Comparison of the diagnostic performances of diffusion parameters in diffusion weighted imaging and diffusion tensor imaging of breast lesions

A review of quantitative diffusion-weighted MR imaging for breast cancer: Towards noninvasive biomarker

Quantitative diffusion-weighted imaging (DWI) is an important adjunct to conventional breast MRI and shows promise as a noninvasive biomarker of breast cancer in multiple clinical scenarios, from the discrimination of benign and malignant lesions, prediction, and evaluation of treatment response to a prognostic assessment of breast cancer. Various quantitative parameters are derived from different DWI models based on special prior knowledge and assumptions, have different meanings, and are easy to confuse. In this review, we describe the quantitative parameters derived from conventional and advanced DWI models commonly used in breast cancer and summarize the promising clinical applications of these quantitative parameters. Although promising, it is still challenging for these quantitative parameters to become clinically useful noninvasive biomarkers in breast cancer, as multiple factors may result in variations in quantitative parameter measurements. Finally, we briefly describe some considerations regarding the factors that cause variations.

Clinical utility of abbreviated breast MRI based on diffusion tensor imaging in patients underwent breast conservative therapy

PURPOSE

To evaluate the added value of the diffusion tensor imaging (DTI) parameters to abbreviated breast MRI protocol in differentiating recurrent breast cancer from post-operative changes in cases of breast conservative surgery (BCS).

METHODS

This prospective study was approved by our institutional review board. Written informed consent was obtained in all patients. 47 female patients (mean age, 49 years; range, 32-66 years) that previously underwent breast conservative surgery with a palpable mass were included in this study (62 breast lesions). Two abbreviated MRI protocols were compared using 1.5 Tesla MRI, AB-MRI 1 (axial T1, T2, pre-contrast T1, 1st post-contrast and subtracted images) and AB-MRI 2 (same sequences plus adding DTI). In both protocols, the wash-in rate was calculated. Histopathology was used as the standard of reference. Appropriate statistical tests were used to assess sensitivity, specificity, and diagnostic accuracy for each protocol.

RESULTS

The mean total acquisition time was of 6 min for AB-MRI 1 and 10 min for AB-MRI 2 protocols while the mean interpretation time was of 57.5 and 75 s, respectively. Among analyzed DTI parameters, MD (mean diffusivity) showed the highest sensitivity (96.43%) and specificity (91.18%) (P value =  < 0.001). FA (fractional anisotropy), AD (axial diffusivity) and RD (radial diffusivity) showed sensitivity = (78.57%, 82.14% and 85.71%), specificity = (88.24, 85.29% and 79.41%), respectively, P value (< 0.001).

CONCLUSION

DTI may be included in abbreviated MRI protocols without a significant increase in acquisition time and with the advantage of increasing specificity and clinical utility in the characterization of post-conservative breast lesions.

Prediction of local breast cancer recurrence after surgery: the added value of diffusion tensor imaging

Background

There is considerable overlap between benign postoperative changes and recurrent breast cancer imaging features in patients surgically treated for breast cancer. This study aims to evaluate the value of adding multiple diffusion tensor imaging (DTI) parameters, including mean diffusivity (MD), fractional anisotropy (FA), radial diffusivity (RD), axial diffusivity, (AD), and relative anisotropy (RA) in differentiating breast cancer recurrence from postoperative changes in patients who were surgically treated for breast cancer and to also evaluate the role of these parameters in characterizing the different pathologies seen in the postoperative breast.

Results

This is a prospective study that was performed on female patients who were surgically treated for breast cancer. The study was done on 60 cases having 77 breast lesions. (Sixty-two of them were described as mass lesions and 15 of them were described as non-mass enhancement on MRI.) Among analyzed DTI parameters, MD showed the highest sensitivity (97.1%), specificity (88.1%), and accuracy (92.2%) in predicting recurrent breast cancer. FA, AD, and RD showed sensitivity (77.1%, 85.7%, and 88.6%) and specificity (83.3%, 83.3%, and 73.8%) in predicting recurrent breast cancer, respectively. The median MD values were lower in grade III recurrent breast cancers when compared to its values in recurrent grade II breast cancers and recurrent DCIS (0.6 × 10–3 mm2/s vs. 0.8 × 10–3 mm2/s and 0.9 × 10–3 mm2/s), respectively. FA also showed median values in grade III recurrent breast cancer higher than its values in grade II recurrent breast cancer and recurrent DCIS (0.6 vs. 0.5 and 0.39), respectively. The sensitivity, specificity, PPV, NPV, accuracy, F1 score, and MCC of DCE-MRI alone versus DCE-MRI plus combined DTI parameters were 88.6% versus 100%, 88.1% versus 90.5%, 86.1% versus 89.7%, 90.2% versus 100%, 88.3% versus 94.6%, 87.3% versus 94.6%, and 76.5% versus 90.1%, respectively.

Conclusions

DTI may play an important role as a complementary method to discriminate recurrent breast cancer from postoperative changes in patients surgically treated for previous breast cancer.

Safely reducing unnecessary benign breast biopsies by applying non-mass and DWI directional variance filters to ADC thresholding

Background

Thresholding apparent diffusion coefficient (ADC) maps obtained from Diffusion-Weighted-Imaging (DWI) has been proposed for identifying benign lesions that can safely avoid biopsy. The presence of malignancies with high ADC values leads to high thresholds, limiting numbers of avoidable biopsies.

Purpose

We evaluate two previously reported methods for identifying avoidable biopsies: using case-set dependent ADC thresholds that assure 100% sensitivity and using negative likelihood ratio (LR-) with a fixed ADC threshold of 1.50 × 10^–3 mm²/s. We evaluated improvements in efficacy obtained by excluding non-mass lesions and lesions with anisotropic intra-lesion morphologic characteristics.

Study type

Prospective.

Population

55 adult females with dense breasts with 69 BI-RADS 4 or 5 lesions (38 malignant, 31 benign) identified on ultrasound and mammography and imaged with MRI prior to biopsy.

Field strength/sequence

1.5 T and 3.0 T. DWI.

Assessment

Analysis of DWI, including directional images was done on an ROI basis. ROIs were drawn on DWI images acquired prior to biopsy, referencing all available images including DCE, and mean ADC was measured. Anisotropy was quantified via variation in ADC values in the lesion core across directional DWI images.

Statistical tests

Improvement in specificity at 100% sensitivity was evaluated with exact McNemar test with 1-sided p-value < 0.05 indicating statistical significance.

Results

Using ADC thresholding that assures 100% sensitivity, non-mass and directional variance filtering improved the percent of avoidable biopsies to 42% from baseline of 10% achieved with ADC thresholding alone. Using LR-, filtering improved outcome to 0.06 from baseline 0.25 with ADC thresholding alone. ADC thresholding showed a lower percentage of avoidable biopsies in our cohort than reported in prior studies. When ADC thresholding was supplemented with filtering, the percentage of avoidable biopsies exceeded those of prior studies.

Data conclusion

Supplementing ADC thresholding with filters excluding non-mass lesions and lesions with anisotropic characteristics on DWI can result in an increased number of avoidable biopsies.

Clinical experience of tensor-valued diffusion encoding for microstructure imaging by diffusional variance decomposition in patients with breast cancer

BACKGROUND

Diffusion-weighted imaging plays a key role in magnetic resonance imaging (MRI) of breast tumors. However, it remains unclear how to interpret single diffusion encoding with respect to its link with tissue microstructure. The purpose of this retrospective cross-sectional study was to use tensor-valued diffusion encoding to investigate the underlying microstructure of invasive ductal carcinoma (IDC) and evaluate its potential value in a clinical setting.

METHODS

We retrospectively reviewed biopsy-proven breast cancer patients who underwent preoperative breast MRI examination from July 2020 to March 2021. We reviewed the MRI of 29 patients with 30 IDCs, including analysis by diffusional variance decomposition enabled by tensor-valued diffusion encoding. The diffusion parameters of mean diffusivity (MD), total mean kurtosis (MKT), anisotropic mean kurtosis (MKA), isotropic mean kurtosis (MKI), macroscopic fractional anisotropy (FA), and microscopic fractional anisotropy (µFA) were estimated. The parameter differences were compared between IDC and normal fibroglandular breast tissue (FGBT), as well as the association between the diffusion parameters and histopathologic items.

RESULTS

The mean value of MD in IDCs was significantly lower than that of normal FGBT (1.07±0.27 vs. 1.34±0.29, P<0.001); however, MKT, MKA, MKI, FA, and µFA were significantly higher (P<0.005). Among all the diffusion parameters, MKI was positively correlated with the tumor size on both MRI and pathological specimen (rs=0.38, P<0.05 vs. rs=0.54, P<0.01), whereas MKT had a positive correlation with the tumor size in the pathological specimen only (rs=0.47, P<0.02). In addition, the lymph node (LN) metastasis group had significantly higher MKT, MKA, and µFA compared to the metastasis negative group (P<0.05).

CONCLUSIONS

Tensor-valued diffusion encoding enables a useful non-invasive method for characterizing breast cancers with information on tissue microstructures. Particularly, µFA could be a potential imaging biomarker for evaluating breast cancers prior to surgery or chemotherapy.

Diffusion tensor imaging on 3-T MRI breast: diagnostic performance in comparison to diffusion-weighted imaging

Background

Breast cancer is the most prevalent cancer among females. Dynamic contrast-enhanced MRI (DCE-MRI) breast is highly sensitive (90%) in the detection of breast cancer. Despite its high sensitivity in detecting breast cancer, its specificity (72%) is moderate. Owing to 3-T breast MRI which has the advantage of a higher signal to noise ratio and shorter scanning time rather than the 1.5-T MRI, the adding of new techniques as diffusion tensor imaging (DTI) to breast MRI became more feasible.

Diffusion-weighted imaging (DWI) which tracks the diffusion of the tissue water molecule as well as providing data about the integrity of the cell membrane has been used as a valuable additional tool of DCE-MRI to increase its specificity.

Based on DWI, more details about the microstructure could be detected using diffusion tensor imaging. The DTI applies diffusion in many directions so apparent diffusion coefficient (ADC) will vary according to the measured direction raising its sensitivity to microstructure elements and cellular density. This study aimed to investigate the diagnostic accuracy of DTI in the assessment of breast lesions in comparison to DWI.

Results

By analyzing the data of the 50 cases (31 malignant cases and 19 benign cases), the sensitivity and specificity of DWI in differentiation between benign and malignant lesions were about 90% and 63% respectively with PPV 90% and NPV 62%, while the DTI showed lower sensitivity and specificity about 81% and 51.7%, respectively, with PPV 78.9% and NPV 54.8% (P-value ≤ 0.05).

Conclusion

While the DWI is still the most established diffusion parameter, DTI may be helpful in the further characterization of tumor microstructure and differentiation between benign and malignant breast lesions.

Comparison of In Vivo and Ex Vivo Magnetic Resonance Imaging in a Rat Model for Glioblastoma-Associated Epilepsy

Magnetic resonance imaging (MRI) is frequently used for preclinical treatment monitoring in glioblastoma (GB). Discriminating between tumors and tumor-associated changes is challenging on in vivo MRI. In this study, we compared in vivo MRI scans with ex vivo MRI and histology to estimate more precisely the abnormal mass on in vivo MRI. Epileptic seizures are a common symptom in GB. Therefore, we used a recently developed GB-associated epilepsy model from our group with the aim of further characterizing the model and making it useful for dedicated epilepsy research. Ten days after GB inoculation in rat entorhinal cortices, in vivo MRI (T2w and mean diffusivity (MD)), ex vivo MRI (T2w) and histology were performed, and tumor volumes were determined on the different modalities. The estimated abnormal mass on ex vivo T2w images was significantly smaller compared to in vivo T2w images, but was more comparable to histological tumor volumes, and might be used to estimate end-stage tumor volumes. In vivo MD images displayed tumors as an outer rim of hyperintense signal with a core of hypointense signal, probably reflecting peritumoral edema and tumor mass, respectively, and might be used in the future to distinguish the tumor mass from peritumoral edema—associated with reactive astrocytes and activated microglia, as indicated by an increased expression of immunohistochemical markers—in preclinical models. In conclusion, this study shows that combining imaging techniques using different structural scales can improve our understanding of the pathophysiology in GB.

Multidimensional Diffusion Magnetic Resonance Imaging for Characterization of Tissue Microstructure in Breast Cancer Patients: A Prospective Pilot Study

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

Diagnostic performance of breast tumor tissue selection in diffusion weighted imaging: A systematic review and meta-analysis

BACKGROUND

Several methods for tumor delineation are used in literature on breast diffusion weighted imaging (DWI) to measure the apparent diffusion coefficient (ADC). However, in the process of reaching consensus on breast DWI scanning protocol, image analysis and interpretation, still no standardized optimal breast tumor tissue selection (BTTS) method exists. Therefore, the purpose of this study is to assess the impact of BTTS methods on ADC in the discrimination of benign from malignant breast lesions in DWI in terms of sensitivity, specificity and area under the curve (AUC).

METHODS AND FINDINGS

In this systematic review and meta-analysis, adhering to the PRISMA statement, 61 studies, with 65 study subsets, in females with benign or malignant primary breast lesions (6291 lesions) were assessed. Studies on DWI, quantified by ADC, scanned on 1.5 and 3.0 Tesla and using b-values 0/50 and ≥ 800 s/mm2 were included. PubMed and EMBASE were searched for studies up to 23-10-2019 (n = 2897). Data were pooled based on four BTTS methods (by definition of measured region of interest, ROI): BTTS1: whole breast tumor tissue selection, BTTS2: subtracted whole breast tumor tissue selection, BTTS3: circular breast tumor tissue selection and BTTS4: lowest diffusion breast tumor tissue selection. BTTS methods 2 and 3 excluded necrotic, cystic and hemorrhagic areas. Pooled sensitivity, specificity and AUC of the BTTS methods were calculated. Heterogeneity was explored using the inconsistency index (I2) and considering covariables: field strength, lowest b-value, image of BTTS selection, pre-or post-contrast DWI, slice thickness and ADC threshold. Pooled sensitivity, specificity and AUC were: 0.82 (0.72-0.89), 0.79 (0.65-0.89), 0.88 (0.85-0.90) for BTTS1; 0.91 (0.89-0.93), 0.84 (0.80-0.87), 0.94 (0.91-0.96) for BTTS2; 0.89 (0.86-0.92), 0.90 (0.85-0.93), 0.95 (0.93-0.96) for BTTS3 and 0.90 (0.86-0.93), 0.84 (0.81-0.87), 0.86 (0.82-0.88) for BTTS4, respectively. Significant heterogeneity was found between studies (I2 = 95).

CONCLUSIONS

None of the breast tissue selection (BTTS) methodologies outperformed in differentiating benign from malignant breast lesions. The high heterogeneity of ADC data acquisition demands further standardization, such as DWI acquisition parameters and tumor tissue selection to substantially increase the reliability of DWI of the breast.

Diffusion-weighted imaging of the breast-a consensus and mission statement from the EUSOBI International Breast Diffusion-Weighted Imaging working group

The European Society of Breast Radiology (EUSOBI) established an International Breast DWI working group. The working group consists of clinical breast MRI experts, MRI physicists, and representatives from large vendors of MRI equipment, invited based upon proven expertise in breast MRI and/or in particular breast DWI, representing 25 sites from 16 countries. The aims of the working group are (a) to promote the use of breast DWI into clinical practice by issuing consensus statements and initiate collaborative research where appropriate; (b) to define necessary standards and provide practical guidance for clinical application of breast DWI; (c) to develop a standardized and translatable multisite multivendor quality assurance protocol, especially for multisite research studies; (d) to find consensus on optimal methods for image processing/analysis, visualization, and interpretation; and (e) to work collaboratively with system vendors to improve breast DWI sequences. First consensus recommendations, presented in this paper, include acquisition parameters for standard breast DWI sequences including specifications of b values, fat saturation, spatial resolution, and repetition and echo times. To describe lesions in an objective way, levels of diffusion restriction/hindrance in the breast have been defined based on the published literature on breast DWI. The use of a small ROI placed on the darkest part of the lesion on the ADC map, avoiding necrotic, noisy or non-enhancing lesion voxels is currently recommended. The working group emphasizes the need for standardization and quality assurance before ADC thresholds are applied. The working group encourages further research in advanced diffusion techniques and tailored DWI strategies for specific indications.

Key Points

• The working group considers breast DWI an essential part of a multiparametric breast MRI protocol and encourages its use.

• Basic requirements for routine clinical application of breast DWI are provided, including recommendations on b values, fat saturation, spatial resolution, and other sequence parameters.

• Diffusion levels in breast lesions are defined based on meta-analysis data and methods to obtain a reliable ADC value are detailed.

Role of Breast MRI in the Evaluation and Detection of DCIS: Opportunities and Challenges

Historically, breast magnetic resonance imaging (MRI) was not considered an effective modality in the evaluation of ductal carcinoma in situ (DCIS). Over the past decade this has changed, with studies demonstrating that MRI is the most sensitive imaging tool for detection of all grades of DCIS. It has been suggested that not only is breast MRI the most sensitive imaging tool for detection but it may also detect the most clinically relevant DCIS lesions. The role and outcomes of MRI in the preoperative setting for patients with DCIS remains controversial; however, several studies have shown benefit in the preoperative evaluation of extent of disease as well as predicting an underlying invasive component. The most common presentation of DCIS on MRI is nonmass enhancement (NME) in a linear or segmental distribution pattern. Maximizing breast MRI spatial resolution is therefore beneficial, given the frequent presentation of DCIS as NME on MRI. Emerging MRI techniques, such as diffusion-weighted imaging (DWI), have shown promising potential to discriminate DCIS from benign and invasive lesions. Future opportunities including advanced imaging visual techniques, radiomics/radiogenomics, and machine learning / artificial intelligence may also be applicable to the detection and treatment of DCIS. Level of Evidence: 3 Technical Efficacy Stage: 3 J. Magn. Reson. Imaging 2019. J. Magn. Reson. Imaging 2020;52:697-709.

Diagnostic Performance of Diffusion Tensor Imaging for Characterizing Breast Tumors: A Comprehensive Meta-Analysis

Rationale and Objectives: Controversy still exists on the diagnosability of diffusion tensor imaging (DTI) for breast lesions characterization across published studies. The clinical guideline of DTI used in the breast has not been established. This meta-analysis aims to pool relevant evidences and evaluate the diagnostic performance of DTI in the differential diagnosis of malignant and benign breast lesions.

Materials and Methods: The studies that assessed the diagnostic performance of DTI parameters in the breast were searched in Embase, PubMed, and Cochrane Library between January 2010 and September 2019. Standardized mean differences and 95% confidence intervals of fractional anisotropy (FA), mean diffusivity (MD), and three diffusion eigenvalues (λ1, λ2, and λ3) were calculated using Review Manager 5.2. The pooled sensitivity, specificity, and area under the curve (AUC) were calculated with a bivariate model. Publication bias and heterogeneity between studies were also assessed using Stata 12.0.

Results: Sixteen eligible studies incorporating 1,636 patients were included. The standardized mean differences indicated that breast cancers had a significantly higher FA but lower MD, λ1, λ2, and λ3 than those of benign lesions (all P < 0.05). Subgroup analysis indicated that invasive breast carcinoma (IBC) had a significantly lower MD value than that of ductal carcinoma in situ (DCIS) (P = 0.02). λ1 showed the best diagnostic accuracy with pooled sensitivity, specificity, and AUC of 93%, 92%, and 0.97, followed by MD (AUC = 0.92, sensitivity = 87%, specificity = 83%) and FA (AUC = 0.76, sensitivity = 70%, specificity = 70%) in the differential diagnosis of breast lesions.

Conclusion: DTI with multiple quantitative parameters was adequate to differentiate breast cancers from benign lesions based on their biological characteristics. MD can further distinguish IBC from DCIS. The parameters, especially λ1 and MD, should attract our attention in clinical practice.

FDA- and EMA-Approved Tyrosine Kinase Inhibitors in Advanced EGFR-Mutated Non-Small Cell Lung Cancer: Safety, Tolerability, Plasma Concentration Monitoring, and Management

Non-small-cell lung cancer (NSCLC) is the most common form of primary lung cancer. The discovery of several oncogenic driver mutations in patients with NSCLC has allowed the development of personalized treatments based on these specific molecular alterations, in particular in the tyrosine kinase (TK) domain of the epidermal growth factor receptor (EGFR) gene. Gefitinib, erlotinib, afatinib, and osimertinib are TK inhibitors (TKIs) that specifically target EGFR and are currently approved by the Food and Drug Administration (FDA) and the European Medicines Agency (EMA) as first line treatment for sensitive EGFR-mutant patients. However, these four drugs are associated with severe adverse events (AEs) that can significantly impact patient health-related quality of life and patient monitoring. EGFR-TKIs are commonly used together with other types of medication that can substantially interact. Here, we review approaches used for the management of TKI-AEs in patients with advanced NSCLC to promote the benefits of treatments and minimize the risk of TKI treatment discontinuation. We also consider potential TKI–drug interactions and discuss the usefulness of plasma concentration monitoring TKIs based on chromatographic and mass spectrometry approaches to guide clinical decision-making. Adjusting the most appropriate therapeutic strategies and drug doses may improve the performance therapy and prognosis of patients with advanced EGFR-mutated NSCLC.

Can apparent diffusion coefficient (ADC) distinguish breast cancer from benign breast findings? A meta-analysis based on 13 847 lesions

BACKGROUND

The purpose of the present meta-analysis was to provide evident data about use of Apparent Diffusion Coefficient (ADC) values for distinguishing malignant and benign breast lesions.

METHODS

MEDLINE library and SCOPUS database were screened for associations between ADC and malignancy/benignancy of breast lesions up to December 2018. Overall, 123 items were identified. The following data were extracted from the literature: authors, year of publication, study design, number of patients/lesions, lesion type, mean value and standard deviation of ADC, measure method, b values, and Tesla strength. The methodological quality of the 123 studies was checked according to the QUADAS-2 instrument. The meta-analysis was undertaken by using RevMan 5.3 software. DerSimonian and Laird random-effects models with inverse-variance weights were used without any further correction to account for the heterogeneity between the studies. Mean ADC values including 95% confidence intervals were calculated separately for benign and malign lesions.

RESULTS

The acquired 123 studies comprised 13,847 breast lesions. Malignant lesions were diagnosed in 10,622 cases (76.7%) and benign lesions in 3225 cases (23.3%). The mean ADC value of the malignant lesions was 1.03 × 10^- 3 mm²/s and the mean value of the benign lesions was 1.5 × 10^- 3 mm²/s. The calculated ADC values of benign lesions were over the value of 1.00 × 10^- 3 mm²/s. This result was independent on Tesla strength, choice of b values, and measure methods (whole lesion measure vs estimation of ADC in a single area).

CONCLUSION

An ADC threshold of 1.00 × 10^- 3 mm²/s can be recommended for distinguishing breast cancers from benign lesions.

Application of in vivo MR methods in the study of breast cancer metabolism

In the last two decades, various in vivo MR methodologies have been evaluated for their potential in the study of cancer metabolism. During malignant transformation, metabolic alterations occur, leading to morphological and functional changes. Among various MR methods, in vivo MRS has been extensively used in breast cancer to study the metabolism of cells, tissues or whole organs. It provides biochemical information at the metabolite level. Altered choline, phospholipid and energy metabolism has been documented using proton (¹ H), phosphorus (³¹ P) and carbon (¹³ C) isotopes. Increased levels of choline-containing compounds, phosphomonoesters and phosphodiesters in breast cancer, which are indicative of altered choline and phospholipid metabolism, have been reported using in vivo, in vitro and ex vivo NMR studies. These changes are reversed on successful therapy, which depends on the treatment regimen given. Monitoring the various tumor intermediary metabolic pathways using nuclear spin hyperpolarization of ¹³ C-labeled substrates by dynamic nuclear polarization has also been recently reported. Furthermore, the utility of various methods such as diffusion, dynamic contrast and perfusion MRI have also been evaluated to study breast tumor metabolism. Parameters such as tumor volume, apparent diffusion coefficient, volume transfer coefficient and extracellular volume ratio are estimated. These parameters provide information on the changes in tumor microstructure, microenvironment, abnormal vasculature, permeability and grade of the tumor. Such changes seen during cancer progression are due to alterations in the tumor metabolism, leading to changes in cell architecture. Due to architectural changes, the tissue mechanical properties are altered; this can be studied using magnetic resonance elastography, which measures the elastic properties of tissues. Moreover, these structural MRI methods can be used to investigate the effect of therapy-induced changes in tumor characteristics. This review discusses the potential of various in vivo MR methodologies in the study of breast cancer metabolism.

Diffusion MRI of the breast: Current status and future directions

Diffusion-weighted imaging (DWI) is increasingly being incorporated into routine breast MRI protocols in many institutions worldwide, and there are abundant breast DWI indications ranging from lesion detection and distinguishing malignant from benign tumors to assessing prognostic biomarkers of breast cancer and predicting treatment response. DWI has the potential to serve as a noncontrast MR screening method. Beyond apparent diffusion coefficient (ADC) mapping, which is a commonly used quantitative DWI measure, advanced DWI models such as intravoxel incoherent motion (IVIM), non-Gaussian diffusion MRI, and diffusion tensor imaging (DTI) are extensively exploited in this field, allowing the characterization of tissue perfusion and architecture and improving diagnostic accuracy without the use of contrast agents. This review will give a summary of the clinical literature along with future directions. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;52:70-90.

Diffusion tensor imaging for characterizing tumor microstructure and improving diagnostic performance on breast MRI: a prospective observational study

Background

Diffusion-weighted imaging (DWI) can increase breast MRI diagnostic specificity due to the tendency of malignancies to restrict diffusion. Diffusion tensor imaging (DTI) provides further information over conventional DWI regarding diffusion directionality and anisotropy. Our study evaluates DTI features of suspicious breast lesions detected on MRI to determine the added diagnostic value of DTI for breast imaging.

Methods

With IRB approval, we prospectively enrolled patients over a 3-year period who had suspicious (BI-RADS category 4 or 5) MRI-detected breast lesions with histopathological results. Patients underwent multiparametric 3 T MRI with dynamic contrast-enhanced (DCE) and DTI sequences. Clinical factors (age, menopausal status, breast density, clinical indication, background parenchymal enhancement) and DCE-MRI lesion parameters (size, type, presence of washout, BI-RADS category) were recorded prospectively by interpreting radiologists. DTI parameters (apparent diffusion coefficient [ADC], fractional anisotropy [FA], axial diffusivity [λ₁], radial diffusivity [(λ₂ + λ₃)/2], and empirical difference [λ₁ − λ₃]) were measured retrospectively. Generalized estimating equations (GEE) and least absolute shrinkage and selection operator (LASSO) methods were used for univariate and multivariate logistic regression, respectively. Diagnostic performance was internally validated using the area under the curve (AUC) with bootstrap adjustment.

Results

The study included 238 suspicious breast lesions (95 malignant, 143 benign) in 194 women. In univariate analysis, lower ADC, axial diffusivity, and radial diffusivity were associated with malignancy (OR = 0.37–0.42 per 1-SD increase, p < 0.001 for each), as was higher FA (OR = 1.45, p = 0.007). In multivariate analysis, LASSO selected only ADC (OR = 0.41) as a predictor for a DTI-only model, while both ADC (OR = 0.41) and FA (OR = 0.88) were selected for a model combining clinical and imaging parameters. Post-hoc analysis revealed varying association of FA with malignancy depending on the lesion type. The combined model (AUC = 0.81) had a significantly better performance than Clinical/DCE-MRI-only (AUC = 0.76, p < 0.001) and DTI-only (AUC = 0.75, p = 0.002) models.

Conclusions

DTI significantly improves diagnostic performance in multivariate modeling. ADC is the most important diffusion parameter for distinguishing benign and malignant breast lesions, while anisotropy measures may help further characterize tumor microstructure and microenvironment.

A Meta-analysis of the Diagnostic Performance of Diffusion MRI for Breast Lesion Characterization

Background Various techniques are available to assess diffusion properties of breast lesions as a marker of malignancy at MRI. The diagnostic performance of these diffusion markers has not been comprehensively assessed. Purpose To compare by meta-analysis the diagnostic performance of parameters from diffusion-weighted imaging (DWI), diffusion-tensor imaging (DTI), and intravoxel incoherent motion (IVIM) in the differential diagnosis of malignant and benign breast lesions. Materials and Methods PubMed and Embase databases were searched from January to March 2018 for studies in English that assessed the diagnostic performance of DWI, DTI, and IVIM in the breast. Studies were reviewed according to eligibility and exclusion criteria. Publication bias and heterogeneity between studies were assessed. Pooled summary estimates for sensitivity, specificity, and area under the curve were obtained for each parameter by using a bivariate model. A subanalysis investigated the effect of MRI parameters on diagnostic performance by using a Student t test or a one-way analysis of variance. Results From 73 eligible studies, 6791 lesions (3930 malignant and 2861 benign) were included. Publication bias was evident for studies that evaluated apparent diffusion coefficient (ADC). Significant heterogeneity (P < .05) was present for all parameters except the perfusion fraction (f). The pooled sensitivity, specificity, and area under the curve for ADC was 89%, 82%, and 0.92, respectively. The highest performing parameter for DTI was the prime diffusion coefficient (λ1), and pooled sensitivity, specificity, and area under the curve was 93%, 90%, and 0.94, respectively. The highest performing parameter for IVIM was tissue diffusivity (D), and the pooled sensitivity, specificity, and area under the curve was 88%, 79%, and 0.90. Choice of MRI parameters had no significant effect on diagnostic performance. Conclusion Diffusion-weighted imaging, diffusion-tensor imaging, and intravoxel incoherent motion have comparable diagnostic accuracy with high sensitivity and specificity. Intravoxel incoherent motion is comparable to apparent diffusion coefficient. Diffusion-tensor imaging is potentially promising but to date the number of studies is limited. © RSNA, 2019 Online supplemental material is available for this article.

Association among T2 signal intensity, necrosis, ADC and Ki-67 in estrogen receptor-positive and HER2-negative invasive ductal carcinoma

PURPOSE

To determine whether T2 signal intensity, necrosis, and ADC values are associated with Ki-67 in patients with Estrogen Receptor (ER)-positive and Human epidermal growth factor receptor type 2 (HER2)-negative invasive ductal carcinoma (IDC).

MATERIALS AND METHODS

Between March 2012 and February 2013, one hundred eighty seven women with ER-positive and HER2-negative IDC who underwent breast MRI and subsequent surgery were included. Intratumoral signal intensity was evaluated based on a combination of T2-weighted (low or equal, high, or very high) and contrast-enhanced MR images (enhancement or not). Necrosis was defined as very high T2 and no enhancement. Using the analysis of variance and pairwise t-test, a model based on intratumoral signal intensity was developed to assess Ki-67 of the surgical specimen. Inter-observer agreement for the developed model was analyzed. Conventional mean and minimum apparent diffusion coefficient (ADC) measurements were performed and correlated with Ki-67.

RESULTS

As the grade of the developed model increased (Grade I: low or equal T2, Grade II: high T2, or necrosis < 50%, Grade III: necrosis ≥ 50%), mean Ki-67 significantly increased (Grade I to III: 12.5%, 17.6%, 45.0%, respectively; P < 0.001). Good inter-observer agreement was found for the model (κ = 0.846, P < 0.001). ADC did not show significant correlations with Ki-67 (Pearson's correlation coefficient, 0.140 [P = 0.057] for mean ADC; -0.079 [P = 0.284] for minimum ADC).

CONCLUSION

Intratumoral signal intensity but not ADC was associated with Ki-67 in patients with ER-positive and HER2-negative IDC.

Monitoring In-Vivo the Mammary Gland Microstructure during Morphogenesis from Lactation to Post-Weaning Using Diffusion Tensor MRI

Lactation and the return to the pre-conception state during post-weaning are regulated by hormonal induced processes that modify the microstructure of the mammary gland, leading to changes in the features of the ductal / glandular tissue, the stroma and the fat tissue. These changes create a challenge in the radiological workup of breast disorder during lactation and early post-weaning. Here we present non-invasive MRI protocols designed to record in vivo high spatial resolution, T₂-weighted images and diffusion tensor images of the entire mammary gland. Advanced imaging processing tools enabled tracking the changes in the anatomical and microstructural features of the mammary gland from the time of lactation to post-weaning. Specifically, by using diffusion tensor imaging (DTI) it was possible to quantitatively distinguish between the ductal / glandular tissue distention during lactation and the post-weaning involution. The application of the T₂-weighted imaging and DTI is completely safe, non-invasive and uses intrinsic contrast based on differences in transverse relaxation rates and water diffusion rates in various directions, respectively. This study provides a basis for further in-vivo monitoring of changes during the mammary developmental stages, as well as identifying changes due to malignant transformation in patients with pregnancy associated breast cancer (PABC).

Additional value of diffusion-weighted imaging to evaluate multifocal and multicentric breast cancer detected using pre-operative breast MRI

OBJECTIVES

To investigate whether diffusion-weighted imaging (DWI) aids pre-operative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to evaluate additional lesions in breast cancer patients.

METHODS

DCE-MRI and DWI were performed on 131 lesions, with available histopathological results. The apparent diffusion coefficient (ADC) of each lesion was measured, and the cut-off value for differentiation between malignant and benign lesions was calculated. A protocol combining the ADC cut-off value with DCE-MRI was validated in a cohort of 107 lesions in 77 patients.

RESULTS

When an ADC cut-off value of 1.11 × 10^-3 mm²/s from the development cohort was applied to the additional lesions in the validation cohort, the specificity increased from 18.9% to 67.6% (P < 0.001), and the diagnostic accuracy increased from 61.7% to 82.2% (P = 0.05), without significant loss of sensitivity (98.6% vs. 90.0%, P = 0.07). The negative predictive values of lesions in the same quadrant had decreased, as had those of lesions ≥1 cm in diameter. The ADC cut-off value in the validation cohort was 1.05 × 10^-3 mm²/s.

CONCLUSIONS

Additional implementation of DWI for breast lesions in pre-operative MRI can help to obviate unnecessary biopsies by increasing specificity. However, to avoid missing cancers, clinicians should closely monitor lesions located in the same quadrant or lesions ≥1 cm.

KEY POINTS

• DWI can be used to further differentiate lesions during pre-operative cancer staging. • ADC cut-off values were similar in the development and validation cohorts. • DWI improves both PPV and NPV in cases of multicentric lesions. • DWI improves both PPV and NPV in lesions <1 in diameter. • NPVs are decreased in multifocal lesions and lesions ≥1 cm in diameter.

Whole-lesion histogram analysis metrics of the apparent diffusion coefficient as a marker of breast lesions characterization at 1.5 T

INTRODUCTION

To retrospectively assess the role of whole-lesion apparent diffusion coefficient (ADC) in the characterization of breast tumors by comparing different histogram metrics.

METHODS

49 patients with 53 breast lesions underwent magnetic resonance imaging (MRI). ADC histogram parameters, including the mean, mode, 10th/50th/90th percentile, skewness, kurtosis, and entropy ADCs, were derived for the whole-lesion volume in each patient. Mann-Whitney U-test, area under the receiver-operating characteristic curve (AUC) were used for statistical analysis.

RESULTS

The mean, mode and 10th/50th/90th percentile ADC values were significantly lower in malignant lesions compared with benign ones (all P < 0.0001), while skewness was significantly higher in malignant lesions P = 0.02. However, no significant difference was found between entropy and kurtosis values in malignant lesions compared with benign ones (P = 0.06 and P = 1.00, respectively). Univariate logistic regression showed that 10th and 50th percentile ADC yielded the highest AUC (0.985; 95% confidence interval [CI]: 0.902, 1.000 and 0.982; 95% confidence interval [CI]: 0.896, 1.000 respectively), whereas kurtosis value yielded the lowest AUC (0.500; 95% CI: 0.355, 0.645), indicating that 10th and 50th percentile ADC values may be more accurate for lesion discrimination.

CONCLUSION

Whole-lesion ADC histogram analysis could be a helpful index in the characterization and differentiation between benign and malignant breast lesions with the 10th and 50th percentile ADC be the most accurate discriminators.

Microstructural models for diffusion MRI in breast cancer and surrounding stroma: an ex vivo study

The diffusion signal in breast tissue has primarily been modelled using apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM) and diffusion tensor (DT) models, which may be too simplistic to describe the underlying tissue microstructure. Formalin-fixed breast cancer samples were scanned using a wide range of gradient strengths, durations, separations and orientations. A variety of one- and two-compartment models were tested to determine which best described the data. Models with restricted diffusion components and anisotropy were selected in most cancerous regions and there were no regions in which conventional ADC or DT models were selected. Maps of ADC generally related to cellularity on histology, but maps of parameters from more complex models suggest that both overall cell volume fraction and individual cell size can contribute to the diffusion signal, affecting the specificity of ADC to the tissue microstructure. The areas of coherence in diffusion anisotropy images were small, approximately 1 mm, but the orientation corresponded to stromal orientation patterns on histology.

Diffusion Tensor Imaging for Assessment of Response to Neoadjuvant Chemotherapy in Patients With Breast Cancer

In this study, the prognostic significance of tumor metrics derived from diffusion tensor imaging (DTI) was evaluated in patients with locally advanced breast cancer undergoing neoadjuvant therapy. DTI and contrast-enhanced magnetic resonance imaging were acquired at 1.5 T in 34 patients before treatment and after 3 cycles of taxane-based therapy (early treatment). Tumor fractional anisotropy (FA), principal eigenvalues (λ1, λ2, and λ3), and apparent diffusion coefficient (ADC) were estimated for tumor regions of interest drawn on DTI data. The association between DTI metrics and final tumor volume change was evaluated with Spearman rank correlation. DTI metrics were investigated as predictors of pathological complete response (pCR) by calculating the area under the receiver operating characteristic curve (AUC). Early changes in tumor FA and ADC significantly correlated with final tumor volume change post therapy (ρ = -0.38, P = .03 and ρ = -0.71, P < .001, respectively). Pretreatment tumor ADC was significantly lower in the pCR than in the non-pCR group (P = .04). At early treatment, patients with pCR had significantly higher percent changes of tumor λ1, λ2, λ3, and ADC than those without pCR. The AUCs for early percent changes in tumor FA and ADC were 0.60 and 0.83, respectively. The early percent changes in tumor eigenvalues and ADC were the strongest DTI-derived predictors of pCR. Although early percent change in tumor FA had a weak association with pCR, the significant correlation with final tumor volume change suggests that this metric changes with therapy and may merit further evaluation.

Diagnostic Performance of Diffusion Tensor Imaging with Readout-segmented Echo-planar Imaging for Invasive Breast Cancer: Correlation of ADC and FA with Pathological Prognostic Markers

Purpose:

To assess the diagnostic performance of readout-segmented echo-planar diffusion tensor imaging (DTI based on rs-EPI) for breast cancer and to determine the correlation between the apparent diffusion coefficient (ADC) and fractional anisotropy (FA) values obtained from DTI based on rs-EPI with prognostic markers of invasive breast cancer.

Materials and Methods:

This retrospective study examined 80 pathologically proven breast lesions (22 benign and 58 malignant lesions) of 80 patients who underwent both diffusion-weighted imaging based on single-shot echo-planar imaging (DWI based on ss-EPI) and DTI based on rs-EPI with b-values of 0 and 1000. We identified and compared the diagnostic performances of the DWI based on ss-EPI and the DTI based on rs-EPI using ADCs by conducting a receiver-operating-characteristics (ROC) analysis. We determined the correlations between the ADCs and the prognostic markers and those of the FA values and the same markers.

Results:

The median ADCs of the benign and malignant lesions based on the ss-EPI were 1.57 and 1.2 × 10⁻³ mm²/sec, and those based on the rs-EPI were 1.53 and 1.09 × 10⁻³ mm²/sec, respectively. The area under the curve on the ROC analysis based on rs-EPI (0.924) was greater than that based on ss-EPI (0.897). There were no significant correlations between the ADCs and the prognostic markers, but there were significant correlations between the FA values and the estrogen receptor status, a proliferative marker, the nuclear grade and the intrinsic subtype.

Conclusion:

For breast cancer, DTI based on rs-EPI had superior diagnostic performance compared to DWI based on ss-EPI. Compared with the ADCs, the FA values were more closely correlated with prognostic markers of invasive breast cancer.

Whole-lesion apparent diffusion coefficient (ADC) metrics as a marker of breast tumour characterization-comparison between ADC value and ADC entropy

OBJECTIVE

To prospectively assess the role of whole-lesion apparent diffusion coefficient (ADC) metrics in the characterization of breast tumours by comparing ADC value with ADC entropy.

METHODS

49 patients with 53 breast lesions underwent phased-array breast coil 1.5-T MRI. Two radiologists experienced in breast MRI, blinded to the final diagnosis, reviewed the ADC maps and placed a volume of interest on all slices including each lesion on the ADC map to obtain whole-lesion mean ADC value and ADC entropy. The mean ADC value and ADC entropy in benign and malignant lesions were compared by the Mann-Whitney U-test. Receiver-operating characteristic analysis was performed to assess the sensitivity and specificity of the two variables in the characterization of the breast lesions.

RESULTS

The benign (n = 19) and malignant lesions (n = 34) had mean diameters of 20.8 mm (10.1-31.5 mm) and 26.4 mm (10.5-42.3 mm), respectively. The mean ADC value of the malignant lesions was significantly lower than that of the benign ones (0.87 × 10^-3 vs 1.49 × 10^-3 mm² s^-1; p < 0.0001). Malignant ADC entropy was higher than benign entropy, without reaching levels of statistical significance (5.4 vs 5.0; p = 0.064). At a mean ADC cut-off value of 1.16 × 10^-3 mm² s^-1, the sensitivity and specificity for diagnosing malignancy became optimal (97.1% and 93.7, respectively) with an area under the curve (AUC) of 0.975. With regard to ADC entropy, the sensitivity and specificity at a cut-off of 5.18 were 67.6 and 68.7%, respectively, with an AUC of 0.664.

CONCLUSION

Whole-lesion mean ADC could be a helpful index in the characterization of suspicious breast lesions, with higher sensitivity and specificity than ADC entropy. Advances in knowledge: Two separate parameters of the whole-lesion histogram were compared for their diagnostic accuracy in characterizing breast lesions. Mean ADC was found to be able to characterize breast lesions, whereas entropy proved to be unable to differentiate benign from malignant breast lesions. It is, however, likely that entropy may distinguish these two groups if a larger cohort were used, or the fact that this may be influenced by the molecular subtypes of breast cancers included.

Diffusion-weighted breast MRI: Clinical applications and emerging techniques

Diffusion-weighted MRI (DWI) holds potential to improve the detection and biological characterization of breast cancer. DWI is increasingly being incorporated into breast MRI protocols to address some of the shortcomings of routine clinical breast MRI. Potential benefits include improved differentiation of benign and malignant breast lesions, assessment and prediction of therapeutic efficacy, and noncontrast detection of breast cancer. The breast presents a unique imaging environment with significant physiologic and inter-subject variations, as well as specific challenges to achieving reliable high quality diffusion-weighted MR images. Technical innovations are helping to overcome many of the image quality issues that have limited widespread use of DWI for breast imaging. Advanced modeling approaches to further characterize tissue perfusion, complexity, and glandular organization may expand knowledge and yield improved diagnostic tools.

LEVEL OF EVIDENCE

5 J. Magn. Reson. Imaging 2016 J. Magn. Reson. Imaging 2017;45:337-355.

Diffusion tensor imaging of breast lesions: evaluation of apparent diffusion coefficient and fractional anisotropy and tissue cellularity

OBJECTIVE

To investigate the apparent diffusion coefficient (ADC) and fractional anisotropy (FA) measured by diffusion tensor imaging (DTI), tissue cellularity and their relationship in breast malignant/benign lesions.

METHODS

88 patients with 88 breast lesions who underwent DTI and dynamic contrast-enhanced MR scanning between November 2013 and December 2014 were retrospectively analyzed. The diagnosis was confirmed pathologically. ADC and FA values as well as histopathological cellularity of different pathological types of lesions were analyzed and compared statistically. The Pearson's correlation between cellularity and ADC and FA was calculated.

RESULTS

There were 59 cases of breast cancer and 29 cases of benign lesions included in the study. ADC values of breast cancers were statistically lower than that of benign lesions (p < 0.001). FA and cellularity were higher in cancers than in benign lesions with statistical significance (p < 0.05 and p < 0.001, respectively). The mean FA values in the patients with invasive ductal carcinoma (IDC) were higher than that in the patients with ductal carcinoma in situ (DCIS) without statistical difference (p > 0.05). The ADC and the cellularity in the IDC of grade III were statistically lower (p < 0.05) and higher (p < 0.05) than that in the DCIS and IDC of grade I-II, respectively. ADC was negatively correlated to cellularity (r = -0.8319, p < 0.001) and FA was positively correlated to cellularity (r = 0.4231, p < 0.001).

CONCLUSION

ADC and FA values were statistically different between benign and malignant breast lesions and were significantly correlated to tissue cellularity. ADC and FA may help to discriminate malignant from benign breast lesions and to predict cellularity. ADC is helpful in the prediction of the grade of breast cancer.

ADVANCES IN KNOWLEDGE

ADC and FA values were statistically different between benign and malignant breast lesions and were significantly correlated to tissue cellularity.

Assessing Detection, Discrimination, and Risk of Breast Cancer According to Anisotropy Parameters of Diffusion Tensor Imaging

BACKGROUND The aim of this study was to investigate whether the anisotropy parameters are helpful in the detection and discrimination of breast cancers, and to determine its value in predicting the risk of cancers. MATERIAL AND METHODS There were 56 patients with 56 lesions (34 malignant, 22 benign) included in the study. DTI was performed in every patient and apparent diffusion coefficient (ADC), fractional anisotropy (FA), and eigenvalues E1, E2, and E3 were measured in every lesion and the normal breast tissue. RESULTS ADC, FA, and eigenvalues of E1, E2, E3, and E1-E3 in breast cancers were all significantly lower than in normal tissue (P<0.001 for all) with mean reduction of (32 ± 17)%, (24 ± 13)%, (33 ± 19)%, (32 ± 17)%, (31 ± 18)%, and (37 ± 20)% for ADC, FA, E1, E2, E3, and E1-E3, respectively. These parameters were also statistically lower in cancers than in benign lesions (P<0.01 for all), except FA (P>0.05). ADC, E1, E2, and E3 were very similar in discriminating breast cancers and benign lesions, with area under the curve (AUC) 0.885-0.898, sensitivity 73.5-85.3%, and specificity 90.9-100%. CONCLUSIONS ADC, E1, E2, E3, and E1-E3 are much lower in breast cancers than in normal tissue and benign lesions. The reduction of ADC, E1, E2, E3, and E1-E3 of a mass in the breast is highly associated with the risk of breast cancer, but the FA has no utility in breast cancer risk prediction.

Diffusion tensor imaging in the normal breast: influences of fibroglandular tissue composition and background parenchymal enhancement

OBJECTIVE

To evaluate effects of fibroglandular tissue (FGT) composition and background parenchymal enhancement (BPE) on diffusion tensor imaging (DTI) parameters in normal breast tissue.

METHODS

DTI analysis was performed on 35 breasts with regions of interest drawn to include only normal tissue. Breasts were dichotomized by FGT composition and by BPE; DTI parameters were compared.

RESULTS

The λ1 principal diffusion coefficient was lower in breasts with moderate/marked BPE versus those with minimal/mild BPE (P=.039). All other parameters were unaffected.

CONCLUSION

λ1 is sensitive to differences in BPE within normal breast tissue that should be taken into account in DTI evaluation.

Diffusion-Weighted Imaging in 3.0 Tesla Breast MRI: Diagnostic Performance and Tumor Characterization Using Small Subregions vs. Whole Tumor Regions of Interest

Introduction

Apparent diffusion coefficient (ADC) values are increasingly reported in breast MRI. As there is no standardized method for ADC measurements, we evaluated the effect of the size of region of interest (ROI) to diagnostic utility and correlation to prognostic markers of breast cancer.

Methods

This prospective study was approved by the Institutional Ethics Board; the need for written informed consent for the retrospective analyses of the breast MRIs was waived by the Chair of the Hospital District. We compared diagnostic accuracy of ADC measurements from whole-lesion ROIs (WL-ROIs) to small subregions (S-ROIs) showing the most restricted diffusion and evaluated correlations with prognostic factors in 112 consecutive patients (mean age 56.2±11.6 years, 137 lesions) who underwent 3.0-T breast MRI.

Results

Intra- and interobserver reproducibility were substantial (κ = 0.616–0.784; Intra-Class Correlation 0.589–0.831). In receiver operating characteristics analysis, differentiation between malignant and benign lesions was excellent (area under curve 0.957–0.962, cut-off ADC values for WL-ROIs: 0.87×10⁻³ mm²s^-1; S-ROIs: 0.69×10⁻³ mm²s^-1, P<0.001). WL-ROIs/S-ROIs achieved sensitivities of 95.7%/91.3%, specificities of 89.5%/94.7%, and overall accuracies of 89.8%/94.2%. In S-ROIs, lower ADC values correlated with presence of axillary metastases (P = 0.03), high histological grade (P = 0.006), and worsened Nottingham Prognostic Index Score (P<0.05). In both ROIs, ADC values correlated with progesterone receptors and advanced stage (P<0.01), but not with HER2, estrogen receptors, or Ki-67.

Conclusions

ADC values assist in breast tumor characterization. Small ROIs were more accurate than whole-lesion ROIs and more frequently associated with prognostic factors. Cut-off values differed significantly depending on measurement procedure, which should be recognized when comparing results from the literature. Instead of using a whole lesion covering ROI, a small ROI could be advocated in diffusion-weighted imaging.

Diffusion tensor tractography for the surgical management of peripheral nerve sheath tumors

OBJECT

Peripheral nerve sheath tumors (PNSTs) are uncommon but bear a significant risk of malignancy. High-resolution MRI is the standard technique for characterizing PNSTs. However, planning the appropriate extent of resection and subsequent reconstructive strategies is highly dependent on the intraoperative findings because preoperative MRI evaluation can be insufficient. Diffusion tensor tractography (DTT) represents a recently developed advanced MRI technique that reveals the microstructure of tissues based on monitoring the random movement of water molecules. DTT has the potential to provide diagnostic insights beyond conventional MRI techniques due to its mapping of specific fibrillar nerve structures. Here, DTT was applied to evaluate PNSTs and to examine the usefulness of this method for the correct delineation of tumor and healthy nerve tissue and the value of this information in the preoperative planning of surgical interventions.

METHODS

In this prospective study, patients with the clinical symptoms of a PNST were investigated using DTT 3-Tesla MRI scans. Image data processing and tractography were performed using the FACT (fiber assessment by continuous tracking) algorithm and multiple-regions-of-interest approach. The surgical findings were then compared with the results of the DTT MRI scans. Preoperative fascicle visualization and the correlation with the intraoperative findings were graded.

RESULTS

In a 21-month period, 12 patients with PNSTs were investigated (7 female and 5 male patients with a mean age of 46.2 ± 19.2 years). All patients underwent surgical removal of the tumor. Schwannoma was the most common benign histopathological finding (n = 7), whereas 2 malignant lesions were detected. In 10 of 12 patients, good preoperative nerve fascicle visualization was achieved using DTT scans. In 9 of 10 patients with good preoperative fascicle visualization, good intraoperative correlation between the DTT scans and surgical anatomy was found.

CONCLUSIONS

DTT properly visualizes the peripheral nerve fascicles and their correct anatomical relation to PNST. DTT represents a promising new method for the preinterventional planning of nerve tumor resection.

Subcategorization of Suspicious Breast Lesions (BI-RADS Category 4) According to MRI Criteria: Role of Dynamic Contrast-Enhanced and Diffusion-Weighted Imaging

OBJECTIVE

The purposes of this study were to investigate whether dynamic contrast-enhanced MRI is adequate for subcategorization of suspicious lesions (BI-RADS category 4) and to evaluate whether use of DWI improves diagnostic performance.

MATERIALS AND METHODS

The study group was composed of 103 suspicious lesions found in 83 subjects. Patient ages and lesion sizes were compiled, and two radiologists reanalyzed the images; subcategorized the findings as BI-RADS 4A, 4B, or 4C; and calculated apparent diffusion coefficient (ADC) values. The stratified variables were tested by univariate analysis and inserted in two multivariate predictive models, which were used to generate ROC curves and compare AUCs. Positive predictive values (PPVs) for each subcategory and ADC level were calculated, and interobserver agreement was tested.

RESULTS

Forty-four (42.7%) suspicious findings proved malignant. Except for age (p = 0.08), all stratified predictor variables were significant in univariate analyses (p < 0.01). Logistic regression models did not differ substantially after comparison of the ROC curves (p = 0.09), but the one including ADC values was slightly better: AUC of 0.89 (95% CI, 0.82-0.95) against AUC of 0.85 (95% CI, 0.78-0.93). PPV increased progressively in each BI-RADS 4 subcategory (4A, 0.15; 4B, 0.37; 4C, 0.84). ADC values of 1.10 × 10(-3) mm(2)/s or less had the second highest PPV (0.77). Interobserver agreement was substantial at a kappa value of 0.80 (95% CI, 0.70-0.90; p < 0.01).

CONCLUSION

Risk stratification of suspicious lesions (BI-RADS category 4) can be satisfactorily performed with DCE-MRI and slightly improved when DWI is introduced.

Preoperative axillary staging with 3.0-T breast MRI: clinical value of diffusion imaging and apparent diffusion coefficient

The axillary staging in newly diagnosed breast cancer is under major evolution. The aims of this study were to define the diagnostic performance of 3.0-T diffusion-weighted imaging (DWI) in the detection of axillary metastases in newly diagnosed breast cancer, to assess apparent diffusion coefficients (ADCs) for histopathologically confirmed metastatic lymph nodes in a clinical setting. Altogether 52 consecutive breast cancer patients underwent magnetic resonance imaging and DWI in addition to axillary ultrasound. ADCs of axillary lymph nodes were analysed by two breast radiologists and ultrasound-guided core biopsies were taken. In a separate reading by one radiologist two types of region of interests were used for a smaller group of patients. Altogether 56 axillae (121 lymph nodes) were included in the statistical analysis. Metastatic axillae (51.8%) had significantly lower ADCs (p<0.001). Mean ADCs were 0.663–0.676 x 10^-3 mm²/s for the histologically confirmed metastatic LNs and 1.100–1.225 x 10^-3 mm²/s for the benign. The sensitivity, specificity, and accuracy of DWI were 72.4%, 79.6%, and 75.9%, respectively with threshold ADC 0.812 x 10^-3 mm²/s. Region of interest with information on the minimum value increased the diagnostic performance (area under the curve 0.794 vs. 0.619). Even though ADCs are significantly associated with histopathologically confirmed axillary metastases the diagnostic performance of axillary DWI remains moderate and ultrasound-guided core biopsies or sentinel lymph node biopsies cannot be omitted.

Microscopic diffusion properties of fixed breast tissue: Preliminary findings

PURPOSE

To investigate the microscopic diffusion properties of formalin-fixed breast tissue.

METHODS

Diffusion microimaging was performed at 16.4T with 40-μm isotropic voxels on two normal and two cancer tissue samples from four patients. Results were correlated with histology of the samples.

RESULTS

Diffusion-weighted images and mean diffusivity maps demonstrated distinct diffusivity differences between breast tissue components. Mean diffusivity (MD) in normal tissue was 0.59 ± 0.24 μm(2) /ms for gland lobule (voxels containing epithelium and intralobular stroma) and 1.23 ± 0.34 μm(2) /ms for interlobular fibrous stroma. In the cancer samples, MD = 0.45 ± 0.23 μm(2) /ms for invasive ductal carcinoma (voxels contain epithelium and intralobular stroma) and 0.61 ± 0.35 μm(2) /ms for ductal carcinoma in situ. There were significant MD differences between all tissue components (P < 0.005), except between gland lobule and ductal carcinoma in situ (P = 0.71). The low diffusivity of epithelium-rich cancer tissue and of normal epithelium relative to its supporting fibrous stroma was similar to that reported for prostate tissue and the esophageal wall.

CONCLUSION

Diffusion microimaging demonstrates distinct diffusivity differences between breast tissue glandular structures. Low diffusivity may be a distinctive feature of mammalian epithelia.

Tracking the mammary architectural features and detecting breast cancer with magnetic resonance diffusion tensor imaging

Breast cancer is the most common cause of cancer among women worldwide. Early detection of breast cancer has a critical role in improving the quality of life and survival of breast cancer patients. In this paper a new approach for the detection of breast cancer is described, based on tracking the mammary architectural elements using diffusion tensor imaging (DTI). The paper focuses on the scanning protocols and image processing algorithms and software that were designed to fit the diffusion properties of the mammary fibroglandular tissue and its changes during malignant transformation. The final output yields pixel by pixel vector maps that track the architecture of the entire mammary ductal glandular trees and parametric maps of the diffusion tensor coefficients and anisotropy indices. The efficiency of the method to detect breast cancer was tested by scanning women volunteers including 68 patients with breast cancer confirmed by histopathology findings. Regions with cancer cells exhibited a marked reduction in the diffusion coefficients and in the maximal anisotropy index as compared to the normal breast tissue, providing an intrinsic contrast for delineating the boundaries of malignant growth. Overall, the sensitivity of the DTI parameters to detect breast cancer was found to be high, particularly in dense breasts, and comparable to the current standard breast MRI method that requires injection of a contrast agent. Thus, this method offers a completely non-invasive, safe and sensitive tool for breast cancer detection.

Test-retest reliability of diffusion tensor imaging of the liver at 3.0 T

PURPOSE

This study was done to evaluate test-retest reliability of liver diffusion tensor imaging (LDTI).

MATERIALS AND METHODS

Ten healthy volunteers (median age 23 years) underwent two LDTI scans on a 3.0 T magnet during two imaging sessions separated by 2 weeks (session-1/-2, respectively). Fifteen gradient directions and b values of 0-1,000 s/mm(2) were used. Two radiologists in consensus assessed liver apparent diffusion coefficient (ADC) and fraction of anisotropy (FA) values on ADC and FA maps at four reference levels, namely: right upper level (RUL), right lower level (RLL), left upper level (LUL) and left lower level (LLL). We then assessed (a) whether ADC and FA values overlapped when measured on different levels within the same imaging session or between different imaging sessions; (b) the degree of variability on an intra-session and inter-session basis, respectively, using the coefficient of variation (CV).

RESULTS

In sessions 1 and 2, the ADC/FA values were significantly larger in the left liver lobe (LUL/LLL) compared to right liver lobe (RUL/RLL) (p < 0.05/6). Intra-session CVs were 9.51 % (session 1) and 9.73 % (session 2) for ADC, and 12.93 % (session 1) and 11.82 % (session 2) for FA, respectively. When comparing RUL, RLL, LUL and LLL on an inter-session basis, CVs were 6.52, 8.20, 6.52 and 11.06 % for ADC, and 15.42, 15.80, 15.42 and 6.80 % for FA, respectively.

CONCLUSION

LDTI provides consistent and repeatable measurements. However, since larger left lobe ADC/FA values can be attributed to artefacts, right lobe values should be considered the most reliable measurements of water diffusivity within the liver.