1H MR spectroscopy and diffusion-weighted imaging of the breast: are they useful tools for characterizing breast lesions before biopsy?

Characteristics of Suspicious Breast Lesions Visible Only on MR Imaging: Is It Possible to Classify into Immediate Biopsy and Careful Observation Groups?

PURPOSE

To investigate the characteristics of suspicious MRI-only visible lesions and to explore the validity of subcategorizing these lesions into the following two groups: lesions that would require immediate biopsy (4Bi) and lesions for which careful clinical follow-up could be recommended (4Fo).

METHODS

A retrospective review of 108 MRI-only visible lesions in 106 patients who were diagnosed as Breast Imaging Reporting and Data System (BI-RADS) category 4 between June 2018 and June 2022 at our institution was performed by two radiologists. The breast MR images were evaluated according to BI-RADS and additional MRI descriptors (linear ductal, branching, and apparent diffusion coefficient values). The lesions were categorized by previously reported classification systems, and the positive predictive values (PPVs) for the different categories were determined and compared. Subsequently, a new classification system was developed in this study.

RESULTS

The total malignancy rate was 31% (34/108). No significant differences between benign and malignant lesions were identified for focus and mass lesions. For non-mass lesions, linear ductal and heterogeneous internal enhancement suggested a benign lesion (P = 0.0013 and P = 0.023, respectively), and branching internal enhancement suggested malignancy (P = 0.0066). Segmental distribution suggested malignancy (P = 0.0097). However, the PPV of segmental distribution with heterogeneous enhancement was significantly lower than that of category 4 segmental lesions with other enhancement patterns (11% vs. 59%; P = 0.0198).As a new classification, the distribution of focal, linear, and segmental was given a score of 0, 1, or 2, and the internal enhancement of heterogeneous, linear-ductal, clumped, branching, and clustered-ring enhancement was given a score of 0, 1, 2, 3, and 4, respectively. When categorized using a scoring system, a statistically significant difference in PPV was observed between 4Fo (n = 27) and 4Bi (n = 33) (7% vs. 61%, P = 0.000029).

CONCLUSION

The new classification system was found to be highly capable of subcategorizing BI-RADS category 4 MRI-only visible non-mass lesions into 4Fo and 4Bi.

Factors influencing the detection rate of fumarate peak in 1H MR spectroscopy of fumarate hydratase-deficient renal cell carcinoma at 3 T MRI

AIM

To identify factors that may be associated with fumarate detection rate in 1H-magnetic resonance spectroscopy (MRS) in fumarate hydratase-deficient renal cell carcinoma (FH-RCC).

MATERIALS AND MEHODS

Between February 2018 and March 2022, 16 FH-RCC patients with 30 lesions underwent 1H-MRS. Detection results were classified as having a detected fumarate peak (n=12), undetected peak (n=10), or technical failure (n=8). Factors including tumour size, tumour location, treatment history, and metastasis status were collected and analysed. A Bayesian logistic regression model was applied to evaluate the association between these factors and the detection result.

RESULTS

Bayesian analysis demonstrated significant associations between fumarate detection results and the following factors: long-axis diameter (odds ratio [OR] of 1.64; 95% confidence interval [CI] of 1.07-2.53), short-axis diameter (OR of 1.90; 95% CI of 1.19-3.06), voxel size (OR of 2.85; 95% CI of 1.70-4.75), treatment history (OR of 0.35; 95% CI of 0.21-0.58), non-metastatic state (OR of 2.45; 95% CI of 1.48-4.06), and lymph node metastasis (OR of 0.35; 95% CI of 0.21-0.58). Technical failure results were associated with factors such as treatment history (OR of 2.59; 95% CI of 1.37-4.66), non-metastatic state (OR of 0.36; 95% CI of 0.19-0.66), and lymph node metastasis (OR of 2.61; 95% CI of 1.39-4.74).

CONCLUSION

Tumour size, treatment history, and metastasis character were associated with the detection of abnormal fumarate accumulation. This finding will serve as a reference for interpreting 1H-MRS results and for selecting suitable scenarios to evaluate FH-RCC.

Diagnostic accuracy of diffusion weighted imaging compared to magnetic resonance spectroscopy in differentiation of benign and malignant breast lesions: A systematic review and meta-analysis

OBJECTIVE

To compare the sensitivity and specificity of diffusion weighted imaging (DWI) and magnetic resonance spectroscopy (MRS) in the differentiation of benign and malignant breast lesions.

METHODS

Scopus, PubMed, and other registries were searched up to April 2023. We included diagnostic studies with DWI and MRS as index tests and histopathologic examination as the reference standard for differentiating benign and malignant breast lesions in adult females. We excluded studies involving healthy women, only breast cancer patients, and non-comparative diagnostic accuracy studies on either index test. The sensitivity and specificity of DWI and MRS were investigated and pooled using random-effect bivariate meta-analysis. Risk of bias was assessed using QUADAS-2. Evidence quality was summarized using GRADE.

RESULTS

Eight eligible studies involving 632 females and 687 breast lesions were identified. The pooled sensitivity and specificity of DWI were 92% (CI 85-96%) and 88% (CI 75-94%), respectively. The pooled sensitivity and specificity of MRS were 85% (CI 66-94%) and 85% (CI 77-91%), respectively. No significant difference was noted in the sensitivity (7%, CI -8-22%) and specificity (3%, CI -9-14%) between DWI and MRS.

CONCLUSIONS

In low to moderate quality evidence, DWI and MRS show comparable sensitivity and specificity in differentiating benign and malignant breast lesions.

Probing lipids relaxation times in breast cancer using magnetic resonance spectroscopic fingerprinting

OBJECTIVES

To investigate the clinical relevance of the relaxation times of lipids within breast cancer and normal fibroglandular tissue in vivo, using magnetic resonance spectroscopic fingerprinting (MRSF).

METHODS

Twelve patients with biopsy-confirmed breast cancer and 14 healthy controls were prospectively scanned at 3 T using a protocol consisting of diffusion tensor imaging (DTI), MRSF, and dynamic contrast-enhanced (DCE) MRI. Single-voxel MRSF data was recorded from the tumor (patients) - identified using DTI - or normal fibroglandular tissue (controls), in under 20 s. MRSF data was analyzed using in-house software. Linear mixed model analysis was used to compare the relaxation times of lipids in breast cancer VOIs vs. normal fibroglandular tissue.

RESULTS

Seven distinguished lipid metabolite peaks were identified and their relaxation times were recorded. Of them, several exhibited statistically significant changes between controls and patients, with strong significance (p < 10^-3) recorded for several of the lipid resonances at 1.3 ppm (T₁ = 355 ± 17 ms vs. 389 ± 27 ms), 4.1 ppm (T₁ = 255 ± 86 ms vs. 127 ± 33 ms), 5.22 ppm (T₁ = 724 ± 81 ms vs. 516 ± 62 ms), and 5.31 ppm (T₂ = 56 ± 5 ms vs. 44 ± 3.5 ms, respectively).

CONCLUSIONS

The application of MRSF to breast cancer imaging is feasible and achievable in clinically relevant scan time. Further studies are required to verify and comprehend the underling biological mechanism behind the differences in lipid relaxation times in cancer and normal fibroglandular tissue.

KEY POINTS

•The relaxation times of lipids in breast tissue are potential markers for quantitative characterization of the normal fibroglandular tissue and cancer. •Lipid relaxation times can be acquired rapidly in a clinically relevant manner using a single-voxel technique, termed MRSF. •Relaxation times of T₁ at 1.3 ppm, 4.1 ppm, and 5.22 ppm, as well as of T₂ at 5.31 ppm, were significantly different between measurements within breast cancer and the normal fibroglandular tissue.

Whole-lesion histogram analysis of apparent diffusion coefficient for the assessment of non-mass enhancement lesions on breast MRI

Objectives

To investigate the application of apparent diffusion coefficient (ADC) histogram analysis in differentiating between benign and malignant breast lesions detected as non-mass enhancement on MRI.

Materials and Methods

A retrospective study was conducted for 25 malignant and 26 benign breast lesions showing non-mass enhancement on breast MRI. An experienced radiologist without prior knowledge of the pathological results drew a region of interest (ROI) outlining the periphery of each lesion on the ADC map. A histogram was then made for each lesion. Following a univariate analysis of 18 summary statistics values, we conducted statistical discrimination after hierarchical clustering using Ward’s method. A comparison between the malignant and the benign groups was made using multiple logistic regression analysis and the Mann-Whitney U test. A P -value of less than 0.05 was considered statistically significant.

Results

Univariate analysis for the 18 summary statistics values showed the malignant group had greater entropy (P < 0.001) and lower uniformity (P < 0.001). While there was no significant difference in mean and skewness values, the malignant group tended to show a lower mean (P = 0.090) and a higher skewness (P = 0.065). Hierarchical clustering of the 18 summary statistics values identified four values (10th percentile, entropy, skewness, and uniformity) of which the 10th percentile values were significantly lower for the malignant group (P = 0.035).

Conclusions

Whole-lesion ADC histogram analysis may be useful for differentiating malignant from benign lesions which show non-mass enhancement on breast MRI.

Papillary Lesions of the Breast: Addition of DWI and TIRM Sequences to Routine Breast MRI Could Help in Differentiation Benign from Malignant

AIM

We aimed to investigate the magnetic resonance imaging (MRI) features of benign, atypical or malignant papillary breast lesions and to assess additional value of diffusion-weighted imaging (DWI) and turbo inversion recovery magnitude (TIRM) sequences to routine breast MRI.

BACKGROUND

Differentiation between benign and malignant papillary breast lesions is essential for patient management. However, morphologic features and enhancement patterns of malignant papillary lesions may overlap with those of benign papilloma.

METHODS

Seventy two papillary breast lesions (50 benign, 22 atypical or malignant) were included in the current study, retrospectively. We divided the patients into two groups as benign papillary breast lesions and atypical or malignant papillary breast lesions. Morphologic, dynamic, turbo inversion recovery magnitude (TIRM) values and diffusion features of the papillary lesions were compared between two groups.

RESULTS

Benign papillary lesions were smaller in size (p=0.006 and p=0.005, for radiologist 1 and 2 respectively), closer to areola (p=0.045 and 0.049 for radiologist 1 and 2 respectively) and had higher ADC values (p=0.001 for two radiologists) than atypical or malignant group. ROC curves showed diagnostic accuracy for ADC (AUC=0.770 and 0.762, p<0.0001 for two radiologists) and showed a cut-off value of ≤957 x 10-6 mm2/s (radiologist 1) and ≤ 910 x 10-6 mm2/s (radiologist 2).

CONCLUSION

MRI is a useful method for differentiation between benign and malignant papillary breast lesions. Centrally located, lesser in size and higher ADC values should be considered benign, whereas peripherally located, larger in size and lower ADC values should be considered malignant.

Apparent Diffusion Coefficient-Based Convolutional Neural Network Model Can Be Better Than Sole Diffusion-Weighted Magnetic Resonance Imaging to Improve the Differentiation of Invasive Breast Cancer From Breast Ductal Carcinoma In Situ

BACKGROUND AND PURPOSE

Breast ductal carcinoma in situ (DCIS) has no metastatic potential, and has better clinical outcomes compared with invasive breast cancer (IBC). Convolutional neural networks (CNNs) can adaptively extract features and may achieve higher efficiency in apparent diffusion coefficient (ADC)-based tumor invasion assessment. This study aimed to determine the feasibility of constructing an ADC-based CNN model to discriminate DCIS from IBC.

METHODS

The study retrospectively enrolled 700 patients with primary breast cancer between March 2006 and June 2019 from our hospital, and randomly selected 560 patients as the training and validation sets (ratio of 3 to 1), and 140 patients as the internal test set. An independent external test set of 102 patients during July 2019 and May 2021 from a different scanner of our hospital was selected as the primary cohort using the same criteria. In each set, the status of tumor invasion was confirmed by pathologic examination. The CNN model was constructed to discriminate DCIS from IBC using the training and validation sets. The CNN model was evaluated using the internal and external tests, and compared with the discriminating performance using the mean ADC. The area under the curve (AUC), sensitivity, specificity, and accuracy were calculated to evaluate the performance of the previous model.

RESULTS

The AUCs of the ADC-based CNN model using the internal and external test sets were larger than those of the mean ADC (AUC: 0.977 vs. 0.866, P = 0.001; and 0.926 vs. 0.845, P = 0.096, respectively). Regarding the internal test set and external test set, the ADC-based CNN model yielded sensitivities of 0.893 and 0.873, specificities of 0.929 and 0.894, and accuracies of 0.907 and 0.902, respectively. Regarding the two test sets, the mean ADC showed sensitivities of 0.845 and 0.818, specificities of 0.821 and 0.829, and accuracies of 0.836 and 0.824, respectively. Using the ADC-based CNN model, the prediction only takes approximately one second for a single lesion.

CONCLUSION

The ADC-based CNN model can improve the differentiation of IBC from DCIS with higher accuracy and less time.

Diagnostic Performance of Proton Magnetic Resonance Spectroscopy and 18F-Fluorocholine PET to Differentiate Benign From Malignant Breast Lesions

PURPOSE

The aim of this study was to evaluate the diagnostic performance of the proton magnetic resonance spectroscopy (MRS) and 18F-fluorocholine (FCH) PET for suspicious breast findings on conventional imaging (mammography and breast ultrasound).

METHODS

From September 2012 to December 2015, 37 women with 39 breast lesions on conventional imaging were enrolled and underwent proton MRS and FCH PET. The MRS parameters of choline signal-to-noise ratio (SNR), choline integral (I(cho)), and the PET parameters including SUVmax in the prone (SUV1) and supine (SUV2) positions were analyzed. Receiver operating characteristic curves with the area under the curve, sensitivity, and specificity under the optimal cutoff points for the different parameters were determined.

RESULTS

Twenty-three lesions (59%) were malignant, and 16 (41.0%) were benign. The malignant lesions tended to show significantly higher MRS and PET parameters than benign lesions (choline SNR, P = 0.007; I(cho), P = 0.003; SUV1 and SUV2, P < 0.0001). Fair to moderate correlations were noted between the choline SNR and PET parameters (SUV1, Spearman rank correlation coefficient, ρ = 0.477; SUV2, ρ = 0.483), as well as I(cho) and PET parameters (SUV1, ρ = 0.493; SUV2, ρ = 0.549). The SUV2 showed the highest diagnostic performance (area under the curve, 0.918). Using 2.5 as the optimal cutoff point, the SUV2 yields 89.5% sensitivity and 87.5% specificity for differentiating malignant from benign lesions.

CONCLUSION

The MRS parameters were fairly to moderately correlated with FCH PET parameters, and both could differentiate malignant from benign breast lesions with SUV2 showing best diagnostic performance.

Low-Field NMR Relaxometry for Intraoperative Tumour Margin Assessment in Breast-Conserving Surgery

Simple Summary

Breast cancer is the most diagnosed cancer for women, and clear surgical margins in breast-conserving surgery (BCS) are essential for preventing recurrence. In this study, the potential of fast field-cycling ¹H-NMR relaxometry as a new tool for intraoperative margin assessment was evaluated. The technique allows the determination of the tissue proton relaxation rates as a function of the applied magnetic field on small tissue samples excised from surgical specimens, at the margins of tumour resection, prior to histopathological analysis. It was found that a good accuracy in margin assessment, i.e., a sensitivity of 92% and a specificity of 85%, can be achieved. The discriminating ability shown by the relaxometric assay relies mainly on the difference of fat/water content between healthy and tumour cells. The information obtained has the potential to support the surgeon in real-time margin assessment during BCS.

Abstract

As conserving surgery is routinely applied for the treatment of early-stage breast cancer, the need for new technology to improve intraoperative margin assessment has become increasingly important. In this study, the potential of fast field-cycling ¹H-NMR relaxometry as a new diagnostic tool was evaluated. The technique allows the determination of the tissue proton relaxation rates (R₁), as a function of the applied magnetic field, which are affected by the changes in the composition of the mammary gland tissue occurring during the development of neoplasia. The study involved 104 small tissue samples obtained from surgical specimens destined for histopathology. It was found that a good accuracy in margin assessment, i.e., a sensitivity of 92% and a specificity of 85%, can be achieved by using two quantifiers, namely (i) the slope of the line joining the R₁ values measured at 0.02 and 1 MHz and (ii) the sum of the R₁ values measured at 0.39 and 1 MHz. The method is fast, and it does not rely on the expertise of a pathologist or cytologist. The obtained results suggest that a simplified, low-cost, automated instrument might compete well with the currently available tools in margin assessment.

Combined Use of Texture Features and Morphological Classification Based on Dynamic Contrast-enhanced MR Imaging: Differentiating Benign and Malignant Breast Masses with High Negative Predictive Value

Purpose: We evaluated the diagnostic performance of the texture features of dynamic contrast-enhanced (DCE) MRI for breast cancer diagnosis in which the discriminator was optimized, so that the specificity was maximized via the restriction of the negative predictive value (NPV) to greater than 98%.

Methods: Histologically proven benign and malignant mass lesions of DCE MRI were enrolled retrospectively. Training and testing sets consist of 166 masses (49 benign, 117 malignant) and 50 masses (15 benign, 35 malignant), respectively. Lesions were classified via MRI review by a radiologist into 4 shape types: smooth (S-type, 34 masses in training set and 8 masses in testing set), irregular without rim-enhancement (I-type, 60 in training and 14 in testing), irregular with rim-enhancement (R-type, 56 in training and 22 in testing), and spicula (16 in training and 6 in testing). Spicula were immediately classified as malignant. For the remaining masses, 298 texture features were calculated using a parametric map of DCE MRI in 3D mass regions. Masses were classified into malignant or benign using two thresholds on a feature pair. On the training set, several feature pairs and their thresholds were selected and optimized for each mass shape type to maximize specificity with the restriction of NPV > 98%. NPV and specificity were computed using the testing set by comparison with histopathologic results and averaged on the selected feature pairs.

Results: In the training set, 27, 12, and 15 texture feature pairs are selected for S-type, I-type, and R-type masses, respectively, and thresholds are determined. In the testing set, average NPV and specificity using the selected texture features were 99.0% and 45.2%, respectively, compared to the NPV (85.7%) and specificity (40.0%) in visually assessed MRI category-based diagnosis.

Conclusion: We, therefore, suggest that the NPV of our texture-based features method described performs similarly to or greater than the NPV of the MRI category-based diagnosis.

[Influences of Tumor Volume and FWHM of the Water Peak and T2* Value of Water on the Detection Rate of the Choline Peaks in Proton MR Spectroscopy of Breast Cancer at 3.0 T-MRI]

In proton magnetic resonance (MR) spectroscopy (¹H-MRS) of the breast cancer, choline peak could be detected. The purpose of this study was to evaluate the influences of the tumor volume, full width at half maximum (FWHM) of the water peak (FWHM), and T₂* value of water (T₂* value) on the detection rate of the choline peaks at 3.0 T-MRI. We measured FWHM and T₂* value in 109 cases, and we evaluated the effect of tumor volume on the detection rate of the choline peaks and the effect of FWHM and T₂* value on the detection of choline peaks. In ¹H-MRS of breast cancer at 3.0 T-MRI, the detection rate of the choline peaks improved as the tumor volume was larger. As a shimming environment when acquiring ¹H-MRS of breast cancer, FWHM is preferably 57.4 Hz or less and T₂* value should be 11 ms or more, and T₂* value has a great influence on the detection rate of the choline peaks.

Enhanced Masses on Contrast-Enhanced Breast: Differentiation Using a Combination of Dynamic Contrast-Enhanced MRI and Quantitative Evaluation with Synthetic MRI

BACKGROUND

The addition of synthetic MRI might improve the diagnostic performance of dynamic contrast-enhanced MRI (DCE-MRI) in patients with breast cancer.

PURPOSE

To evaluate the diagnostic value of a combination of DCE-MRI and quantitative evaluation using synthetic MRI for differentiation between benign and malignant breast masses.

STUDY TYPE

Retrospective, observational.

POPULATION

In all, 121 patients with 131 breast masses who underwent DCE-MRI with additional synthetic MRI were enrolled.

FIELD STRENGTH/SEQUENCE

3.0 Tesla, T₁ -weighted DCE-MRI and synthetic MRI acquired by a multiple-dynamic, multiple-echo sequence.

ASSESSMENT

All lesions were differentiated as benign or malignant using the following three diagnostic methods: DCE-MRI type based on the Breast Imaging-Reporting and Data System; synthetic MRI type using quantitative evaluation values calculated by synthetic MRI; and a combination of the DCE-MRI + Synthetic MRI types. The diagnostic performance of the three methods were compared.

STATISTICAL TESTS

Univariate (Mann-Whitney U-test) and multivariate (binomial logistic regression) analyses were performed, followed by receiver-operating characteristic curve (AUC) analysis.

RESULTS

Univariate and multivariate analyses showed that the mean T₁ relaxation time in a breast mass obtained by synthetic MRI prior to injection of contrast agent (pre-T₁ ) was the only significant quantitative value acquired by synthetic MRI that could independently differentiate between malignant and benign breast masses. The AUC for all enrolled breast masses assessed by DCE-MRI + Synthetic MRI type (0.83) was significantly greater than that for the DCE-MRI type (0.70, P < 0.05) or synthetic MRI type (0.73, P < 0.05). The AUC for category 4 masses assessed by the DCE-MRI + Synthetic MRI type was significantly greater than that for those assessed by the DCE-MRI type (0.74 vs. 0.50, P < 0.05).

DATA CONCLUSION

A combination of synthetic MRI and DCE-MRI improves the accuracy of diagnosis of benign and malignant breast masses, especially category 4 masses. Level of Evidence 4 Technical Efficacy Stage 2 J. MAGN. RESON. IMAGING 2021;53:381-391.

Current status of breast cancer screening in high-risk women in Japan

Overseas, the importance for breast MRI screening for high-risk groups has been shown. However, the evidence among Japanese population was lacking. Therefore, we collaborated with the "Study on clinical and genetic characterization of hereditary breast and ovarian cancer and improvement in prognosis using genetic information in Japan" group, as part of the Comprehensive Research Project on the Promotion of Cancer Control, Health and Labour Sciences Research, and have been conducting the study entitled, "Study of the usefulness of MRI surveillance of BRCA1/2 mutation carriers" since 2014. In addition, we found that in the Japanese population also, the pathological and imaging characteristics differ between BRCA1 and BRCA2 mutation carriers, like in non-Japanese populations by the several reports. In high-risk females, risk categories such as BRCA1 or BRCA2 mutation carriers are very important. Furthermore, in the future, the optimal surveillance modalities and examination intervals would also vary according to the age, thinness of the breast (constitution), breast density (individual differences on mammography), etc.; this would be "personalized surveillance", and quality-assured MRI examination is of the essence. This review will present clinical trial data of prospective MRI surveillance in Japan, and summarize the current status of breast cancer screening in high-risk Japanese women.

In vivo MR spectroscopy for breast cancer diagnosis

Breast cancer is a significant health concern in females, worldwide. In vivo proton (1H) MR spectroscopy (MRS) has evolved as a non-invasive tool for diagnosis and for biochemical characterization of breast cancer. Water-to-fat ratio, fat and water fractions and choline containing compounds (tCho) have been identified as diagnostic biomarkers of malignancy. Detection of tCho in normal breast tissue of volunteers and in lactating females limits the use of tCho as a diagnostic marker. Technological developments like high-field scanners, multi channel coils, pulse sequences with water and fat suppression facilitated easy detection of tCho. Also, quantification of tCho and its cut-off for objective assessment of malignancy have been reported. Meta-analysis of in vivo 1H MRS studies have documented the pooled sensitivities and the specificities in the range of 71-74% and 78-88%, respectively. Inclusion of MRS has been shown to enhance the diagnostic specificity of MRI, however, detection of tCho in small sized lesions (≤1 cm) is challenging even at high magnetic fields. Potential of MRS in monitoring the effect of chemotherapy in breast cancer has also been reported. This review briefly presents the potential clinical role of in vivo 1H MRS in the diagnosis of breast cancer, its current status and future developments.

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.

Role of diffusion weighted imaging and magnetic resonance spectroscopy in breast cancer patients with indeterminate dynamic contrast enhanced magnetic resonance imaging findings

PURPOSE

Dynamic contrast enhanced MRI (DCEMRI), diffusion weighted imaging (DWI) and in vivo proton (¹H) magnetic resonance spectroscopy (MRS) provides functional and molecular nature of breast cancer. This study evaluates the potential of the combination of three MR parameters [curve kinetics, apparent diffusion coefficient (ADC) and total choline (tCho) concentration] determined from these techniques in increasing the sensitivity of breast cancer detection.

METHODS

MR investigations were carried out at 1.5 T on 56 patients with cytologically/histologically confirmed breast carcinoma. Single-voxel MRS was used to determine the tCho concentration. 3D FLASH was used for DCEMRI while single shot EPI based DWI was used for ADC determination.

RESULTS

On DCEMRI, one patient showed type I curve, while 8 showed type II and 47 showed type III curve thus giving a sensitivity of 83.9% as detection rate of malignancy. tCho concentration was above cut-off value (2.54 mmol/kg) for 50/56 cases giving a sensitivity of 89.3%. Among 9 indeterminate DCEMRI cases, tCho showed malignancy in 6 cases with type II curve. DWI detected malignancy in 54/56 cases that included 9 cases that were false negative on DCEMRI, yielding a sensitivity of 96.4%. A total of 54 cases showed malignancy when any two of the three MR parameters was positive for malignancy yielding a sensitivity of 96.4% while it increased to 100% when any one parameters showed positive result.

CONCLUSION

DWI showed highest sensitivity of detection compared to DCEMRI and MRS. Multi-parametric approach yielded 96.4% and 100% sensitivity when any two or one of the three parameters was taken as positive for malignancy, respectively. Also the results demonstrated that addition of DWI and MRS play a significant role in establishing the final diagnosis of malignancy, especially in cases where DCEMRI is indeterminate.

Proton MR spectroscopy in the breast: Technical innovations and clinical applications

Proton magnetic resonance spectroscopy (MRS) is a promising noninvasive diagnostic technique for investigation of breast cancer metabolism. Spectroscopic imaging data may be obtained following contrast-enhanced MRI by applying the point-resolved spectroscopy sequence (PRESS) or the stimulated echo acquisition mode (STEAM) sequence from the MR voxel encompassing the breast lesion. Total choline signal (tCho) measured in vivo using either a qualitative or quantitative approach has been used as a diagnostic test in the workup of malignant breast lesions. In addition to tCho metabolites, other relevant metabolites, including multiple lipids, can be detected and monitored. MRS has been heavily investigated as an adjunct to morphologic and dynamic MRI to improve diagnostic accuracy in breast cancer, obviating unnecessary benign biopsies. Besides its use in the staging of breast cancer, other promising applications have been recently investigated, including the assessment of treatment response and therapy monitoring. This review provides guidance on spectroscopic acquisition and quantification methods and highlights current and evolving clinical applications of proton MRS. Level of Evidence 5 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2019.

Is there a correlation between 3T multiparametric MRI and molecular subtypes of breast cancer?

OBJECTIVES

To test whether 3 T multiparametric magnetic resonance imaging (mMRI) provides information related to molecular subtypes of breast cancer.

METHODS

Women with mammographic or US findings of breast lesions (BI-RADS 4-5) underwent 3 T mMRI (DCE, DWI and MR spectroscopy). The histological type of breast cancer was assessed. Estrogen-receptor (ER), progesterone-receptor (PgR), Ki-67 status and HER-2 expression, assessed by immunohistochemistry (IHC), defined four molecular subtypes: Luminal-A, Luminal-B, HER2-enriched and triple-negative. Non-parametric tests (Kruskal-Wallis, k-sample equality of medians, and Mann-Whitney), logistic regression or ANOVA, and a multivariate analysis were performed to investigate correlations between the four molecular subtypes and mMRI (lesion volume, margins or distribution, enhancement pattern, ADC, type of kinetic curve, and total choline (tCho) signal-to-noise-ratio (SNR)). A ROC analysis was finally performed to test the diagnostic power of a multivariate logistic regression model.

RESULTS

433 patients (453 lesions) were considered. Volume was smaller in Luminal-B and larger in triple-negative tumours compared to the other subtypes combined. Margins were significantly correlated to Luminal-A and Luminal-B. The type of curve was significantly correlated to Luminal-A. ADC values were higher in Luminal-A. tCho SNR was higher in triple-negative tumours. The ROC analysis showed that the area under the curve (AUC) significantly improved when multiple MRI features were used compared to individual parameters.

CONCLUSIONS

A significant correlation was found between some MRI features and molecular subtypes of breast tumours. A multiparametric approach improved the diagnostic power of MRI. However, further research is needed in order to predict the molecular subtype on the sole basis of mMRI.

Impact of Advancing Technology on Diagnosis and Treatment of Breast Cancer

New emerging breast imaging techniques have shown great promise in breast cancer screening, evaluation of extent of disease, and response to neoadjuvant therapy. Tomosynthesis, allows 3-dimensional imaging of the breast, and increases breast cancer detection. Fast abbreviated MRI has reduced time and costs associated with traditional breast MRI while maintaining cancer detection. Diffusion-weighted imaging is a functional MRI technique that does not require contrast and has shown potential in screening, lesion characterization and also evaluation of treatment response. New image-guided preoperative localizations are available that have increased patient satisfaction and decreased operating room delays.

Breast MRI as a Problem-solving Study in the Evaluation of BI-RADS Categories 3 and 4 Microcalcifications: Is it Worth Performing?

RATIONALE AND OBJECTIVES

We aimed to investigate the utility of problem-solving breast magnetic resonance imaging (MRI) for mammographic Breast Imaging Reporting and Data System (BI-RADS) categories 3 and 4 microcalcifications.

MATERIALS AND METHODS

Between January 1, 2010 and December 31, 2011, 138 women with 146 areas of categories 3 and 4 microcalcifications without sonographic correlates underwent breast MRI and had a stereotactic core biopsy using an 11-gauge needle or follow-up at least for 24 months. Positive predictive value (PPV), negative predictive value, sensitivity, and specificity were calculated on the basis of BI-RADS category, with categories 1-3 being considered benign and categories 4 and 5 being considered malignant.

RESULTS

Twenty-four cases (16.4%) were malignant (18 ductal carcinoma in situ, 6 invasive). MRI increased PPV and specificity from 43% to 68% and from 80% to 93% (P = .054 and .005) compared to mammography. Within 102 category 3 microcalcifications, 5 carcinomas were assessed correctly as category 4 by MRI. Within 44 category 4 microcalcifications, a correct diagnosis was made by MRI in 77% (34 of 44) as opposed to 43% (19 of 44) by mammography, and 80% (20 of 25) of unnecessary biopsies could have been avoided. Within the 24 carcinomas, 5 were negative at MRI. MRI-negative carcinomas have a significantly higher possibility of being low grade (ductal carcinoma in situ or invasive) (P = .0362).

CONCLUSIONS

Breast MRI has the potential to improve the diagnosis of category 3 or 4 microcalcifications and could alter indications for biopsy. Breast MRI could help predict the presence or absence of higher-grade carcinoma for category 3 or 4 microcalcifications.

Diffusion-Weighted Imaging for the Discrimination of Benign and Malignant Breast Masses; Utility of ADC and Relative ADC

Purpose:

To determine the contribution of apparent diffusion coefficient (ADC), and relative ADC (rADC) values to differentiate between benign and malignant breast masses.

Materials and Methods:

Magnetic resonance imaging (MRI) of the breast with diffusion-weighted imaging (DWI) of patients with benign or malignant breast masses diagnosed either by histopathological findings or by follow-up imaging were evaluated retrospectively. Histopathological analyses were performed for 71 lesions (80.7%) while the remaining were followed up every six months for one year. DWI was performed using b-values of 0 and 1000 sec/mm², and ADC and rADC were calculated and compared. A receiver operating characteristic (ROC) curve and Youden index were used to evaluate the parameter’s optimal threshold and diagnostic value. Statistical significance was set as p < 0.05.

Results:

Eighty-eight lesions from a total of 81 patients, aged between 16 and 73 (mean age 42 ± 11.3) years were obtained and evaluated. Pathological results of 34 (38.6%) out of 71 lesions were malignant and 37 lesions (42%) were benign. Seventeen (19.3%) lesions remained stable at one-year follow-up and were accepted as benign breast masses. Mean ADC values of benign and malignant lesions were 1.584 × 10^–3mm²/sec and 0.884 × 10^–3mm²/sec (p < 0.05), respectively. Sensitivity and specificity of ADC were 88% and 87%, respectively at a cut-off value of 1.04 × 10^–3mm²/sec. Mean rADC was 0.931 for benign lesions and 0.557 for malignant lesions (p < 0.05). Sensitivity and specificity were 82% and 83% at a cut-off value of 0.639. No prominent superiority of rADC over ADC is identified in the differentiation of breast masses.

Conclusion:

ADC and rADC values derived from DWI can be equally useful in clinical setting to differentiate benign from malignant breast masses.

Grading System to Categorize Breast MRI in BI-RADS 5th Edition: A Multivariate Study of Breast Mass Descriptors in Terms of Probability of Malignancy

OBJECTIVE

The purpose of this study is to analyze the association between the probability of malignancy and breast mass descriptors in the BI-RADS 5th edition and to devise criteria for grading mass lesions, including subcategorization of category 4 lesions with or without apparent diffusion coefficient (ADC) values.

MATERIALS AND METHODS

A total of 519 breast masses in 499 patients were selected. Breast MRI was performed with a 1.5-T MRI scanner using a 16-channel dedicated breast radiofrequency coil. Two radiologists determined the morphologic and kinetic features of the breast masses. Mean ADC values were measured on ADC maps by placing round ROIs that encircled the largest possible solid mass portions. An optimal ADC threshold was chosen to maximize the Youden index. Corresponding pathologic diagnoses were obtained by either biopsy or surgery.

RESULTS

A total of 472 masses were malignant. Multivariate model analysis showed that shape (irregular, p < 0.001), margin type (not circumscribed, p < 0.001), internal enhancement (rim enhancement and heterogeneous enhancement, p = 0.0001), and delayed phase (washout, p = 0.0003) were the significant explanatory variables. The 3-point scoring system for findings suspicious for malignancy and the proposed classification system for breast mass descriptors (with points for category designation ranging from 0 to > 4) were significant with respect to malignancy (p < 0.01). The inclusion of ADC values improved the positive predictive values for categories 3, 4A, and 4B.

CONCLUSION

The 3-point scoring system for findings suspicious for malignancy and the proposed classification system for breast mass descriptors would be valid as a categorization system. ADC values may be used to downgrade benign lesions in categories 3, 4A, and 4B.

Computed diffusion weighted imaging (cDWI) and voxelwise-computed diffusion weighted imaging (vcDWI) for oncologic liver imaging: A pilot study

Objective

Aim of the study was to evaluate the influence of the selection of measured b-values on the precision of cDWI in the upper abdomen as well as on the lesion contrast of PET-positive liver metastases in cDWI and vcDWI.

Methods

We performed a retrospective analysis of 10 patients (4 m, 63.5 ± 12.9 y/o) with PET-positive liver metastases examined in 3 T-PET/MRI with b = 100,600,800,1000 and 1500s/mm². cDWI (cb1000/cb1500) and vcDWI were computed based on following combinations: i) b = 100/600 s/mm², ii) b = 100/800 s/mm², iii) b = 100/1000s/mm², iv) b = 100/600/1000s/mm² v) all measured b-values. Mean signal intensity (SI) and standard deviation (SD) in the liver, spleen, kidney, bone marrow and in liver lesions were acquired. The coefficient of variation (CV = SD/SI), the differences of SI between measured and calculated high b-value images and the lesion contrast (SI lesion/liver) were computed.

Results

With increasing upper measured b-values, the CV in cDWI and vcDWI decreased (CV in the liver in cb1500: 0.42 with b100/600 s/mm² and 0.28 with b100/b1000s/mm²) while the differences of measured and calculated b-value images decreased (in the liver in cb1500: 30.7% with b = 100/600 s/mm², 19.7% with b100/b1000s/mm²). In diffusion-restricted lesions, lesion contrast was at least 1.6 in cb1000 and 1.4 in cb1500, respectively, with an upper measured b-value of b = 800 s/mm² and 2.1 for vcDWI with an upper measured b-value of b = 1000s/mm². Overall, the lesion contrast was superior in cb1500 and vcDWI compared to cb1000 (15% and 11%, respectively).

Conclusion

Measuring higher upper b-values seems to lead to more precise computed high b-value images and a decrease of CV. vcDWI provides a comparable lesion contrast to b = 1500s/mm² and offers additionally the reduction of T2 shine-through effects. For vcDWI, measuring b = 1000s/mm² as upper b-value seems to be necessary to guarantee good lesion visibility in the liver based on our preliminary results.

Grading system to categorize breast MRI using BI-RADS 5th edition: a statistical study of non-mass enhancement descriptors in terms of probability of malignancy

PURPOSE

To analyze the association of breast non-mass enhancement descriptors in the BI-RADS 5th edition with malignancy, and to establish a grading system and categorization of descriptors.

MATERIALS AND METHODS

This study was approved by our institutional review board. A total of 213 patients were enrolled. Breast MRI was performed with a 1.5-T MRI scanner using a 16-channel breast radiofrequency coil. Two radiologists determined internal enhancement and distribution of non-mass enhancement by consensus. Corresponding pathologic diagnoses were obtained by either biopsy or surgery. The probability of malignancy by descriptor was analyzed using Fisher's exact test and multivariate logistic regression analysis. The probability of malignancy by category was analyzed using Fisher's exact and multi-group comparison tests.

RESULTS

One hundred seventy-eight lesions were malignant. Multivariate model analysis showed that internal enhancement (homogeneous vs others, p < 0.001, heterogeneous and clumped vs clustered ring, p = 0.003) and distribution (focal and linear vs segmental, p < 0.001) were the significant explanatory variables. The descriptors were classified into three grades of suspicion, and the categorization (3, 4A, 4B, 4C, and 5) by sum-up grades showed an incremental increase in the probability of malignancy (p < 0.0001).

CONCLUSION

The three-grade criteria and categorization by sum-up grades of descriptors appear valid for non-mass enhancement.

Can diffusion-weighted imaging add information in the evaluation of breast lesions considered suspicious on magnetic resonance imaging?

OBJECTIVE

To assess the role of diffusion-weighted imaging (DWI) in the evaluation of breast lesions classified as suspicious on magnetic resonance imaging (MRI), correlating the findings with the results of the histological analysis.

MATERIALS AND METHODS

This was a retrospective, descriptive study based on a review of the medical records of 215 patients who were submitted to MRI with DWI before undergoing biopsy at a cancer center. Apparent diffusion coefficient (ADC) values were calculated for each lesion, and the result of the histological analysis was considered the gold standard.

RESULTS

The mean age was 49 years. We identified 252 lesions, 161 (63.9%) of which were found to be malignant in the histological analysis. The mean ADC value was higher for the benign lesions than for the malignant lesions (1.50 × 10-3 mm2/s vs. 0.97 × 10-3 mm2/s), the difference being statistically significant (p < 0.001). The ADC cut-off point with the greatest sensitivity and specificity on the receiver operating characteristic curve was 1.03 × 10-3 mm2/s. When the DWI and conventional MRI findings were combined, the accuracy reached 95.9%, with a sensitivity of 95.7% and a specificity of 96.4%.

CONCLUSION

The use of DWI could facilitate the characterization of breast lesions, especially those classified as BI-RADS 4, increasing the specificity and diagnostic accuracy of MRI.

Magnetic Resonance Spectroscopy and its Clinical Applications: A Review

In vivo NMR spectroscopy is known as magnetic resonance spectroscopy (MRS). MRS has been applied as both a research and a clinical tool in order to detect visible or nonvisible abnormalities. The adaptability of MRS allows a technique that can probe a wide variety of metabolic uses across different tissues. Although MRS is mostly applied for brain tissue, it can be used for detection, localization, staging, tumour aggressiveness evaluation, and tumour response assessment of breast, prostate, hepatic, and other cancers. In this article, the medical applications of MRS in the brain, including tumours, neural and psychiatric disorder studies, breast, prostate, hepatic, gastrointestinal, and genitourinary investigations have been reviewed.

Identification of Gastric Cancer Biomarkers Using 1H Nuclear Magnetic Resonance Spectrometry

Existing gastric cancer diagnosing methods were invasive, hence, a reliable non-invasive gastric cancer diagnosing method is needed. As a starting point, we used 1H NMR for identifying gastric cancer biomarkers using a panel of gastric cancer spheroids and normal gastric spheroids. We were able to identify 8 chemical shift biomarkers for gastric cancer spheroids. Our data suggests that the cancerous and non-cancerous spheroids significantly differ in the lipid composition and energy metabolism. These results encourage the translation of these biomarkers into in-vivo gastric cancer detection methodology using MRI-MS.

Potential of Diffusion-Weighted Imaging in the Characterization of Malignant, Benign, and Healthy Breast Tissues and Molecular Subtypes of Breast Cancer

The role of apparent diffusion coefficient (ADC) in the diagnosis of breast cancer and its association with molecular biomarkers was investigated in 259 patients with breast cancer, 67 with benign pathology, and 54 healthy volunteers using diffusion-weighted imaging (DWI) at 1.5 T. In 59 breast cancer patients, dynamic contrast-enhanced MRI (DCEMRI) was also acquired. Mean ADC of malignant lesions was significantly lower (1.02 ± 0.17 × 10⁻³ mm²/s) compared to benign (1.57 ± 0.26 × 10⁻³ mm²/s) and healthy (1.78 ± 0.13 × 10⁻³ mm²/s) breast tissues. A cutoff ADC value of 1.23 × 10⁻³ mm²/s (sensitivity 92.5%; specificity 91.1%; area under the curve 0.96) to differentiate malignant from benign diseases was arrived by receiver operating curve analysis. In 10/59 breast cancer patients, indeterminate DCE curve was seen, while their ADC value was indicative of malignancy, implying the potential of the addition of DWI in increasing the specificity of DCEMRI data. Further, the association of ADC with tumor volume, stage, hormonal receptors [estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor (HER2)], and menopausal status was investigated. A significant difference was seen in tumor volume between breast cancer patients of stages IIA and IIIA, IIB and IIIA, and IIB and III (B + C), respectively (P < 0.05). Patients with early breast cancer (n = 52) had significantly lower ADC and tumor volume than those with locally advanced breast cancer (n = 207). No association was found in ADC and tumor volume with the menopausal status. Breast cancers with ER−, PR−, and triple-negative (TN) status showed a significantly larger tumor volume compared to ER+, PR+, and non-triple-negative (nTN) cancers, respectively. Also, TN tumors showed a significantly higher ADC compared to ER+, PR+, and nTN cancers. Patients with ER− and TN cancers were younger than those with ER+ and nTN cancers. The present study demonstrated that ADC may increase the diagnostic specificity of DCEMRI and be useful for treatment management in clinical setting. Additionally, it provides an insight into characterization of molecular types of breast cancer and may serve as an indicator of metabolic reprograming underlying tumor proliferation.

Efficacy of single voxel 1H MR spectroscopic imaging at 3T for the differentiation of benign and malign breast lesions

PURPOSE

The aim of our study was to evaluate the effect of 1H Magnetic Resonance Spectroscopy (MRS) in differentiating breast lesions.

MATERIALS AND METHODS

Single voxel 1H Magnetic Resonance Spectroscopy (1H-MRS) was performed with 3T magnet in 45 women. The choline cut off point was set semi-quantitavely. Sensitivity, specificity and accuracy of MRS were calculated.

RESULTS

Twenty-four of 25 (96%) malignant and 9 of 26 (35%) benign lesions had choline peak. With the use cutoff value of 19,5 MRS provided a 96% sensitivity, 65% specificity and 80% accuracy.

CONCLUSION

MRS has a high diagnostic accuracy in differentiating breast lesions.

Morphology evaluation of nonmass enhancement on breast MRI: Effect of a three-step interpretation model for readers' performances and biopsy recommendations

OBJECTIVE

To evaluate and compare the use of a newly introduced interpretation model for breast nonmass enhancement (NME, defined as an area of enhancement without a three-dimensional, space-occupying lesion) with the use of the standard interpretation method based on BI-RADS.

MATERIALS AND METHODS

Two expert and two less-experienced breast imaging radiologists performed reading sessions of 86 malignant and 64 benign NME lesions twice. First, radiologists characterized NME using BI-RADS descriptors and assessed the likelihood of malignancy and need for a biopsy. Second, the likelihood of malignancy and need for a biopsy were assessed with the use of the model, in which three-step characterization of morphological features were performed: (1) selection of distribution modifiers, (2) homogeneous vs. heterogeneous internal enhancement (IE) pattern, and (3) evaluation of presence of "clumped", "clustered ring enhancement (CRE)", and "branching" IE signs. Multireader-multicase receiver operating characteristic analysis was used to evaluate observers' performances. Univariate and multivariate logistic regression analyses were performed for morphology descriptors.

RESULTS

With use of the model, average Az of less-experienced radiologists (0.77-0.83; p=0.013) and average sensitivity of all radiologists (96.2-98.2%; p=0.007) improved significantly. NPV also improved but nonsignificantly (81.1-91.9%; p=0.055). Multivariate analyses of the second reading showed branching, clumped, and CRE signs to be significant predictors of malignancy in the results of 3, 2, and 2 readers, respectively.

CONCLUSION

The three-step interpretation model for NME has the potential to improve less-experienced radiologists' performances, making them comparable to expert breast imagers.