Multiparametric Approach to Breast Cancer With Emphasis on Magnetic Resonance Imaging in the Era of Personalized Breast Cancer Treatment.

ABSTRACT: A multiparametric approach to breast cancer imaging offers the advantage of integrating the diverse contributions of various parameters. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is the most important MRI sequence for breast imaging. The vascularity and permeability of lesions can be estimated through the use of semiquantitative and quantitative parameters. The increased use of ultrafast DCE-MRI has facilitated the introduction of novel kinetic parameters. In addition to DCE-MRI, diffusion-weighted imaging provides information associated with tumor cell density, with advanced diffusion-weighted imaging techniques such as intravoxel incoherent motion, diffusion kurtosis imaging, and time-dependent diffusion MRI opening up new horizons in microscale tissue evaluation. Furthermore, T2-weighted imaging plays a key role in measuring the degree of tumor aggressiveness, which may be related to the tumor microenvironment. Magnetic resonance imaging is, however, not the only imaging modality providing semiquantitative and quantitative parameters from breast tumors. Breast positron emission tomography demonstrates superior spatial resolution to whole-body positron emission tomography and allows comparable delineation of breast cancer to MRI, as well as providing metabolic information, which often precedes vascular and morphological changes occurring in response to treatment. The integration of these imaging-derived factors is accomplished through multiparametric imaging. In this article, we explore the relationship among the key imaging parameters, breast cancer diagnosis, and histological characteristics, providing a technical and theoretical background for these parameters. Furthermore, we review the recent studies on the application of multiparametric imaging to breast cancer and the significance of the key imaging parameters.

Ultrafast Dynamic Contrast-Enhanced MRI of the Breast: From Theory to Practice.

The development of ultrafast dynamic contrast-enhanced (UF-DCE) MRI has occurred in tandem with fast MRI scan techniques, particularly view-sharing and compressed sensing. Understanding the strengths of each technique and optimizing the relevant parameters are essential to their implementation. UF-DCE MRI has now shifted from research protocols to becoming a part of clinical scan protocols for breast cancer. UF-DCE MRI is expected to compensate for the low specificity of abbreviated MRI by adding kinetic information from the upslope of the time-intensity curve. Because kinetic information from UF-DCE MRI is obtained from the shape and timing of the initial upslope, various new kinetic parameters have been proposed. These parameters may be associated with receptor status or prognostic markers for breast cancer. In addition to the diagnosis of malignant lesions, more emphasis has been placed on predicting and evaluating treatment response because hyper-vascularity is linked to the aggressiveness of breast cancers. In clinical practice, it is important to note that breast lesion images obtained from UF-DCE MRI are slightly different from those obtained by conventional DCE MRI in terms of morphology. A major benefit of using UF-DCE MRI is avoidance of the marked or moderate background parenchymal enhancement (BPE) that can obscure the target enhancing lesions. BPE is less prominent in the earlier phases of UF-DCE MRI, which offers better lesion-to-noise contrast. The excellent contrast of early-enhancing vessels provides a key to understanding the detailed pathological structure of tumor-associated vessels. UF-DCE MRI is normally accompanied by a large volume of image data for which automated/artificial intelligence-based processing is expected to be useful. In this review, both the theoretical and practical aspects of UF-DCE MRI are summarized. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 2.

A multiparametric approach to predict triple-negative breast cancer including parameters derived from ultrafast dynamic contrast-enhanced MRI.

OBJECTIVE: Triple-negative breast cancer (TNBC) is a highly proliferative breast cancer subtype. We aimed to identify TNBC among invasive cancers presenting as masses using maximum slope (MS) and time to enhancement (TTE) measured on ultrafast (UF) DCE-MRI, ADC measured on DWI, and rim enhancement on UF DCE-MRI and early-phase DCE-MRI. METHODS: This retrospective single-center study, between December 2015 and May 2020, included patients with breast cancer presenting as masses. Early-phase DCE-MRI was performed immediately after UF DCE-MRI. Interrater agreements were evaluated using the intraclass correlation coefficient (ICC) and Cohen's kappa. Univariate and multivariate logistic regression analyses of the MRI parameters, lesion size, and patient age were performed to predict TNBC and create a prediction model. The programmed death-ligand 1 (PD-L1) expression statuses of the patients with TNBCs were also evaluated. RESULTS: In total, 187 women (mean age, 58 years ± 12.9 [standard deviation]) with 191 lesions (33 TNBCs) were evaluated. The ICC for MS, TTE, ADC, and lesion size were 0.95, 0.97, 0.83, and 0.99, respectively. The kappa values of rim enhancements on UF and early-phase DCE-MRI were 0.88 and 0.84, respectively. MS on UF DCE-MRI and rim enhancement on early-phase DCE-MRI remained significant parameters after multivariate analyses. The prediction model created using these significant parameters yielded an area under the curve of 0.74 (95% CI, 0.65, 0.84). The PD-L1-expressing TNBCs tended to have higher rim enhancement rates than the non-PD-L1-expressing TNBCs. CONCLUSION: A multiparametric model using UF and early-phase DCE-MRI parameters may be a potential imaging biomarker to identify TNBCs. CLINICAL RELEVANCE STATEMENT: Prediction of TNBC or non-TNBC at an early point of diagnosis is crucial for appropriate management. This study offers the potential of UF and early-phase DCE-MRI to offer a solution to this clinical issue. KEY POINTS: • It is crucial to predict TNBC at an early clinical period. • Parameters on UF DCE-MRI and early-phase conventional DCE-MRI help in predicting TNBC. • Prediction of TNBC by MRI may be useful in determining appropriate clinical management.

Diffusion kurtosis imaging as a biomarker of breast cancer.

Diffusion kurtosis imaging (DKI) is a diffusion-weighted imaging method that describes non-Gaussian signal behavior using a relatively simple mathematical model. A parameter, kurtosis K, describes the deviation of the diffusion signal decay from a Gaussian pattern. The deviation reflects the complexity of the tissue microstructure affecting water diffusion. Several studies have investigated the diagnostic performance of DKI in distinguishing malignant from benign breast lesions. DKI has been reported to correlate with subtypes and with several molecular and other factors related to the treatment and prognosis of breast cancer. Some technical considerations remain to be resolved for the clinical application of DKI in the breast. Advances in knowledge: DKI, which increases the sensitivity to complex tissue microstructure compared to standard DWI, has been applied in the breast, allowing to increase clinical performance in distinguishing malignant from benign lesions and in predicting prognosis or treatment response in breast cancer.

Comparison of Ultrafast Dynamic Contrast-Enhanced (DCE) MRI with Conventional DCE MRI in the Morphological Assessment of Malignant Breast Lesions.

Ultrafast (UF) dynamic contrast-enhanced (DCE)-MRI offers the potential for a faster and, therefore, less expensive examination of breast lesions; however, there are no reports that have evaluated whether UF DCE-MRI can be used the same as conventional DCE-MRI in the reading of morphological information. This study evaluated the agreement in morphological information obtained from malignant breast mass lesions between UF DCE-MRI and conventional DCE-MRI. UF DCE-MRI data were obtained over the first 60 s post-contrast injection, followed by the conventional DCE images. Two readers evaluated the size and morphology of the lesions in the final phase of the UF DCE-MRI and the early phase of the conventional DCE-MRI. Inter-method agreement in morphological information was evaluated for the two readers using the intraclass correlation coefficient for size, and the kappa statistics for the morphological descriptors. Differences in the proportion of each descriptor were examined using Fisher's test of independence. Most inter-method agreements were higher than substantial. UF DCE-MRI showed a circumscribed margin and homogeneous enhancement more often than conventional imaging. However, the percentages of readings showing the same morphology assessment between the UF DCE-MRI and conventional DCE-MRI were 71.2% (136/191) for Reader 1 and 69.1% (132/191) for Reader 2. We conclude that UF DCE-MRI may replace conventional DCE-MRI to evaluate the morphological information of malignant breast mass lesions.

Evaluation of breast lesions based on modified BI-RADS using high-resolution readout-segmented diffusion-weighted echo-planar imaging and T2/T1-weighted image.

PURPOSE: To evaluate the diagnostic performance of a non-contrast magnetic resonance imaging (MRI) protocol combining high-resolution diffusion-weighted images (HR-DWI) using readout-segmented echo planar imaging, T1-weighted imaging (T1WI), and T2-weighted imaging (T2WI), using our modified Breast Imaging-Reporting and Data System (modified BI-RADS). METHODS: Two experienced radiologists, blinded to the final pathological diagnosis, categorized a total of 108 breast lesions (61 malignant and 47 benign) acquired with the above protocol using the modified BI-RADS with a diagnostic decision tree. The decision tree included subcategories of category 4, as in mammography (categories 2, 3, 4A, 4B, 4C, and 5). These results were compared with the pathological diagnoses. RESULTS: The area under the ROC curve (AUC) was 0.89 (95% confidence interval [CI]: 0.83-0.95) for reader 1, and 0.89 (95% CI: 0.82-0.96) for reader 2. When categories 4C and above were classified as malignant, the sensitivity, specificity, and accuracy were 73.8%, 93.6%, and 82.4%, for reader 1; and 82.0%, 89.4%, and 85.2% for reader 2, respectively. CONCLUSION: Our results suggest that using HR-DWI, T1WI/T2WI analyzed with a modified BI-RADS and a decision tree showed promising diagnostic performance in breast lesions, and is worthy of further study.

Biomarkers Predictive of Distant Disease-free Survival Derived from Diffusion-weighted Imaging of Breast Cancer.

PURPOSE: To investigate whether intravoxel incoherent motion (IVIM) and/or non-Gaussian diffusion parameters are associated with distant disease-free survival (DDFS) in patients with invasive breast cancer. METHODS: From May 2013 to March 2015, 101 patients (mean age 60.0, range 28-88) with invasive breast cancer were evaluated prospectively. IVIM parameters (flowing blood volume fraction [fIVIM] and pseudodiffusion coefficient [D*]) and non-Gaussian diffusion parameters (theoretical apparent diffusion coefficient [ADC] at a b value of 0 s/mm2 [ADC0] and kurtosis [K]) were estimated using a diffusion-weighted imaging series of 16 b values up to 2500 s/mm2. Shifted ADC values (sADC200-1500) and standard ADC values (ADC0-800) were also calculated. The Kaplan-Meier method was used to generate survival analyses for DDFS, which were compared using the log-rank test. Univariable Cox proportional hazards models were used to assess any associations between each parameter and distant metastasis-free survival. RESULTS: The median observation period was 80 months (range, 35-92 months). Among the 101 patients, 12 (11.9%) developed distant metastasis, with a median time to metastasis of 79 months (range, 10-92 months). Kaplan-Meier analysis showed that DDFS was significantly shorter in patients with K > 0.98 than in those with K ≤ 0.98 (P = 0.04). Cox regression analysis showed a marginal statistical association between K and distant metastasis-free survival (P = 0.05). CONCLUSION: Non-Gaussian diffusion may be associated with prognosis in invasive breast cancer. A higher K may be a marker to help identify patients at an elevated risk of distant metastasis, which could guide subsequent treatment.

Evaluation of pathological complete response after neoadjuvant systemic treatment of invasive breast cancer using diffusion-weighted imaging compared with dynamic contrast-enhanced based kinetic analysis.

PURPOSE: This study compared the performance of diffusion-weighted imaging (DWI) to dynamic contrast-enhanced (DCE)-MRI for diagnosing pathological complete response (pCR) before surgery. METHOD: Overall, 133 lesions from 133 patients who underwent pre-surgical MRI evaluation after neoadjuvant systemic treatment were included. Two readers blinded to the pathological diagnosis evaluated the images. MR images were obtained using a routine protocol sequence that included DWI and DCE-MRI. DWI of the target lesion was scored using a three-point scale. Kinetic patterns of lesions on DCE-MRI were scored using a four-point scale. The capacities of DWI and kinetic parameters for discriminating pCR and non-pCR were assessed via receiver operating characteristic (ROC) analysis. RESULTS: For DWI scores, ROC analysis showed areas under the ROC curve (AUCs) of 0.84 (95% confidence interval: 0.77-0.90) and 0.85 (0.77-0.90) for readers 1 and 2, respectively; corresponding AUCs of kinetic scores were 0.89 (0.82-0.94) and 0.88 (0.81-0.93). Among the triple-negative subtype, the AUCs of DWI scores were 0.84 (0.70-0.93) and 0.88 (0.75-0.96) for readers 1 and 2, respectively; corresponding AUCs of kinetic scores were 0.94 (0.83-0.99) and 0.93 (0.82-0.99). Among the luminal subtype, the AUCs of DWI scores were 0.85 (0.71-0.94) and 0.82 (0.68-0.92) for readers 1 and 2, respectively; corresponding AUCs of kinetic scores were 0.82 (0.68-0.92) and 0.72 (0.56-0.85). CONCLUSIONS: Our DWI-based visual score and kinetic score showed similar diagnostic performances. Both DWI and kinetic scores tended to perform better in predicting pCR for the triple-negative subtype.

Visual Evaluation of Ultrafast MRI in the Assessment of Residual Breast Cancer after Neoadjuvant Systemic Therapy: A Preliminary Study Association with Subtype.

The purpose of this study was to investigate the diagnostic performance of ultrafast DCE (UF-DCE) MRI after the completion of neoadjuvant systemic therapy (NST) in breast cancer. In this study, MR examinations of 55 post-NST breast cancers were retrospectively analyzed. Residual tumor sizes were measured in the 20th phase of UF-DCE MRI, early and delayed phases of conventional DCE MRI, and high spatial-resolution CE MRI (UF, early, delayed, and HR, respectively). The diagnostic performance for the detection of residual invasive cancer was calculated by ROC analysis. The size difference between MRI and pathological findings was analyzed using the Wilcoxon signed-rank test with the Bonferroni correction. The overall AUC was highest for UF (0.86 and 0.88 for readers 1 and 2, respectively). The difference in imaging and pathological sizes for UF (5.7 ± 8.2 mm) was significantly smaller than those for early, delayed, and HR (p < 0.01). For luminal subtype breast cancer, the size difference was significantly smaller for UF and early than for delayed (p < 0.01). UF-DCE MRI demonstrated higher AUC and specificity for the more accurate detection of residual cancer and the visualization of tumor extent than conventional DCE MRI.

Ultrafast Dynamic Contrast-enhanced MRI of the Breast: How Is It Used?

Ultrafast dynamic contrast-enhanced (UF-DCE) MRI is a new approach to capture kinetic information in the very early post-contrast period with high temporal resolution while keeping reasonable spatial resolution. The detailed timing and shape of the upslope in the time-intensity curve are analyzed. New kinetic parameters obtained from UF-DCE MRI are useful in differentiating malignant from benign lesions and in evaluating prognostic markers of the breast cancers. Clinically, UF-DCE MRI contributes in identifying hypervascular lesions when the background parenchymal enhancement (BPE) is marked on conventional dynamic MRI. This review starts with the technical aspect of accelerated acquisition. Practical aspects of UF-DCE MRI include identification of target hypervascular lesions from marked BPE and diagnosis of malignant and benign lesions based on new kinetic parameters derived from UF-DCE MRI: maximum slope (MS), time to enhance (TTE), bolus arrival time (BAT), time interval between arterial and venous visualization (AVI), and empirical mathematical model (EMM). The parameters derived from UF-DCE MRI are compared in terms of their diagnostic performance and association with prognostic markers. Pitfalls of UF-DCE MRI in the clinical situation are also covered. Since UF-DCE MRI is an evolving technique, future prospects of UF-DCE MRI are discussed in detail by citing recent evidence. The topic covers prediction of treatment response, multiparametric approach using DWI-derived parameters, evaluation of tumor-related vessels, and application of artificial intelligence for UF-DCE MRI. Along with comprehensive literature review, illustrative clinical cases are used to understand the value of UF-DCE MRI.

Kinetic information from dynamic contrast-enhanced MRI enables prediction of residual cancer burden and prognosis in triple-negative breast cancer: a retrospective study.

This study aimed to evaluate the predictions of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for prognosis of triple-negative breast cancer (TNBC), especially with residual disease (RD) after preoperative chemotherapy. This retrospective analysis included 74 TNBC patients who received preoperative chemotherapy. DCE-MRI findings from three timepoints were examined: at diagnosis (MRIpre), at midpoint (MRImid) and after chemotherapy (MRIpost). These findings included cancer lesion size, washout index (WI) as a kinetic parameter using the difference in signal intensity between early and delayed phases, and time-signal intensity curve types. Distant disease-free survival was analysed using the log-rank test to compare RD group with and without a fast-washout curve. The diagnostic performance of DCE-MRI findings, including positive predictive value (PPV) for pathological responses, was also calculated. RD without fast washout curve was a significantly better prognostic factor, both at MRImid and MRIpost (hazard ratio = 0.092, 0.098, p < 0.05). PPV for pathological complete remission at MRImid was 76.7% by the cut-off point at negative WI value or lesion size = 0, and 66.7% at lesion size = 0. WI and curve types derived from DCE-MRI at the midpoint of preoperative chemotherapy can help not only assess tumour response but also predict prognosis.

The Rate of Apparent Diffusion Coefficient Change With Diffusion Time on Breast Diffusion-Weighted Imaging Depends on Breast Tumor Types and Molecular Prognostic Biomarker Expression.

INTRODUCTION: The aim of this study was to investigate the variation of apparent diffusion coefficient (ADC) values with diffusion time according to breast tumor type and prognostic biomarkers expression. MATERIALS AND METHODS: A total of 201 patients with known or suspected breast tumors were prospectively enrolled in this study, and 132 breast tumors (86 malignant and 46 benign) were analyzed. Diffusion-weighted imaging scans with 2 diffusion times were acquired on a clinical 3-T magnetic resonance imaging scanner using oscillating and pulsed diffusion-encoding gradients (effective diffusion times, 4.7 and 96.6 milliseconds) and b values of 0 and 700 s/mm2. Diagnostic performances to differentiate malignant and benign breast tumors for ADC values at short and long diffusion times (ADCshort and ADClong), ΔADC (the rate of change in ADC values with diffusion time), ADC0-1000 (ADC value from a standard protocol), and standard reading including dynamic contrast-enhanced magnetic resonance imaging (BI-RADS) were investigated. The correlations of ADCshort, ADClong, and ΔADC values with hormone receptor expression and breast cancer subtypes were also analyzed. RESULTS: The ADC values were lower, and ΔADC was higher in malignant tumors compared with benign tumors. The specificity of ADC values at all diffusion times and ΔADC values for differentiating malignant and benign breast tumors was superior to that of BI-RADS (87.0%-95.7% vs 73.9%), whereas the sensitivity was inferior (87.2%-90.7% vs 100%). Lower ADCshort and ADC0-1000 in ER-positive compared with ER-negative cancers (false discovery rate [FDR]-adjusted P = 0.037 and 0.018, respectively) and lower ADCshort, ADClong, and ADC0-1000 in progesterone receptor-positive compared with progesterone receptor-negative cancers (FDR-adjusted P = 0.037, 0.036, and 0.018, respectively) were found. Ki-67-positive cancers had larger ΔADCs than Ki-67-negative cancers (FDR-adjusted P = 0.018). CONCLUSIONS: The ADC values vary with different diffusion time and vary in correlation with molecular biomarkers, especially Ki-67. Those results suggest that the diffusion time, which should be reported, might be a useful parameter to consider for breast cancer management.

Usefulness of model-based iterative reconstruction in low-dose lumbar spine CT after spine surgery with metal implant: a phantom study.

BACKGROUND: The major problems of computed tomography (CT) imaging include radiation exposure and severe artifacts caused by operative implants. PURPOSE: To evaluate the usefulness of the metal artifact reduction algorithm and model-based iterative reconstruction (MBIR) in postoperative low-dose (LD) spine CT. MATERIAL AND METHODS: A CT torso phantom was scanned at standard-dose (SD) and LD settings. The CT images were reconstructed by three methods: hybrid iterative reconstruction (HIR); metal artifact reduction; and MBIR. The radiation dose of the phantom imaging was evaluated by volume CT dose index (mGy), dose length product (DLP, mGy × cm), and effective dose (mSv). The image quality of the six images was visually evaluated and analyzed using Scheffe's paired comparison method. The average preference of each method was calculated based on the comparative scores. The task transfer function (TTF) and noise power spectrum for HIR and MBIR were also measured. RESULTS: The respective radiation-dose-related parameters of the SD and LD conditions were: volume CT dose index = 10.2 and 1.2 mGy; DLP = 277.9 and 33.9 mGy × cm; and effective dose = 4.2 and 0.5 mSv. The average preference for diagnostic acceptability of MBIR at LD was not significantly different from the other reconstructions of SD data. MBIR successfully reduced metal artifacts in the LD condition. The 10% TTF was higher for HIR at SD and higher for MBIR at LD. CONCLUSION: MBIR is useful for LD spine CT after spine surgery with metal implant.

Subcategory classifications of Breast Imaging and Data System (BI-RADS) category 4 lesions on MRI.

PURPOSE: Category 4 in BI-RADS for magnetic resonance imaging (MRI) has a wide range of probabilities of malignancy, extending from > 2 to < 95%. We classified category 4 lesions into three subcategories and analyzed the positive predictive value (PPV) of malignancy in a tertiary hospital. MATERIALS AND METHODS: This retrospective study included 346 breast MRIs with 434 category 2-5 lesions. All enhancing lesions were classified as category 2 (0% probability of malignancy), 3 (> 0%, ≤ 2%), 4 (> 2%, < 95%) and 5 (≥ 95%); category 4 lesions were further subcategorized into 4A (> 2%, ≤ 10%), 4B (> 10%, ≤ 50%) and 4C (> 50%, < 95%) at the time of diagnosis. Radiological and pathological reports were retrospectively analyzed, and the PPVs were calculated. RESULTS: We included 149 malignant and 285 benign lesions. The PPVs of subcategories 4A, 4B and 4C were 1.8%, 11.8% and 67.5%, respectively. The PPVs were higher for lesions coexisting with category 5 or 6 lesions compared with those for isolated lesions. CONCLUSION: Category 4 lesions can be classified into three subcategories depending on the likelihood of malignancy. Lesions coexisting with category 5 or 6 lesions are more likely to be malignant.