Differentiation between benign and malignant breast lesions detected by bilateral dynamic contrast-enhanced MRI: a sensitivity and specificity study

Quantitative analysis from ultrafast dynamic contrast-enhanced breast MRI using population-based versus individual arterial input functions, and comparison with semi-quantitative analysis

PURPOSE

To evaluate the value of inline quantitative analysis of ultrafast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using a population-based arterial input function (P-AIF) compared with offline quantitative analysis with an individual AIF (I-AIF) and semi-quantitative analysis for diagnosing breast cancer.

METHODS

This prospective study included 99 consecutive patients with 109 lesions (85 malignant and 24 benign). Model-based parameters (Ktrans, kep, and ve) and model-free parameters (washin and washout) were derived from CAIPIRINHA-Dixon-TWIST-VIBE (CDTV) DCE-MRI. Univariate analysis and multivariate logistic regression analysis with forward stepwise covariate selection were performed to identify significant variables. The AUC and F1 score were assessed for semi-quantitative and two quantitative analyses.

RESULTS

kep from inline quantitative analysis with P-AIF for diagnosing breast cancer provided an AUC similar to kep from offline quantitative analysis with I-AIF (0.782 vs 0.779, p = 0.954), higher compared to washin from semi-quantitative analysis (0.782 vs 0.630, p = 0.034). Furthermore, the inline quantitative analysis with P-AIF achieved the larger F1 score (0.920) compared with offline quantitative analysis with I-AIF (0.780) and semi-quantitative analysis (0.480). There were no statistically significant differences for kep values between the two quantitative analysis schemes (p = 0.944).

CONCLUSION

The inline quantitative analysis with P-AIF from CDTV in characterizing breast lesions could offer similar diagnostic accuracy to offline quantitative analysis with I-AIF, and higher diagnostic accuracy to semi-quantitative analysis.

Differentiation of silent corticotroph pituitary neuroendocrine tumors (PitNETs) from non-functioning PitNETs using kinetic analysis of dynamic MRI

PURPOSE

Silent corticotroph pituitary adenomas (SCAs)/pituitary neuroendocrine tumors (PitNETs) are common non-functioning pituitary adenomas (NFAs)/PitNETs with a clinically aggressive course. This study aimed to investigate the ability of time-intensity analysis of dynamic magnetic resonance imaging (MRI) for distinguishing adrenocorticotropic hormone (ACTH)-positive SCAs and ACTH-negative SCAs from other NFAs.

MATERIALS AND METHODS

We retrospectively evaluated the dynamic MRI findings of patients with NFAs. The initial slope of the kinetic curve (slopeini) obtained by dynamic MRI for each tumor was analyzed using a modified empirical mathematical model. The maximum slope of the kinetic curve (slopemax) was obtained by geometric calculation.

RESULTS

A total of 106 patients with NFAs (11 ACTH-positive SCAs, 5 ACTH-negative SCAs, and 90 other NFAs) were evaluated. The kinetic curves of ACTH-positive SCAs had significantly lesser slopeini and slopemax compared with ACTH-negative SCAs (P = 0.040 and P = 0.001, respectively) and other NFAs (P = 0.018 and P = 0.035, respectively). Conversely, the slopeini and slopemax were significantly greater in ACTH-negative SCAs than in NFAs other than ACTH-negative SCAs (P = 0.033 and P = 0.044, respectively). In receiver operating characteristic analysis of ACTH-positive SCAs and other NFAs, the area under the curve (AUC) values for slopeini and slopemax were 0.762 and 0748, respectively. In predicting ACTH-negative SCAs, the AUC values for slopeini and slopemax were 0.784 and 0.846, respectively.

CONCLUSIONS

Dynamic MRI can distinguish ACTH-positive SCAs and ACTH-negative SCAs from other NFAs.

Prediction of Lung Shunt Fraction for Yttrium-90 Treatment of Hepatic Tumors Using Dynamic Contrast Enhanced MRI with Quantitative Perfusion Processing

There is no noninvasive method to estimate lung shunting fraction (LSF) in patients with liver tumors undergoing Yttrium-90 (Y90) therapy. We propose to predict LSF from noninvasive dynamic contrast enhanced (DCE) MRI using perfusion quantification. Two perfusion quantification methods were used to process DCE MRI in 25 liver tumor patients: Kety's tracer kinetic modeling with a delay-fitted global arterial input function (AIF) and quantitative transport mapping (QTM) based on the inversion of transport equation using spatial deconvolution without AIF. LSF was measured on SPECT following Tc-99m macroaggregated albumin (MAA) administration via hepatic arterial catheter. The patient cohort was partitioned into a low-risk group (LSF ≤ 10%) and a high-risk group (LSF > 10%). Results: In this patient cohort, LSF was positively correlated with QTM velocity |u| (r = 0.61, F = 14.0363, p = 0.0021), and no significant correlation was observed with Kety's parameters, tumor volume, patient age and gender. Between the low LSF and high LSF groups, there was a significant difference for QTM |u| (0.0760 ± 0.0440 vs. 0.1822 ± 0.1225 mm/s, p = 0.0011), and Kety's Ktrans (0.0401 ± 0.0360 vs 0.1198 ± 0.3048, p = 0.0471) and Ve (0.0900 ± 0.0307 vs. 0.1495 ± 0.0485, p = 0.0114). The area under the curve (AUC) for distinguishing between low LSF and high LSF was 0.87 for |u|, 0.80 for Ve and 0.74 for Ktrans. Noninvasive prediction of LSF is feasible from DCE MRI with QTM velocity postprocessing.

Quantitative classification of invasive and noninvasive breast cancer using dynamic magnetic resonance imaging of the mammary gland

Objectives

Breast cancers are classified as invasive or noninvasive based on histopathological findings. Although time-intensity curve (TIC) analysis using magnetic resonance imaging (MRI) can differentiate benign from malignant disease, its diagnostic ability to quantitatively distinguish between invasive and noninvasive breast cancers has not been determined. In this study, we evaluated the ability of TIC analysis of dynamic MRI data (MRI-TIC) to distinguish between invasive and noninvasive breast cancers.

Material and Methods

We collected and analyzed data for 429 cases of epithelial invasive and noninvasive breast carcinomas. TIC features were extracted in washout areas suggestive of malignancy.

Results

The graph determining the positive diagnosis rate for invasive and noninvasive cases revealed that the cut-off θ_i/ni value was 21.6° (invasive: θ_w > 21.6°, noninvasive: θ_w ≤ 21.6°). Tissues were classified as invasive or noninvasive using this cut-off value, and each result was compared with the histopathological diagnosis. Using this method, the accuracy of tissue classification by MRI-TIC was 88.6% (380/429), which was higher than that using ultrasound (73.4%, 315/429).

Conclusion

MRI-TIC is effective for the classification of invasive vs. noninvasive breast cancer.

Type 2 dynamic curves: A diagnostic dilemma

BACKGROUND

Magnetic resonance imaging (MRI) with multiparametric dynamic contrast plays a critical role in the assessment of breast lesions. Dynamic curves are a critical parameter in determining the benign or malignant nature of lesions. Dynamic curves of type 1 are known to represent benign masses, while dynamic curves of type 3 are known to identify malignant masses. Type 2 dynamic curves have a sensitivity of 42.6% and specificity of 75% for malignancy detection.

AIM

To investigate the pathological diagnosis of lesions with type 2 dynamic curves.

METHODS

We evaluated breast MRI examinations performed between 2020 and 2021 retrospectively and included lesions with type 2 dynamic curves. We included 38 lesions from 33 patients. The lesions were evaluated for their pathological diagnosis and morphological characteristics.

RESULTS

Twenty-six lesions were malignant, while twelve were benign. The most frequently encountered benign lesion (7/12, 58.3%) was sclerosing adenosis, while the most frequently encountered malignant diagnosis was invasive ductal cancer. The presence of a type 2 dynamic curve had a sensitivity of 40.2% and specificity of 73.4% for predicting malignancy. By combining type 2 curves and morphological features, the sensitivity and specificity were increased.

CONCLUSION

The high rates of malignancy detected histopathologically among patients with type 2 dynamic curves in our study are remarkable. Type 2 dynamic curves can be detected in benign breast masses, especially in sclerosing adenosis cases. Considering morphological features can increase the diagnostic accuracy in cases with type 2 dynamic curves.

Quantitative transport mapping (QTM) for differentiating benign and malignant breast lesion: Comparison with traditional kinetics modeling and semi-quantitative enhancement curve characteristics

PURPOSE

To test the feasibility of using quantitative transport mapping (QTM) method, which is based on the inversion of transport equation using spatial deconvolution without any arterial input function, for automatically postprocessing dynamic contrast enhanced MRI (DCE-MRI) to differentiate malignant and benign breast tumors.

MATERIALS AND METHODS

Breast DCE-MRI data with biopsy confirmed malignant (n = 13) and benign tumors (n = 13) was used to assess QTM velocity (|u|) and diffusion coefficient (D), volume transfer constant (K^trans), volume fraction of extravascular extracellular space (V_e) from kinetics method, and traditional enhancement curve characteristics (ECC: amplitude A, wash-in rate α, wash-out rate β). A Mann-Whitney U test and receiver operating characteristic curve (ROC) analysis were performed to assess the diagnostic performance of these parameters for distinguishing between benign and malignant tumors.

RESULTS

Between malignant and benign tumors, there was a significant difference in |u| and K^trans, (p = 0.0066, 0.0274, respectively), but not in D, V_e, A, α and β (p = 0.1119, 0.2382, 0.4418,0.2592 and 0.9591, respectively). ROC area-under-the-curve was 0.82, 0.75 (95% confidence level 0.60-0.95, 0.51-0.90) for |u| and K^trans, respectively.

CONCLUSION

QTM postprocesses DCE-MRI automatically through deconvolution in space and time to solve the inverse problem of the transport equation. Comparing with traditional kinetics method and ECC, QTM method showed better diagnostic accuracy in differentiating benign from malignant breast tumors in this study.

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.

Improved value of whole-lesion histogram analysis on DCE parametric maps for diagnosing small breast cancer (≤ 1 cm)

OBJECTIVES

To determine if whole-lesion histogram analysis on dynamic contrast-enhanced (DCE) parametric maps help to improve the diagnostic accuracy of small suspicious breast lesions (≤ 1 cm).

METHODS

This retrospective study included 99 female patients with 114 lesions (40 malignant and 74 benign lesions) suspicious on magnetic resonance imaging (MRI).Two radiologists reviewed all lesions and descripted the morphologic and kinetic characteristics according to BI-RADS by consensus. Whole lesions were segmented on DCE parametric maps (washin and washout), and quantitative histogram features were extracted. Univariate analysis and multivariate logistic regression analysis with forward stepwise covariate selection were performed to identify significant variables. Diagnostic performance was assessed and compared with that of qualitative BI-RADS assessment and quantitative histogram analysis by ROC analysis.

RESULTS

For malignancy defined as a washout or plateau pattern, the qualitative kinetic pattern showed a significant difference between the two groups (p = 0.023), yielding an AUC of 0.603 (95% confidence interval [CI]: 0.507, 0.694). The mean and median of washout were independent quantitative predictors of malignancy (p = 0.002, 0.010), achieving an AUC of 0.796 (95% CI: 0. 709, 0.865). The AUC of the quantitative model was better than that of the qualitative model (p < 0.001).

CONCLUSIONS

Compared with the qualitative BI-RADS assessment, quantitative whole-lesion histogram analysis on DCE parametric maps was better to discriminate between small benign and malignant breast lesions (≤ 1 cm) initially defined as suspicious on DCE-MRI.

KEY POINTS

• For malignancy defined as a washout or plateau, the kinetic pattern may provide information to diagnose small breast cancer. • The mean and median of washout map were significantly lower for small malignant breast lesions than for benign lesions. • Quantitative histogram analysis on MRI parametric maps improves diagnostic accuracy for small breast cancer, which may obviate unnecessary biopsy.

Apparent diffusion coefficient values in borderline breast lesions upgraded and not upgraded at definitive histopathological examination after surgical excision

Purpose

The study aims were to evaluate if the apparent diffusion coefficient (ADC) value could distinguish between breast lesions classified as B3 at core needle biopsy (CNB) that show or do not show atypia or malignancy at definitive histopathological examination (DHE) after surgical excision.

Material and methods

From January 2013 to December 2017, 141 patients with a B3 breast lesion underwent magnetic resonance imaging and were included in the study. The ADC value was assessed drawing a ROI outlining the entire lesion, evaluating the mean (ADC_mean) and minimum ADC values (ADC_min).

Results

Both ADC_mean and ADC_min values showed a statistically significant difference between B3 lesions without and with malignancy or, for B3a lesions, atypia at DHE. They both showed a statistically significant difference also between B3a lesions without or with atypia or malignancy at DHE, but only ADC_min (not ADC_mean) showed statistically significant difference between B3b lesions without or with malignancy at DHE.

Conclusions

The ADC value could help distinguish between B3a lesions without or with atypia/malignancy at DHE after surgical excision and between B3b lesions without or with malignancy at DHE. Therefore, it could be used to help guide the diagnostic-therapeutic pathway of these lesions, particularly of B3a lesions.

Value of ultrafast and standard dynamic contrast-enhanced magnetic resonance imaging in the evaluation of the presence and extension of residual disease after neoadjuvant chemotherapy in breast cancer

PURPOSE

To evaluate the diagnostic performance of ultrafast and standard dynamic contrast-enhanced (DCE)-MRI in evaluating the residual disease after neoadjuvant chemotherapy (NAC) for breast cancer.

MATERIALS AND METHODS

Sixty-seven consecutive patients underwent MRI after NAC. Visual analysis of enhancement was performed on ultrafast and standard DCE-MRI, and compared between no residual disease and residual disease groups. The lesion diameters measured on the last phase of ultrafast DCE-MRI and early and delayed phases of standard DCE-MRI were compared with pathological diameter of entire residual cancer and residual invasive ductal carcinoma (IDC).

RESULTS

The visual analysis in the delayed phase of standard DCE-MRI exhibited the highest sensitivity (90%), whereas ultrafast DCE-MRI revealed the highest positive predictive value (92%). There were no significant differences between the diameters in the delayed phase of the standard DCE-MRI and the pathological entire residual cancer (p = 0.97), and the diameters in ultrafast DCE-MRI and the pathological residual IDC (p = 0.97).

CONCLUSION

The delayed phase of standard DCE-MRI may be effective for detecting the residual disease and evaluating the extension of entire residual cancer. Enhancement in ultrafast DCE-MRI may be strongly suggestive of the presence of residual disease, and effective for evaluating the extension of residual IDC.

Utility of Tissue Classification in Invasive Ductal Carcinoma using Dynamic Magnetic Resonance Imaging of the Mammary Gland

Objectives

In Japan, invasive ductal carcinomas, which account for 75% of breast cancer cases, are sub-classified as solid, tubule-forming, scirrhous, and other types based on the histopathological findings. Although time-intensity curve (TIC) analysis of magnetic resonance (MR) images has shown diagnostic ability in differentiating benign and malignant tumors, its ability to diagnose different tumor tissue types has not yet been achieved. In this study, we report a histological classification of invasive ductal carcinoma using the TIC analysis of dynamic MR images of the mammary gland.

Material and Methods

A total of 312 invasive ductal carcinomas were analyzed, and each tissue type that indicated malignancy in the washout parts of the tumors was classified and characterized using the TIC.

Results

The tissue was classified, and the results were then compared to the pathohistological diagnosis. Using this method, the accuracy of tissue classification by quantitative analysis of TIC-MR images was 86.9% (271/312), which was higher than that obtained by ultrasonography 68.9% (215/312).

Conclusion

This method is effective for classifying tissue types in invasive ductal carcinoma.

A dynamic lesion model for differentiation of malignant and benign pathologies

Malignant lesions have a high tendency to invade their surrounding environment compared to benign ones. This paper proposes a dynamic lesion model and explores the 2nd order derivatives at each image voxel, which reflect the rate of change of image intensity, as a quantitative measure of the tendency. The 2nd order derivatives at each image voxel are usually represented by the Hessian matrix, but it is difficult to quantify a matrix field (or image) through the lesion space as a measure of the tendency. We conjecture that the three eigenvalues contain important information of the Hessian matrix and are chosen as the surrogate representation of the Hessian matrix. By treating the three eigenvalues as a vector, called Hessian vector, which is defined in a local coordinate formed by three orthogonal Hessian eigenvectors and further adapting the gray level occurrence computing method to extract the vector texture descriptors (or measures) from the Hessian vector, a quantitative presentation for the dynamic lesion model is completed. The vector texture descriptors were applied to differentiate malignant from benign lesions from two pathologically proven datasets: colon polyps and lung nodules. The classification results not only outperform four state-of-the-art methods but also three radiologist experts.

Evaluation of metronomic chemotherapy response using diffusion and dynamic contrast-enhanced MRI

PURPOSE

To investigate the feasibility of using diffusion MRI (dMRI) and dynamic contrast-enhanced (DCE) MRI to evaluate the treatment response of metronomic chemotherapy (MCT) in the 4T1 mammary tumor model of locally advanced breast cancer.

METHODS

Twelve Balb/c mice with metastatic breast cancer were divided into treated and untreated (control) groups. The treated group (n = 6) received five treatments of anti-metabolite agent 5-Fluorouracil (5FU) in the span of two weeks. dMRI and DCE-MRI were acquired for both treated and control groups before and after MCT. Immunohistochemically staining and measurements were performed after the post-MRI measurements for comparison.

RESULTS

The control mice had significantly (p<0.005) larger tumors than the MCT treated mice. The DCE-MRI analysis showed a decrease in contrast enhancement for the control group, whereas the MCT mice had a more stable enhancement between the pre-chemo and post-chemo time points. This confirms the antiangiogenic effects of 5FU treatment. Comparing amplitude of enhancement revealed a significantly (p<0.05) higher enhancement in the MCT tumors than in the controls. Moreover, the MCT uptake rate was significantly (p<0.001) slower than the controls. dMRI analysis showed the MCT ADC values were significantly larger than the control group at the post-scan time point.

CONCLUSION

dMRI and DCE-MRI can be used as potential biomarkers for assessing the treatment response of MCT. The MRI and pathology observations suggested that in addition to the cytotoxic effect of cell kills, the MCT with a cytotoxic drug, 5FU, induced changes in the tumor vasculature similar to the anti-angiogenic effect.

Pharmacokinetic parameters and radiomics model based on dynamic contrast enhanced MRI for the preoperative prediction of sentinel lymph node metastasis in breast cancer

Background

To establish pharmacokinetic parameters and a radiomics model based on dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) for predicting sentinel lymph node (SLN) metastasis in patients with breast cancer.

Methods

A total of 164 breast cancer patients confirmed by pathology were prospectively enrolled from December 2017 to May 2018, and underwent DCE-MRI before surgery. Pharmacokinetic parameters and radiomics features were derived from DCE-MRI data. Least absolute shrinkage and selection operator (LASSO) regression method was used to select features, which were then utilized to construct three classification models, namely, the pharmacokinetic parameters model, the radiomics model, and the combined model. These models were built through the logistic regression method by using 10-fold cross validation strategy and were evaluated on the basis of the receiver operating characteristics (ROC) curve. An independent validation dataset was used to confirm the discriminatory power of the models.

Results

Seven radiomics features were selected by LASSO logistic regression. The radiomics model, the pharmacokinetic parameters model, and the combined model yielded area under the curve (AUC) values of 0.81 (95% confidence interval [CI]: 0.72 to 0.89), 0.77 (95% CI: 0.68 to 0.86), and 0.80 (95% CI: 0.72 to 0.89), respectively, for the training cohort and 0.74 (95% CI: 0.59 to 0.89), 0.74 (95% CI: 0.59 to 0.90), and 0.76 (95% CI: 0.61 to 0.91), respectively, for the validation cohort. The combined model showed the best performance for the preoperative evaluation of SLN metastasis in breast cancer.

Conclusions

The model incorporating radiomics features and pharmacokinetic parameters can be conveniently used for the individualized preoperative prediction of SLN metastasis in patients with breast cancer.

3.0 T Magnetic Resonance Functional Imaging Quantitative Parameters for Differential Diagnosis of Benign and Malignant Lesions of the Breast

Objective: To investigate value of quantitative dynamic contrast-enhanced magnetic resonance imaging (MRI) parameters and apparent diffusion coefficient (ADC) value in differential diagnosis of breast benign and malignant lesions, and their correlation with prognostic factors of breast cancer. Methods: The study collected MRI images and clinical data from 232 female patients suspected of breast cancer. Philips INGENIA 3.0T superconducting magnetic resonance scanner was used for imaging examination. Complete pathological data of patients were collected, and the expression of ER, PR, HER-2, and Ki-67 were further investigated. Results: K_ep was higher in malignant breast lesion group than that in benign breast lesion group, and ADC value was lower in the former group than that in the latter group (both p < 0.05). The areas under the receiver operating characteristic curves for K_ep, ADC, and extravascular volume fraction (V_e) were 0.904 (95% confidence interval [CI]: 0.863-0.945), 0.813 (95% CI: 0.752-0.875), and 0.774 (95% CI: 0.707-0.841), respectively. Furthermore, according to the maximum Youden index, the specificity of K_ep and the sensitivity of ADC were high, which were 97.20% and 96.00%, respectively, with a cutoff value of 0.314 and 0.151, respectively. K_ep value in ER-positive expression group was significantly higher than that in ER-negative expression group (p < 0.05). K_ep value in PR-positive expression group was significantly higher than that in PR-negative expression group (p < 0.05). There was positive correlation between K_ep and expression of Ki-67 (p < 0.05). ADC value was negatively correlated with Ki-67 expression (p < 0.05). Conclusion: Quantitative parameters K_ep and ADC of 3.0 T MR functional imaging can be used as reference indexes for differential diagnosis of benign and malignant breast lesions and for biological behavior evaluation, indicating potential clinical value for noninvasive preoperative evaluation of breast cancer.

Nonmass Enhancement Breast Lesions: Diagnostic Performance of Kinetic Assessment on Ultrafast and Standard Dynamic Contrast-Enhanced MRI in Comparison With Morphologic Evaluation

OBJECTIVE. The purpose of this article was to evaluate the diagnostic performance of the kinetic parameters of ultrafast and standard dynamic contrast-enhanced MRI (DCE-MRI) compared with morphologic evaluation in differentiating benign from malignant nonmass enhancement (NME) breast lesions. MATERIALS AND METHODS. A total of 77 consecutive patients with 77 NMEs (23 benign and 54 malignant) underwent 3-T MRI, including one unenhanced and eight contrast-enhanced ultrafast DCE-MRI scans (7-second scans) and standard DCE-MRI scans. The two readers evaluated the lesions' likelihood of malignancy on a continuous scale from 0 to 100% as the morphologic score using standard DCE-MRI. The kinetic curves of ultrafast DCE-MRI were fitted using an empirical mathematical model, ΔS(t) = A × (1 - e^-αt), where A is the upper limit of signal intensity, e is the Euler number, and alpha (s^-1) is the rate of signal increase. The initial slope of the kinetic curve (A × α) and the initial AUC (AUC₃₀, which is the integration of the kinetic curve from 0 to 30 seconds) were calculated. From standard DCE-MRI, initial enhancement rate and signal enhancement ratio (SER) were calculated. These parameters were compared between benign and malignant NMEs. RESULTS. The morphologic score of malignant NME was statistically significantly higher than that of benign NME (p < 0.0001). The upper limit of signal intensity, rate of signal increase, initial slope of the kinetic curve, and AUC₃₀ of ultrafast DCE-MRI, initial enhancement rate, SER of standard DCE-MRI of malignant NMEs were statistically significantly higher than those of benign NMEs (p = 0.0011, 0.0045, < 0.0001, < 0.0001, 0.0017, and < 0.0001, respectively). AUC ROC analysis found no statistically significant difference between morphologic score, AUC₃₀ of ultrafast DCE-MRI, or SER of standard DCE-MRI. CONCLUSION. The kinetic parameters of ultrafast and standard DCE-MRI were as effective as morphologic evaluation for differentiation between benign and malignant NMEs.

Utility of synthetic MRI in predicting the Ki-67 status of oestrogen receptor-positive breast cancer: a feasibility study

AIM

To evaluate the utility of synthetic magnetic resonance imaging (MRI) of the breast in predicting the Ki-67 status in patients with oestrogen receptor (ER)-positive breast cancer.

MATERIALS AND METHODS

Forty-nine patients with 50 histopathologically proven breast cancers who underwent additional synthetic MRI were enrolled in the present study. Using synthetic MRI images, T1 and T2 relaxation times and their standard deviations (SD) in the breast lesions before (T1-Pre, T2-Pre, PD-Pre, SD of T1-Pre, SD of T2-Pre, SD of PD-Pre) and after (T1-Gd, T2-Gd, PD-Gd, SD of T1-Gd, SD of T2-Gd, SD of PD-Gd) contrast agent injection were obtained. These quantitative values were compared between the low Ki-67 expression (<14%) lesions (low-proliferation group: n=23) and high Ki-67 expression (≥14%) lesions (high-proliferation group: n=27).

RESULTS

The univariate analysis showed that the SD of T1-Gd (p<0.001) and T2-Gd (p=0.042) were significantly higher in the high-proliferation group than in the low-proliferation group. Multivariate analysis further showed that the SD of T1-Gd was a significant and independent predictor of Ki-67 expression, with an area under the receiver operating characteristic (AUROC) curve of 0.885. The sensitivity, specificity, and accuracy of the SD of T1-Gd with an optimal cut-off value of 98.5 were 77.8%, 87%, and 82%, respectively.

CONCLUSION

The SD of T1-Gd obtained from synthetic MRI was useful to predict Ki-67 status.

Model-Based High-Definition Dynamic Contrast Enhanced MRI for Concurrent Estimation of Perfusion and Microvascular Permeability

This work introduces a model-based, high-definition dynamic contrast enhanced (DCE) MRI for concurrent estimation of perfusion and microvascular permeability over the whole brain. A time series of reference-subtracted signals is decomposed into one component that reflects main contrast dynamics and the other one that includes residual contrast agents (CA) and background signals. The former is described by linear superposition of a finite number of basic vectors trained from an augmented set of data that consists of tracer-kinetic model driven signal vectors and patient-specific measured ones. Contrast dynamics is estimated by solving a constrained optimization problem that incorporates the linearized signal decomposition into the measurement model of DCE MRI and then combining the main component with the background-suppressed, residual CA signals. To the best of our knowledge, this is the first work that prospectively enables rapid temporal sampling with 1.5 s (3 ∼ 4 times higher than clinical routines) while simultaneously achieving high isotropic spatial resolution with 1.0 mm³ (4 ∼ 6 times higher than routines), enhancing estimation of both patient-specific inputs and outputs for quantification of microvascular functions. Simulations and experiments are performed to demonstrate the effectiveness of the proposed method in patients with brain cancer.

Validating an Empirical Mathematical Model for Dynamic Contrast-enhanced MR Imaging of Hand and Wrist Synovitis in Rheumatoid Arthritis: Correlation of Model Parameters with Clinical Disease Activity

Purpose:

To evaluate the feasibility of an empirical mathematical model (EMM) to fit dynamic contrast-enhanced MRI (DCE-MRI) data of hand and wrist synovitis and whether parameters of EMM are significantly correlated with clinical disease activity in patients with rheumatoid arthritis (RA).

Methods:

Thirty-one consecutive patients with RA prospectively underwent Institutional Review Board (IRB)- approved DCE-MRI scans with temporal resolution of 20 s using a 1.5T system. ROIs were placed where the highest signal increase was observed and the kinetic curves were analyzed using an EMM: ΔS(t) = A(1 − e^−αt) e^−βt, where ΔS is relative enhancement, t is time from when the signal increase was first observed, starting from baseline (ΔS = 0), A is the upper limit of signal intensity, ^α (s⁻¹) is the rate of signal increase, and β (s⁻¹) is the rate of signal decrease during washout. The initial slope of the kinetic curve (A^α), the initial area under the curve (AUC30), the time at which the kinetic curve reached its peak (T_peak) and the signal enhancement ratio (SER) defined as the change in signal intensity between the initial and delayed time points (t = 60 and 300 s, respectively) were calculated. RA magnetic resonance imaging scores (RAMRIS) with and without contrast media were evaluated. These parameters or scores were compared with the Disease Activity Score (DAS) 28-erythrocyte sedimentation rate (ESR).

Results:

A showed a significant correlation with DAS28-ESR (r = 0.58; P = 0.0005). β, AUC30 and T_peak were also significantly correlated with DAS28-ESR with a lesser degree (r = 0.49; P = 0.0051, r = 0.50; P = 0.0038 and r = −0.51; P = 0.0028, respectively), whereas α, Aα, SER and RAMRIS were not.

Conclusion:

EMM could fit the DCE-MRI data of hand and wrist synovitis. AUC30 obtained from the uptake phase of the kinetic curve as well as A, β and T_peak obtained throughout the kinetic curve might be effective to predict the clinical disease activity.

Ultrafast Dynamic Contrast-Enhanced Breast MRI: Kinetic Curve Assessment Using Empirical Mathematical Model Validated with Histological Microvessel Density

RATIONALE AND OBJECTIVES

To evaluate whether parameters from empirical mathematical model (EMM) for ultrafast dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) correlate with histological microvessel density (MVD) in invasive breast cancer.

MATERIALS AND METHODS

Ninety-eight consecutive patients with invasive breast cancer underwent an institutional review board-approved ultrafast DCE-MRI including a pre- and 18 postcontrast whole breast ultrafast scans (3 seconds) followed by four standard scans (60 seconds) using a 3T system. Region of interest was placed within each lesion where the highest signal increase was observed on ultrafast DCE-MRI, and the increase rate of enhancement was calculated as follows: ΔS = (SIpost - SIpre)/SIpre. The kinetic curve obtained from ultrafast DCE-MRI was analyzed using a truncated EMM: ΔS(t) = A(1 - e^-αt), where A is the upper limit of the signal intensity, α (min^-1) is the rate of signal increase. The initial slope of the kinetic curve is given by Aα. Initial area under curve (AUC30) and time of initial enhancement was calculated. From the standard DCE-MRI, the initial enhancement rate (IER) and the signal enhancement ratio (SER) were calculated as follows: IER = (SIearly - SIpre)/SIpre, SER = (SIearly - SIpre)/(SIdelayed - SIpre). The parameters were compared to MVD obtained from surgical specimens.

RESULTS

A, α, Aα, AUC30, and time of initial enhancement significantly correlated with MVD (r = 0.29, 0.40, 0.51, 0.43, and -0.32 with p = 0.0027, p < 0.0001, p < 0.0001, p < 0.0001, and p = 0.0012, respectively), whereas IER and SER from standard DCE-MRI did not.

CONCLUSION

The parameters of the EMM, especially the initial slope or Aα, for ultrafast DCE-MRI correlated with MVD in invasive breast cancer.

A graph-based lesion characterization and deep embedding approach for improved computer-aided diagnosis of nonmass breast MRI lesions

Nonmass-like enhancements are a common but diagnostically challenging finding in breast MRI. Nonmass-like lesions can be described as clusters of spatially and temporally inter-connected regions of enhancements, so they can be modeled as networks and their properties characterized via network-based connectivity. In this work, we represented nonmass lesions as graphs using a link formation energy model that favors linkages between regions of similar enhancement and closer spatial proximity. However, adding graph features to an existing computer-aided diagnosis (CAD) pipeline incurs an increase of feature space dimensionality, which poses additional challenges to traditional supervised machine learning techniques due to the inability to increase accordingly the number of training datasets. We propose the combination of unsupervised dimensionality reduction and embedded space clustering followed by a supervised classifier to improve the performance of a CAD system for nonmass-like lesions in breast MRI. Our work extends a previoulsy proposed framework for deep embedded unsupervised clustering (DEC) to embedding space classification, with the joint optimization of objective functions for DEC and supervised multi-layered perceptron (MLP) classification. The strength of the method lies in the ability to learn and further optimize an embedded feature representation of lower dimensionality that maximizes the diagnostic accuracy of a CAD lesion classifier to discriminate between benign and malignant lesions. We identified 792 nonmass-like enhancements (267 benign, 110 malignant and 415 unknown) in 411 patients undergoing breast MRI at our institution. The diagnostic performance of the proposed method was evaluated and compared to the performance of a conventional supervised MLP classifier in original feature space. A statistically significant increase in diagnostic area under the ROC curve (AUC) was achieved. Generalization AUC increased from 0.67 ± 0.08 to 0.81 ± 0.10 (21% increase, p-value=4.2×10^-8) with the proposed graph-based lesion characterization and deep embedding framework.

Fast Temporal Resolution Dynamic Contrast-Enhanced MRI: Histogram Analysis Versus Visual Analysis for Differentiating Benign and Malignant Breast Lesions

OBJECTIVE

The purpose of this study was to validate a kinetic assessment based on visually identified peak enhancement, which is routinely used in clinical practice, for differentiating benign from malignant lesions during fast dynamic contrast-enhanced MRI.

MATERIALS AND METHODS

Between January 2015 and December 2016, 90 consecutively registered patients with 105 breast lesions (40 benign, 65 malignant) underwent dynamic contrast-enhanced 1.5-T MRI that included one unenhanced and eight contrast-enhanced fast temporal resolution (10 seconds) whole-breast acquisitions. Histogram analysis was performed to measure the voxel-based enhancement of the entire lesion to obtain 90th, 75th, and 50th percentile values at each time point and to generate kinetic curves. Two observers selected visually identified peak enhancement within the lesions to generate the kinetic curves. The kinetic curves from histogram and visually identified peak enhancement analyses were fitted by means of an empiric mathematic model (EMM): ΔS(t) = A × (1 - e^-αt), where A is the upper limit of signal intensity, e indicates the exponential function, and α (min^-1) is the rate of increase in signal intensity. The initial slope of the kinetic curve (A × α) and the initial AUC (AUC₃₀) were calculated. These parameters were compared between benign and malignant lesions, and results from visually identified peak enhancement analysis were compared with those from histogram analysis.

RESULTS

Benign lesions were successfully differentiated from malignant lesions in both visually identified peak enhancement and histogram analyses (90th and 75th percentile values) on the basis of α, A × α, and AUC₃₀ from the EMM. There was no significant difference in ROC AUC in these EMM parameters between visually identified peak enhancement and histogram analyses (p = 0.21).

CONCLUSION

Kinetic assessment with visually identified peak enhancement was acceptable for differentiating benign from malignant lesions.

Feasibility of high spatiotemporal resolution for an abbreviated 3D radial breast MRI protocol

PURPOSE

To develop a volumetric imaging technique with 0.8-mm isotropic resolution and 10-s/volume rate to detect and analyze breast lesions in a bilateral, dynamic, contrast-enhanced MRI exam.

METHODS

A local low-rank temporal reconstruction approach that also uses parallel imaging and spatial compressed sensing was designed to create rapid volumetric frame rates during a contrast-enhanced breast exam (vastly undersampled isotropic projection [VIPR] spatial compressed sensing with temporal local low-rank [STELLR]). The dynamic-enhanced data are subtracted in k-space from static mask data to increase sparsity for the local low-rank approach to maximize temporal resolution. A T₁ -weighted 3D radial trajectory (VIPR iterative decomposition with echo asymmetry and least squares estimation [IDEAL]) was modified to meet the data acquisition requirements of the STELLR approach. Additionally, the unsubtracted enhanced data are reconstructed using compressed sensing and IDEAL to provide high-resolution fat/water separation. The feasibility of the approach and the dual reconstruction methodology is demonstrated using a 16-channel breast coil and a 3T MR scanner in 6 patients.

RESULTS

The STELLR temporal performance of subtracted data matched the expected temporal perfusion enhancement pattern in small and large vascular structures. Differential enhancement within heterogeneous lesions is demonstrated with corroboration from a basic reconstruction using a strict 10-second temporal footprint. Rapid acquisition, reliable fat suppression, and high spatiotemporal resolution are presented, despite significant data undersampling.

CONCLUSION

The STELLR reconstruction approach of 3D radial sampling with mask subtraction provides a high-performance imaging technique for characterizing enhancing structures within the breast. It is capable of maintaining temporal fidelity, while visualizing breast lesions with high detail over a large FOV to include both breasts.

Distinguishing benign and malignant breast tumors: preliminary comparison of kinetic modeling approaches using multi-institutional dynamic contrast-enhanced MRI data from the International Breast MR Consortium 6883 trial

Comparative preliminary analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data collected in the International Breast MR Consortium 6883 multicenter trial was performed to distinguish benign and malignant breast tumors. Prebiopsy DCE-MRI data from 45 patients with suspicious breast lesions were obtained. Semiquantitative mean signal-enhancement ratio ([Formula: see text]) was calculated for all lesions, and quantitative pharmacokinetic, parameters [Formula: see text], [Formula: see text], and [Formula: see text], were calculated for the subset with available [Formula: see text] maps ([Formula: see text]). Diagnostic performance was estimated for DCE-MRI parameters and compared to standard clinical MRI assessment. Quantitative and semiquantitative metrics discriminated benign and malignant lesions, with receiver operating characteristic area under the curve (AUC) values of 0.71, 0.70, and 0.82 for [Formula: see text], [Formula: see text], and [Formula: see text], respectively ([Formula: see text]). At equal 94% sensitivity, the specificity and positive predictive value of [Formula: see text] (53% and 63%, respectively) and K^trans (42% and 58%) were higher than clinical MRI assessment (32% and 54%). A multivariable model combining [Formula: see text] and clinical MRI assessment had an AUC value of 0.87. Quantitative pharmacokinetic and semiquantitative analyses of DCE-MRI improves discrimination of benign and malignant breast tumors, with our findings suggesting higher diagnostic accuracy using [Formula: see text]. [Formula: see text] has potential to help reduce unnecessary biopsies resulting from routine breast imaging.

MRI texture analysis in differentiating luminal A and luminal B breast cancer molecular subtypes - a feasibility study

Background

The aim of this study was to use texture analysis (TA) of breast magnetic resonance (MR) images to assist in differentiating estrogen receptor (ER) positive breast cancer molecular subtypes.

Methods

Twenty-seven patients with histopathologically proven invasive ductal breast cancer were selected in preliminary study. Tumors were classified into molecular subtypes: luminal A (ER-positive and/or progesterone receptor (PR)-positive, human epidermal growth factor receptor type 2 (HER2) -negative, proliferation marker Ki-67 < 20 and low grade (I)) and luminal B (ER-positive and/or PR-positive, HER2-positive or HER2-negative with high Ki-67 ≥ 20 and higher grade (II or III)). Co-occurrence matrix -based texture features were extracted from each tumor on T1-weighted non fat saturated pre- and postcontrast MR images using TA software MaZda. Texture parameters and tumour volumes were correlated with tumour prognostic factors.

Results

Textural differences were observed mainly in precontrast images. The two most discriminative texture parameters to differentiate luminal A and luminal B subtypes were sum entropy and sum variance (p = 0.003). The AUCs were 0.828 for sum entropy (p = 0.004), and 0.833 for sum variance (p = 0.003), and 0.878 for the model combining texture features sum entropy, sum variance (p = 0.001). In the LOOCV, the AUC for model combining features sum entropy and sum variance was 0.876. Sum entropy and sum variance showed positive correlation with higher Ki-67 index. Luminal B types were larger in volume and moderate correlation between larger tumour volume and higher Ki-67 index was also observed (r = 0.499, p = 0.008).

Conclusions

Texture features which measure randomness, heterogeneity or smoothness and homogeneity may either directly or indirectly reflect underlying growth patterns of breast tumours. TA and volumetric analysis may provide a way to evaluate the biologic aggressiveness of breast tumours and provide aid in decisions regarding therapeutic efficacy.

Using quantitative features extracted from T2-weighted MRI to improve breast MRI computer-aided diagnosis (CAD)

Computer-aided diagnosis (CAD) has been proposed for breast MRI as a tool to standardize evaluation, to automate time-consuming analysis, and to aid the diagnostic decision process by radiologists. T2w MRI findings are diagnostically complementary to T1w DCE-MRI findings in the breast and prior research showed that measuring the T2w intensity of a lesion relative to a tissue of reference improves diagnostic accuracy. The diagnostic value of this information in CAD has not been yet quantified. This study proposes an automatic method of assessing relative T2w lesion intensity without the need to select a reference region. We also evaluate the effect of adding this feature to other T2w and T1w image features in the predictive performance of a breast lesion classifier for differential diagnosis of benign and malignant lesions. An automated feature of relative T2w lesion intensity was developed using a quantitative regression model. The diagnostic performance of the proposed feature in addition to T2w texture was compared to the performance of a conventional breast MRI CAD system based on T1w DCE-MRI features alone. The added contribution of T2w features to more conventional T1w-based features was investigated using classification rules extracted from the lesion classifier. After institutional review board approval that waived informed consent, we identified 627 breast lesions (245 malignant, 382 benign) diagnosed after undergoing breast MRI at our institution between 2007 and 2014. An increase in diagnostic performance in terms of area under the curve (AUC) from the receiver operating characteristic (ROC) analysis was observed with the additional T2w features and the proposed quantitative feature of relative T2w lesion intensity. AUC increased from 0.80 to 0.83 and this difference was statistically significant (adjusted p-value = 0.020).

Novel High Spatiotemporal Resolution Versus Standard-of-Care Dynamic Contrast-Enhanced Breast MRI: Comparison of Image Quality

OBJECTIVE

Currently, dynamic contrast-enhanced (DCE) breast magnetic resonance imaging (MRI) prioritizes spatial resolution over temporal resolution given the limitations of acquisition techniques. The purpose of our intrapatient study was to assess the ability of a novel high spatial and high temporal resolution DCE breast MRI method to maintain image quality compared with the clinical standard-of-care (SOC) MRI.

MATERIALS AND METHODS

Thirty patients, each demonstrating a focal area of enhancement (29 benign, 1 cancer) on their SOC MRI, consented to undergo a research DCE breast MRI on a second date. For the research DCE MRI, a method (DIfferential Subsampling with Cartesian Ordering [DISCO]) using pseudorandom k-space sampling, view sharing reconstruction, 2-point Dixon fat-water separation, and parallel imaging was used to produce images with an effective temporal resolution 6 times faster than the SOC MRI (27 vs 168 seconds, respectively). Both the SOC and DISCO MRI scans were acquired with matching spatial resolutions of 0.8 × 0.8 × 1.6 mm. Image quality (distortion/artifacts, resolution, fat suppression, lesion conspicuity, perceived signal-to-noise ratio, and overall image quality) was scored by 3 radiologists in a blinded reader study.

RESULTS

Differences in image quality scores between the DISCO and SOC images were all less than 0.8 on a 10-point scale, and both methods were assessed as providing diagnostic image quality in all cases. DISCO images with the same high spatial resolution, but 6 times the effective temporal resolution as the SOC MRI scans, were produced, yielding 20 postcontrast time points with DISCO compared with 3 for the SOC MRI, over the same total time interval.

CONCLUSIONS

DISCO provided comparable image quality compared with the SOC MRI, while also providing 6 times faster effective temporal resolution and the same high spatial resolution.

Abbreviated MRI and Accelerated MRI for Screening and Diagnosis of Breast Cancer

Although published studies have revealed that magnetic resonance imaging (MRI) is by far the most effective imaging modality for cancer detection, it is currently considered cost-ineffective for screening women at an intermediate risk for breast cancer. The concept of an "abbreviated MRI" protocol has recently emerged as a possible solution for reducing the cost of MRI. The abbreviated MRI is a shortened version of the standard MRI, consisting of a single early phase dynamic contrast enhanced (DCE) series. Several clinical studies have shown that this MRI protocol would not affect sensitivity or specificity for breast MRI screening purposes. In clinical practice, morphologic evaluation and kinetic assessment are 2 major components of the interpretation process. However, kinetic assessment cannot be performed with the abbreviated protocol, because multiple sets of post-contrast images are necessary for the generation of kinetic curves. "Accelerated MRI" is a collective term for imaging techniques that acquire DCE-MR images in a very short time. Published studies suggest that the kinetic assessment during the very early post-contrast phase obtained with the accelerated MRI techniques is comparable to that with the standard MRI techniques. Applying accelerated MR techniques could potentially enhance the abbreviated MRI protocol in terms of diagnostic potential, while maintaining the shorter study time. Thus, the abbreviated MRI protocol associated with accelerated MRI techniques may provide value for screening and for diagnostic purposes.

Comparison of conventional DCE-MRI and a novel golden-angle radial multicoil compressed sensing method for the evaluation of breast lesion conspicuity

PURPOSE

To compare a novel multicoil compressed sensing technique with flexible temporal resolution, golden-angle radial sparse parallel (GRASP), to conventional fat-suppressed spoiled three-dimensional (3D) gradient-echo (volumetric interpolated breath-hold examination, VIBE) MRI in evaluating the conspicuity of benign and malignant breast lesions.

MATERIALS AND METHODS

Between March and August 2015, 121 women (24-84 years; mean, 49.7 years) with 180 biopsy-proven benign and malignant lesions were imaged consecutively at 3.0 Tesla in a dynamic contrast-enhanced (DCE) MRI exam using sagittal T1-weighted fat-suppressed 3D VIBE in this Health Insurance Portability and Accountability Act-compliant, retrospective study. Subjects underwent MRI-guided breast biopsy (mean, 13 days [1-95 days]) using GRASP DCE-MRI, a fat-suppressed radial "stack-of-stars" 3D FLASH sequence with golden-angle ordering. Three readers independently evaluated breast lesions on both sequences. Statistical analysis included mixed models with generalized estimating equations, kappa-weighted coefficients and Fisher's exact test.

RESULTS

All lesions demonstrated good conspicuity on VIBE and GRASP sequences (4.28 ± 0.81 versus 3.65 ± 1.22), with no significant difference in lesion detection (P = 0.248). VIBE had slightly higher lesion conspicuity than GRASP for all lesions, with VIBE 12.6% (0.63/5.0) more conspicuous (P < 0.001). Masses and nonmass enhancement (NME) were more conspicuous on VIBE (P < 0.001), with a larger difference for NME (14.2% versus 9.4% more conspicuous). Malignant lesions were more conspicuous than benign lesions (P < 0.001) on both sequences.

CONCLUSION

GRASP DCE-MRI, a multicoil compressed sensing technique with high spatial resolution and flexible temporal resolution, has near-comparable performance to conventional VIBE imaging for breast lesion evaluation.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 3 J. MAGN. RESON. IMAGING 2017;45:1746-1752.

Kinetic Analysis of Benign and Malignant Breast Lesions With Ultrafast Dynamic Contrast-Enhanced MRI: Comparison With Standard Kinetic Assessment

OBJECTIVE

The purposes of this study were to evaluate diagnostic parameters measured with ultrafast MRI acquisition and with standard acquisition and to compare diagnostic utility for differentiating benign from malignant lesions.

MATERIALS AND METHODS

Ultrafast acquisition is a high-temporal-resolution (7 seconds) imaging technique for obtaining 3D whole-breast images. The dynamic contrast-enhanced 3-T MRI protocol consists of an unenhanced standard and an ultrafast acquisition that includes eight contrast-enhanced ultrafast images and four standard images. Retrospective assessment was performed for 60 patients with 33 malignant and 29 benign lesions. A computer-aided detection system was used to obtain initial enhancement rate and signal enhancement ratio (SER) by means of identification of a voxel showing the highest signal intensity in the first phase of standard imaging. From the same voxel, the enhancement rate at each time point of the ultrafast acquisition and the AUC of the kinetic curve from zero to each time point of ultrafast imaging were obtained.

RESULTS

There was a statistically significant difference between benign and malignant lesions in enhancement rate and kinetic AUC for ultrafast imaging and also in initial enhancement rate and SER for standard imaging. ROC analysis showed no significant differences between enhancement rate in ultrafast imaging and SER or initial enhancement rate in standard imaging.

CONCLUSION

Ultrafast imaging is useful for discriminating benign from malignant lesions. The differential utility of ultrafast imaging is comparable to that of standard kinetic assessment in a shorter study time.

Ultrafast Bilateral DCE-MRI of the Breast with Conventional Fourier Sampling: Preliminary Evaluation of Semi-quantitative Analysis

RATIONALE AND OBJECTIVES

The study aimed to evaluate the feasibility and advantages of a combined high temporal and high spatial resolution protocol for dynamic contrast-enhanced magnetic resonance imaging of the breast.

MATERIALS AND METHODS

Twenty-three patients with enhancing lesions were imaged at 3T. The acquisition protocol consisted of a series of bilateral, fat-suppressed "ultrafast" acquisitions, with 6.9- to 9.9-second temporal resolution for the first minute following contrast injection, followed by four high spatial resolution acquisitions with 60- to 79.5-second temporal resolution. All images were acquired with standard uniform Fourier sampling. A filtering method was developed to reduce noise and detect significant enhancement in the high temporal resolution images. Time of arrival (TOA) was defined as the time at which each voxel first satisfied all the filter conditions, relative to the time of initial arterial enhancement.

RESULTS

Ultrafast images improved visualization of the vasculature feeding and draining lesions. A small percentage of the entire field of view (<6%) enhanced significantly in the 30 seconds following contrast injection. Lesion conspicuity was highest in early ultrafast images, especially in cases with marked parenchymal enhancement. Although the sample size was relatively small, the average TOA for malignant lesions was significantly shorter than the TOA for benign lesions. Significant differences were also measured in other parameters descriptive of early contrast media uptake kinetics (P < 0.05).

CONCLUSIONS

Ultrafast imaging in the first minute of dynamic contrast-enhanced magnetic resonance imaging of the breast has the potential to add valuable information on early contrast dynamics. Ultrafast imaging could allow radiologists to confidently identify lesions in the presence of marked background parenchymal enhancement.

An MRI-based classification scheme to predict passive access of 5 to 50-nm large nanoparticles to tumors

Nanoparticles are useful tools in oncology because of their capacity to passively accumulate in tumors in particular via the enhanced permeability and retention (EPR) effect. However, the importance and reliability of this effect remains controversial and quite often unpredictable. In this preclinical study, we used optical imaging to detect the accumulation of three types of fluorescent nanoparticles in eight different subcutaneous and orthotopic tumor models, and dynamic contrast-enhanced and vessel size index Magnetic Resonance Imaging (MRI) to measure the functional parameters of these tumors. The results demonstrate that the permeability and blood volume fraction determined by MRI are useful parameters for predicting the capacity of a tumor to accumulate nanoparticles. Translated to a clinical situation, this strategy could help anticipate the EPR effect of a particular tumor and thus its accessibility to nanomedicines.

Improving the Accuracy of Computer-aided Diagnosis for Breast MR Imaging by Differentiating between Mass and Nonmass Lesions

PURPOSE

To determine suitable features and optimal classifier design for a computer-aided diagnosis (CAD) system to differentiate among mass and nonmass enhancements during dynamic contrast material-enhanced magnetic resonance (MR) imaging of the breast.

MATERIALS AND METHODS

Two hundred eighty histologically proved mass lesions and 129 histologically proved nonmass lesions from MR imaging studies were retrospectively collected. The institutional research ethics board approved this study and waived informed consent. Breast Imaging Reporting and Data System classification of mass and nonmass enhancement was obtained from radiologic reports. Image data from dynamic contrast-enhanced MR imaging were extracted and analyzed by using feature selection techniques and binary, multiclass, and cascade classifiers. Performance was assessed by measuring the area under the receiver operating characteristics curve (AUC), sensitivity, and specificity. Bootstrap cross validation was used to predict the best classifier for the classification task of mass and nonmass benign and malignant breast lesions.

RESULTS

A total of 176 features were extracted. Feature relevance ranking indicated unequal importance of kinetic, texture, and morphologic features for mass and nonmass lesions. The best classifier performance was a two-stage cascade classifier (mass vs nonmass followed by malignant vs benign classification) (AUC, 0.91; 95% confidence interval (CI): 0.88, 0.94) compared with one-shot classifier (ie, all benign vs malignant classification) (AUC, 0.89; 95% CI: 0.85, 0.92). The AUC was 2% higher for cascade (median percent difference obtained by using paired bootstrapped samples) and was significant (P = .0027). Our proposed two-stage cascade classifier decreases the overall misclassification rate by 12%, with 72 of 409 missed diagnoses with cascade versus 82 of 409 missed diagnoses with one-shot classifier.

CONCLUSION

Separately optimizing feature selection and training classifiers for mass and nonmass lesions improves the accuracy of CAD for breast MR imaging. By cascading classifiers, we achieved a significant improvement in performance with respect to the use of a one-shot classifier. Our cascaded classifier may provide an advantage for screening women at high risk for breast cancer, in whom the ability to diagnose cancers at an early stage is of primary importance.

Comparison of dynamic contrast-enhanced MRI parameters of breast lesions at 1.5 and 3.0 T: a pilot study

OBJECTIVE

To compare dynamic contrast-enhanced (DCE) MRI parameters from scans of breast lesions at 1.5 and 3.0 T.

METHODS

11 patients underwent paired MRI examinations in both Philips 1.5 and 3.0 T systems (Best, Netherlands) using a standard clinical fat-suppressed, T1 weighted DCE-MRI protocol, with 70-76 s temporal resolution. Signal intensity vs time curves were fit with an empirical mathematical model to obtain semi-quantitative measures of uptake and washout rates as well as time-to-peak enhancement (TTP). Maximum percent enhancement and signal enhancement ratio (SER) were also measured for each lesion. Percent differences between parameters measured at the two field strengths were compared.

RESULTS

TTP and SER parameters measured at 1.5 and 3.0 T were similar; with mean absolute differences of 19% and 22%, respectively. Maximum percent signal enhancement was significantly higher at 3 T than at 1.5 T (p = 0.006). Qualitative assessment showed that image quality was significantly higher at 3 T (p = 0.005).

CONCLUSION

Our results suggest that TTP and SER are more robust to field strength change than other measured kinetic parameters, and therefore measurements of these parameters can be more easily standardized than measurements of other parameters derived from DCE-MRI. Semi-quantitative measures of overall kinetic curve shape showed higher reproducibility than do discrete classification of kinetic curve early and delayed phases in a majority of the cases studied.

ADVANCES IN KNOWLEDGE

Qualitative measures of curve shape are not consistent across field strength even when acquisition parameters are standardized. Quantitative measures of overall kinetic curve shape, by contrast, have higher reproducibility.

Dynamic contrast-enhanced magnetic resonance imaging: applications in oncology

Dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) allows functional characterisation of tissue perfusion characteristics and acts as a biomarker for tumour angiogenesis. It involves serial acquisition of MRI images before and after injection of contrast, as such, tissue perfusion and permeability can be assessed based on the signal enhancement kinetics. The ability to evaluate whole tumour volumes in a non-invasive manner makes DCE MRI especially attractive for potential oncological applications. Here we provide an overview of the current research involving DCE MRI as a biomarker for the diagnosis and characterisation of malignancies, prediction of the therapeutic response and survival outcomes, as well as radiation therapy planning.

Classification of breast lesions pre-contrast injection using water resonance lineshape analysis

Inhomogeneously broadened, non-Lorentzian water resonances have been observed in small image voxels of breast tissue. The non-Lorentzian components of the water resonance are probably produced by bulk magnetic susceptibility shifts caused by dense, deoxygenated tumor blood vessels (the 'blood oxygenation level-dependent' effect), but can also be produced by other characteristics of local anatomy and physiology, including calcifications and interfaces between different types of tissue. Here, we tested the hypothesis that the detection of non-Lorentzian components of the water resonance with high spectral and spatial resolution (HiSS) MRI allows the classification of breast lesions without the need to inject contrast agent. Eighteen malignant lesions and nine benign lesions were imaged with HiSS MRI at 1.5 T. A new algorithm was developed to detect non-Lorentzian (or off-peak) components of the water resonance. After a Lorentzian fit had been subtracted from the data, the largest peak in the residual spectrum in each voxel was identified as the major off-peak component of the water resonance. The difference in frequency between these off-peak components and the main water peaks, and their amplitudes, were measured in malignant lesions, benign lesions and breast fibroglandular tissue. Off-peak component frequencies were significantly different between malignant and benign lesions (p < 0.001). Receiver operating characteristic (ROC) analysis was used to assess the diagnostic performance of HiSS off-peak component analysis compared with dynamic contrast-enhanced (DCE) MRI parameters. The areas under the ROC curves for the 'DCE rapid uptake fraction', 'DCE washout fraction', 'off-peak component amplitude' and 'off-peak component frequency' were 0.75, 0.83, 0.50 and 0.86, respectively. These results suggest that water resonance lineshape analysis performs well in the classification of breast lesions without contrast injection and could improve the diagnostic accuracy of clinical breast MR examinations. In addition, this approach may provide an alternative to DCE MRI in women who are at risk for adverse reactions to contrast media.

Dynamic breast MRI: does lower temporal resolution negatively affect clinical kinetic analysis?

OBJECTIVE

The purpose of this study was to compare the differences in kinetic assessments of lesions at breast MRI performed with higher and lower temporal resolution.

MATERIALS AND METHODS

All consecutively evaluated BI-RADS category 4, 5, and 6 lesions imaged with breast MRI and pathologically confirmed from October 2005 to August 2009 were identified. Patients underwent MRI with one of two dynamic contrast-enhanced protocols: one with 90-second (October 2005-June 2006) and another with 180-second (July 2006-August 2009) temporal resolution. Studies were processed with a computer-aided evaluation system with initial and delayed contrast-enhanced time points with the k-space centered 90 and 450 seconds after contrast injection. Initial-phase peak enhancement, delayed-phase predominant curve type, and worst curve type were recorded and compared for benign and malignant lesions across protocols.

RESULTS

The analysis set comprised 993 lesions: 145 imaged with the 90-second acquisition (17 benign, 28 ductal carcinoma in situ [DCIS], 100 invasive cancer) and 848 imaged with the 180-second acquisition (212 benign, 145 DCIS, 491 invasive cancer). Peak enhancement was significantly higher for both benign lesions (p = 0.01) and invasive cancers (p = 0.0008) with the 180-second protocol. Peak enhancement of DCIS was similar in the two protocols (p = 0.88). Delayed-phase kinetics were similar for the two protocols for both benign and malignant lesions when defined by predominant or worst curve type.

CONCLUSION

Although it has lower temporal resolution, a 180-second acquisition may be preferable because it allows higher spatial resolution and captures higher initial-phase peak enhancement without loss of delayed-phase kinetic information.

Empirical mathematical model for dynamic manganese-enhanced MRI of the murine pancreas for assessment of β-cell function

Autoimmune ablation of pancreatic β-cells and alteration of its microvasculature may be a predictor of Type I diabetes development. A dynamic manganese-enhanced MRI (MEMRI) approach and an empirical mathematical model were developed to monitor whole pancreatic β-cell function and vasculature modifications in mice. Normal and streptozotocin-induced diabetic FVB/N mice were imaged on a 9.4T MRI system using a 3D magnetization prepared rapid acquisition gradient echo pulse sequence to characterize low dose manganese kinetics in the pancreas head, body and tail. Average signal enhancement in the pancreas (head, body, and tail) as a function of time was fit by a novel empirical mathematical model characterizing contrast uptake/washout rates and yielding parameters describing peak signal, initial slope, and initial area under the curve. Signal enhancement from glucose-induced manganese uptake was fit by a linear function. The results demonstrated that the diabetic pancreatic tail had a significantly lower contrast uptake rate, smaller initial slope/initial area under the curve, and a smaller rate of Mn uptake following glucose activation (p<0.05) compared to the normal pancreatic tail. These observations parallel known patterns of β-cell loss and alteration in supportive vasculature associated with diabetes. Dynamic MEMRI is a promising technique for assessing β-cell functionality and vascular perfusion with potential applications for monitoring diabetes progression and/or therapy.

Characterizing early contrast uptake of ductal carcinoma in situ with high temporal resolution dynamic contrast-enhanced MRI of the breast: a pilot study

Improvements in the reliable diagnosis of preinvasive ductal carcinoma in situ (DCIS) by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) are needed. In this study, we present a new characterization of early contrast kinetics of DCIS using high temporal resolution (HiT) DCE-MRI and compare it with other breast lesions and normal parenchyma. Forty patients with mammographic calcifications suspicious for DCIS were selected for HiT imaging using T(1)-weighted DCE-MRI with ∼7 s temporal resolution for 90 s post-contrast injection. Pixel-based and whole-lesion kinetic curves were fit to an empirical mathematical model (EMM) and several secondary kinetic parameters derived. Using the EMM parameterized and fitted concentration time curve for subsequent analysis allowed for calculation of kinetic parameters that were less susceptible to fluctuations due to noise. The parameters' initial area under the curve (iAUC) and contrast concentration at 1 min (C(1 min)) provided the highest diagnostic accuracy in the task of distinguishing pathologically proven DCIS from normal tissue. There was a trend for DCIS lesions with solid architectural pattern to exhibit a negative slope at 1 min (i.e. increased washout rate) compared to those with a cribriform pattern (p < 0.04). This pilot study demonstrates the feasibility of quantitative analysis of early contrast kinetics at high temporal resolution and points to the potential for such an analysis to improve the characterization of DCIS.

The ACR BI-RADS experience: learning from history

The Breast Imaging Reporting and Data System (BI-RADS) initiative, instituted by the ACR, was begun in the late 1980s to address a lack of standardization and uniformity in mammography practice reporting. An important component of the BI-RADS initiative is the lexicon, a dictionary of descriptors of specific imaging features. The BI-RADS lexicon has always been data driven, using descriptors that previously had been shown in the literature to be predictive of benign and malignant disease. Once established, the BI-RADS lexicon provided new opportunities for quality assurance, communication, research, and improved patient care. The history of this lexicon illustrates a series of challenges and instructive successes that provide a valuable guide for other groups that aspire to develop similar lexicons in the future.