mass and No-mass breast images.Evaluation experiments of the diagnostic performances of our MF-CAD system are conducted over private datasets that contain both MG and DCE-MRI images acquired from 286 patients, which are “Breast DCE-MRI”, “Breast-MG” and “Breast Multimodal” datasets. Experimental results of the proposed MF-CAD system achieved an Area Under the ROC Curve (AUC) value of 99.10% using RBFNN classifier, while for each single modality alone, the best AUC values of 97.20% and 93.50% are obtained respectively for MG and DCE-MRI modalities using random forest classifier. A comparative study with recent existing approaches shows the competitive performances of the proposed approach.

Characterizing Errors in Pharmacokinetic Parameters from Analyzing Quantitative Abbreviated DCE-MRI Data in Breast Cancer.

This study characterizes the error that results when performing quantitative analysis of abbreviated dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data of the breast with the Standard Kety–Tofts (SKT) model and its Patlak variant. More specifically, we used simulations and patient data to determine the accuracy with which abbreviated time course data could reproduce the pharmacokinetic parameters, Ktrans (volume transfer constant) and ve (extravascular/extracellular volume fraction), when compared to the full time course data. SKT analysis of simulated abbreviated time courses (ATCs) based on the imaging parameters from two available datasets (collected with a 3T MRI scanner) at a temporal resolution of 15 s (N = 15) and 7.23 s (N = 15) found a concordance correlation coefficient (CCC) greater than 0.80 for ATCs of length 3.0 and 2.5 min, respectively, for the Ktrans parameter. Analysis of the experimental data found that at least 90% of patients met this CCC cut-off of 0.80 for the ATCs of the aforementioned lengths. Patlak analysis of experimental data found that 80% of patients from the 15 s resolution dataset and 90% of patients from the 7.27 s resolution dataset met the 0.80 CCC cut-off for ATC lengths of 1.25 and 1.09 min, respectively. This study provides evidence for both the feasibility and potential utility of performing a quantitative analysis of abbreviated breast DCE-MRI in conjunction with acquisition of current standard-of-care high resolution scans without significant loss of information in the community setting.

The value of dynamic contrast-enhanced magnetic resonance imaging combined with apparent diffusion coefficient in the differentiation of benign and malignant diseases of the breast.

The value of combined dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and apparent diffusion coefficient (ADC) histogram analysis for the diagnosis of breast cancer has not been evaluated in previous studies. To investigate the diagnostic value of DCE-MRI combined with ADC in benign and malignant breast lesions. The clinicopathological imaging data included 168 patients (177 lesions) with breast lesions who underwent convention breast MRI, DCE-MRI, and diffusion-weighted imaging (DWI); they were divided into the benign lesion group (n = 39) and malignant lesion group (n = 129) based on pathology. Using the type III outflow curve as a diagnostic criterion for malignant breast lesions, the diagnostic sensitivity was 76.9%, the specificity was 80%, the correct rate was 72.2%, and its area under the curve (AUC) was 0.823. Using an enhancement ratio > 100% as a diagnostic criterion for malignant breast lesions, the sensitivity was 61.5%, specificity was 80%, and AUC was 0.723. Using > 3 ipsilateral vessels as a diagnostic criterion for malignant lesions in the breast resulted in a diagnostic sensitivity of 81.6%, a specificity of 80.8%, and an AUC of 0.805. The type of time intensity curve DCE-MRI, the early enhancement rate in the first phase, the number of ipsilateral vessels, and the ADC full volume histogram of the blood supply score and DWI are valuable in the diagnosis of benign and malignant breast lesions.