Optimization of apparent diffusion coefficient measured by diffusion-weighted MRI for diagnosis of breast lesions presenting as mass and non-mass-like enhancement

Comparison of the diagnostic value of contrast-enhanced ultrasound combined with conventional ultrasound versus magnetic resonance imaging in malignant non-mass breast lesions

OBJECTIVE

To compare the diagnostic value of contrast-enhanced ultrasound (CEUS)+conventional ultrasound vs MRI for malignant non-mass breast lesions (NMLs).

METHODS

A total of 109 NMLs detected by conventional ultrasound and examined by both CEUS and MRI were retrospectively analysed. The characteristics of NMLs in CEUS and MRI were noted, and agreement between the two modalities was analysed. Sensitivity, specificity, positive-predictive value (PPV), negative-predictive value (NPV), and area under the curve (AUC) of the two methods for diagnosing malignant NMLs were calculated in the overall sample and subgroups of different sizes(<10 mm, 10-20 mm, >20 mm).

RESULTS

A total of 66 NMLs detected by conventional ultrasound showed non-mass enhancement in MRI. Agreement between ultrasound and MRI was 60.6%. Probability of malignancy was higher when there was agreement between the two modalities. In the overall group, the sensitivity, specificity, PPV, and NPV of the two methods were 91.3%, 71.4%, 60%, 93.4% and 100%, 50.4%, 59.7%, 100%, respectively. The diagnostic performance of CEUS+conventional ultrasound was better than that of MRI (AUC: 0.825 vs 0.762, p = 0.043). The specificity of both methods decreased as lesion size increased, but sensitivity did not change. There was no significant difference between the AUCs of the two methods in the size subgroups (p > 0.05).

CONCLUSION

The diagnostic performance of CEUS+conventional ultrasound may be better than that of MRI for NMLs detected by conventional ultrasound. However, the specificity of both methods decrease significantly as lesion size increases.

ADVANCES IN KNOWLEDGE

This is the first study to compare the diagnostic performance of CEUS+conventional ultrasound vs that of MRI for malignant NMLs detected by conventional ultrasound. While CEUS+conventional ultrasound appears to be superior to MRI, subgroup analysis suggests that diagnostic performance is poorer for larger NMLs.

Diffusion kurtosis imaging and intravoxel incoherent motion imaging parameters in breast lesions: Effect of radiologists' experience and region-of-interest selection

PURPOSE

To investigate the influence of ROI placement methods and radiologists' experience on diffusion kurtosis imaging (DKI) and intravoxel incoherent motion (IVIM) parameters' diagnostic performance in differentiating benign and malignant lesions based on the mass and non-mass enhancement (NME).

METHODS

We evaluated 138 lesions in 131 patients retrospectively. The IVIM and DKI parameter values were measured by three radiologists with different experiences independently using two different ROI placement methods. IVIM parameters include diffusion coefficient (ADCstand), true diffusion coefficient (ADCslow), pseudo-diffusion coefficient (ADCfast) and perfusion fraction (f). DKI parameters include mean diffusivity (MD) and mean kurtosis (MK). Each radiologist measured the lesions twice with a 3-month interval. We utilized intra-class correlation (ICC) to determine the inter- and intra-reader agreement for mass and NME, respectively. ROC analysis compared the diagnostic performance of parameters between different radiologists, ROI methods, and between mass and NME.

RESULTS

In mass lesions, inter- and intra-observer agreement were perfect for all parameters (ICC: 0.800-989). In NME, the inter-observer agreement was substantial to perfect for all parameters(ICC: 0.703-877), the intra-observer agreement of the senior and intermediate radiologists was substantial to perfect(ICC: 0.748-931) and the intra-observer agreement of the junior radiologist was moderate to substantial(ICC: 0.569-784). The diagnostic performance of ADCslow (Z = 2.209, P = 0.023), MD (mean diffusivity) (Z = 2.887, P = 0.004), and MK (mean kurtosis) (Z = 2.080, P = 0.038) in the small ROI measured by the senior radiologist was better than that of the junior radiologist for NME. The diagnostic performance of ADCslow in the large ROI measured by the senior radiologist (Z = 2.281, P = 0.023) and intermediate radiologist (Z = 2.867, P = 0.0041) was better than the junior radiologist for mass lesions. The diagnostic performance of ADCslow, ADCstand, MD, and MK did not show a significant difference between the two ROI placement methods (P > 0.05).

CONCLUSION

The observers' experience can influence the ROI selection and the diagnostic performance of ADCslow, ADCstand, MD, and MK measured using different methods show equal diagnostic performance.

Correlation of Shear-Wave Elastography and Apparent Diffusion Coefficient Values in Breast Cancer and Their Relationship with the Prognostic Factors

Background: Diffusion-weighted imaging and elastography are widely accepted methods in the evaluation of breast masses, however, there is very limited data comparing the two methods. The apparent diffusion coefficient is a measure of the diffusion of water molecules obtained by diffusion-weighted imaging as a part of breast MRI. Breast elastography is an adjunct to conventional ultrasonography, which provides a noninvasive evaluation of the stiffness of the lesion. Theoretically, increased tissue density and stiffness are related to each other. The purpose of this study is to compare MRI ADC values of the breast masses with quantitative elastography based on ultrasound shear wave measurements and to investigate their possible relation with the prognostic factors and molecular subtypes. Methods: We retrospectively evaluated histopathologically proven 147 breast lesions. The molecular classification of malignant lesions was made according to the prognostic factors. Shear wave elastography was measured in kiloPascal (kPa) units which is a quantitative measure of tissue stiffness. DWI was obtained using a 1.5-T MRI system. Results: ADC values were strongly inversely correlated with elasticity (r = −0.662, p < 0.01) according to Pearson Correlation. In our study, the cut-off value of ADC was 1.00 × 10−3 cm2/s to achieve a sensitivity of 84.6% and specificity of 75.4%, and the cut-off value of elasticity was 105.5 kPa to achieve the sensitivity of 96.3% and specificity 76.9% to discriminate between the malignant and benign breast lesions. The status of prognostic factors was not correlated with the ADC values and elasticity. Conclusions: Elasticity and ADC values are correlated. Both cannot predict the status of prognostic factors and differentiate between molecular subtypes.

Contribution of Diffusion-Weighted Imaging and ADC Values to Papillary Breast Lesions

This study aimed to evaluate the role of apparent diffusion coefficient (ADC) values obtained from diffusion-weighted imaging (DWI) in the differentiation of malignant from benign papillary breast lesions. The magnetic resonance imaging (MRI) data of 94 breast papillary lesions confirmed by pathology were retrospectively analyzed. The differences in ADC values of papillary lesions under different enhancements in MRI and different pathological types were investigated, and the ADC threshold was determined by the receiver operating characteristic curve for its potential diagnostic value. The mean ADC values in borderline and malignant lesions (1.01 ± 0.20 × 10-3 mm2/s) were significantly lower compared to benign lesions (1.21 ± 0.27 × 10-3 mm2/s) (P &lt; 0.05). The optimal threshold of the ADC value could be 1.00 × 10-3 mm2/s. The ADC values were statistically significant in differentiating between benign and malignant papillary lesions whether in mass or non-mass enhancement (P &lt; 0.05). However, there were no statistical differences in the ADC values among borderline or any other histological subtypes of malignant lesions (P &gt; 0.05). Measuring ADC values from DWI can be used to identify benign and malignant breast papillary lesions. The diagnostic performance of the ADC value in identifying benign and malignant breast lesions is not affected by the way of lesion enhancement. However, it shows no use for differential diagnosis among malignant lesion subtypes for now. The ADC value of 1.00 × 10-3 mm2/s can be used as the most appropriate threshold for distinguishing between benign and malignant breast papillary lesions.

The diagnostic dilemma with the plateau pattern of the time-intensity curve: can the relative apparent diffusion coefficient (rADC) optimise the ADC parameter for differentiating breast lesions?

AIM

To assess the performance of the apparent diffusion coefficient (ADC) and relative ADC (rADC) to differentiate benign from malignant breast lesions using the plateau pattern of the time-intensity curve (Type II TIC), including the impact of lesions-enhancement subtypes and menopausal status of patients.

MATERIALS AND METHODS

Between September 2016 and December 2019, 408 patients with 169 benign and 239 malignant lesions with Type II TIC underwent magnetic resonance imaging (MRI), including diffusion-weighted imaging, with b-values of 50 and 800 s/mm². ADC and rADC values were calculated by placing regions of interest (ROIs) on the lesion, the parenchyma of the normal breast, and the pectoralis major muscle. A receiver operating characteristic (ROC) curve was generated to compare the diagnostic performance of each parameter in distinguishing between benign and malignant breast lesions. Further classification was undertaken to study the discriminatory performance of each parameter in the different lesions enhancement subtypes (mass-like enhancement [MLE] and non-MLE [NMLE]) and menopausal status of patients (pre-menopausal and post-menopausal).

RESULTS

There was a significant difference in the ADC and rADC values between benign and malignant lesions. The sensitivities of lesion ADC, gland rADC, and muscle rADC were 79.29%, 77.51%, and 79.29%, respectively, with specificities of 94.56%, 82.01%, and 94.98%, respectively. The area under the ROC curve (AUC) of muscle rADC was the highest (AUC=0.92), especially in the MLE subtype (AUC=0.96), and was not affected by the menopausal status.

CONCLUSION

Muscle rADC and lesion ADC assessment improved the diagnostic performance of breast MRI in distinguishing between benign and malignant breast lesions with Type II TIC, especially muscle rADC in the MLE subtype.

Distinguishing between benign and malignant breast lesions using diffusion weighted imaging and intravoxel incoherent motion: A systematic review and meta-analysis

PURPOSE

We sought to evaluate the diagnostic performance of diffusion weighted imaging (DWI) and intravoxel incoherent motion (IVIM) for distinguishing between benign and malignant breast tumors by performing a meta-analysis.

METHODS

We comprehensively searched the electronic databases PubMed and Embase from January 2000 to April 2020 for studies in English. Studies were included if they reported the sensitivity and specificity for identifying benign and malignant breast lesions using DWI or IVIM. Studies were reviewed according to QUADAS-2. The data inhomogeneity and publication bias were also assessed. In order to explore the influence of different field strengths and different b values on diagnostic efficiency, we conducted subgroup analysis.

RESULTS

We analyzed 79 studies, which included a total of 6294 patients with 4091 malignant lesions and 2793 benign lesions. Overall, the pooled sensitivity and specificity of ADC for detecting malignant breast tumors were 0.87 (0.86-0.88) and 0.80 (0.78-0.81), respectively. The PLR was 5.09 (4.16-6.24); the NLR was 0.15 (0.13-0.18); and the DOR was 38.95 (28.87-52.54). The AUC value was 0.9297. The highest performing parameter for IVIM was tissue diffusivity (D), and the pooled sensitivity and specificity was 0.85 (0.82-0.88) and 0.87(0.83-0.90), respectively; the PLR was 5.65 (3.91-8.18); the NLR was 0.17 (0.12-0.26); and the DOR was 38.44 (23.57-62.69). The AUC value was 0.9265. Most of parameters demonstrated considerable statistically significant heterogeneity (P < 0.05, I²>50 %) except the pooled DOR, PLR of D and the pooled DOR and NLR of D*.

CONCLUSIONS

Our meta-analysis indicated that DWI and IVIM had high sensitivity and specificity in the differential diagnosis of breast lesions; and compared with DWI, IVIM could not further increase the diagnostic performance. There was no significant difference in diagnostic accuracy.

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.

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.

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.

Morphological MR features and quantitative ADC evaluation in invasive breast cancer: Correlation with prognostic factors

OBJECTIVE

Assess the correlation between MRI characteristics of invasive breast cancer and tumor prognostic features.

MATERIALS AND METHODS

95 women with invasive breast cancer underwent pre-treatment MR. Morphological findings and quantitative ADC were retrospectively evaluated.

RESULTS

Smaller size, round shape, spiculated margins and homogeneous internal enhancement pattern on dynamic MRI were independently associated with established predictors of good prognosis, while larger size and rim enhancement pattern were related to predictors of poor prognosis. A positive correlation was observed between ADC value and clinical stage.

CONCLUSIONS

MRI may be a useful tool for breast cancer aggressiveness prediction and for guiding subsequent clinical-therapeutic management.

Histogram analysis of volume-based apparent diffusion coefficient in breast cancer

Background Breast cancer is a heterogeneous disease. Recent studies showed that apparent diffusion coefficient (ADC) values have various association with tumor aggressiveness and prognosis. Purpose To evaluate the value of histogram analysis of ADC values obtained from the whole tumor volume in invasive ductal cancer (IDC) and ductal carcinoma in situ (DCIS). Material and Methods This retrospective study included 201 patients with confirmed DCIS (n = 37) and IDC (n = 164). The IDC group was divided into two groups based on the presence of a DCIS component: IDC-DCIS (n = 76) and pure IDC (n = 88). All patients underwent preoperative breast magnetic resonance imaging (MRI) with diffusion-weighted images at 3.0 T. Histogram parameters of cumulative ADC values, skewness, and kurtosis were calculated and statistically analyzed. Results The differences between DCIS, IDC-DCIS, and pure IDC were significant in all percentiles of ADC values, in descending order of DCIS, IDC-DCIS, and pure IDC. IDC showed significantly lower ADC values than DCIS, and ADC50 was the best indicator for discriminating IDC from DCIS, with a threshold of 1.185 × 10^-3 mm²/s (sensitivity of 82.9%, specificity of 75.7%). However, multivariate analysis of obtained ADC values showed no significant differences between DCIS, IDC-DCIS, and pure IDC ( P > 0.05). Conclusion Volume-based ADC values showed association with heterogeneity of breast cancer. However, there was no additional diagnostic performance in histogram analysis for differentiating between DCIS, IDC-DCIS, and pure IDC.

Quantitative DWI implemented after DCE-MRI yields increased specificity for BI-RADS 3 and 4 breast lesions

PURPOSE

To assess if specificity can be increased when semiautomated breast lesion analysis of quantitative diffusion-weighted imaging (DWI) is implemented after dynamic contrast-enhanced (DCE-) magnetic resonance imaging (MRI) in the workup of BI-RADS 3 and 4 breast lesions larger than 1 cm.

MATERIALS AND METHODS

In all, 120 consecutive patients (mean-age, 48 years; age range, 23-75 years) with 139 breast lesions (≥1 cm) were examined (2010-2014) with 1.5T DCE-MRI and DWI (b = 0, 50, 200, 500, 800, 1000 s/mm² ) and the BI-RADS classification and histopathology were obtained. For each lesion malignancy was excluded using voxelwise semiautomated breast lesion analysis based on previously defined thresholds for the apparent diffusion coefficient (ADC) and the three intravoxel incoherent motion (IVIM) parameters: molecular diffusion (D_slow ), microperfusion (D_fast ), and the fraction of D_fast (f_fast ). The sensitivity (Se), specificity (Sp), and negative predictive value (NPV) based on only IVIM parameters combined in parallel (D_slow , D_fast , and f_fast ), or the ADC or the BI-RADS classification by DCE-MRI were compared. Subsequently, the Se, Sp, and NPV of the combination of the BI-RADS classification by DCE-MRI followed by the IVIM parameters in parallel (or the ADC) were compared.

RESULTS

In all, 23 of 139 breast lesions were benign. Se and Sp of DCE-MRI was 100% and 30.4% (NPV = 100%). Se and Sp of IVIM parameters in parallel were 92.2% and 52.2% (NPV = 57.1%) and for the ADC 95.7% and 17.4%, respectively (NPV = 44.4%). In all, 26 of 139 lesions were classified as BI-RADS 3 (n = 7) or BI-RADS 4 (n = 19). DCE-MRI combined with ADC (Se = 99.1%, Sp = 34.8%) or IVIM (Se = 99.1%, Sp = 56.5%) did significantly improve (P = 0.016) Sp of DCE-MRI alone for workup of BI-RADS 3 and 4 lesions (NPV = 92.9%).

CONCLUSION

Quantitative DWI has a lower NPV compared to DCE-MRI for evaluation of breast lesions and may therefore not be able to replace DCE-MRI; when implemented after DCE-MRI as problem solver for BI-RADS 3 and 4 lesions, the combined specificity improves significantly. J. Magn. Reson. Imaging 2016;44:1642-1649.

Clinical Intravoxel Incoherent Motion and Diffusion MR Imaging: Past, Present, and Future

The concept of diffusion magnetic resonance (MR) imaging emerged in the mid-1980s, together with the first images of water diffusion in the human brain, as a way to probe tissue structure at a microscopic scale, although the images were acquired at a millimetric scale. Since then, diffusion MR imaging has become a pillar of modern clinical imaging. Diffusion MR imaging has mainly been used to investigate neurologic disorders. A dramatic application of diffusion MR imaging has been acute brain ischemia, providing patients with the opportunity to receive suitable treatment at a stage when brain tissue might still be salvageable, thus avoiding terrible handicaps. On the other hand, it was found that water diffusion is anisotropic in white matter, because axon membranes limit molecular movement perpendicularly to the nerve fibers. This feature can be exploited to produce stunning maps of the orientation in space of the white matter tracts and brain connections in just a few minutes. Diffusion MR imaging is now also rapidly expanding in oncology, for the detection of malignant lesions and metastases, as well as monitoring. Water diffusion is usually largely decreased in malignant tissues, and body diffusion MR imaging, which does not require any tracer injection, is rapidly becoming a modality of choice to detect, characterize, or even stage malignant lesions, especially for breast or prostate cancer. After a brief summary of the key methodological concepts beyond diffusion MR imaging, this article will give a review of the clinical literature, mainly focusing on current outstanding issues, followed by some innovative proposals for future improvements.

Evaluation of Apparent Diffusion Coefficient to Predict Grade, Microinvasion, and Invasion in Ductal Carcinoma In Situ of the Breast

RATIONALE AND OBJECTIVES

To evaluate the role of apparent diffusion coefficient (ADC) in distinguishing ductal carcinoma in situ (DCIS) grades and identifying microinvasive and/or invasive disease in the preoperative evaluation of patients with core biopsy-proven DCIS.

MATERIALS AND METHODS

Research Ethics Board-approved study with informed consent from 81 women (age, 36-84 years) scheduled for core-biopsy with results of 82 noninvasive breast carcinomas. All patients were assessed preoperatively by diffusion sequence in addition to contrast magnetic resonance imaging (MRI). Lesion morphology and ADC values were recorded. The Kruskal-Wallis or one-way analysis of variance test and Pearson correlation coefficient were used to study the association between ADC and MRI lesion characteristics. Logistic regression analysis was used to evaluate the ability of ADC to predict the presence of invasion.

RESULTS

Surgical pathology demonstrated associated invasive cancer in 26.8%, microinvasion in 14.6%, and pure DCIS in 58.5%. The minimum regions of interest (ROI)-based ADC was significantly different among the following three groups (P < .001, Kruskal-Wallis test): 0.98 × 10(-3) mm(2)/s ± 0.25 for pure DCIS, 0.82 × 10(-3) mm(2)/s ± 0.20 for DCIS with microinvasion, and 0.71 × 10(-3) mm(2)/s ± 0.27 for DCIS with invasive disease. Based on logistic regression analysis, the minimum ROI-based ADC of 0.56 × 10(-3) mm(2)/s was a significant predictor for invasive disease (odds ratio = 0.02, 95% confidence interval [0.002, 0.207], P = .001). Regardless of the field strength (1.5 vs. 3.0 T) ADC values of high-grade and non-high-grade DCIS were not significantly different.

CONCLUSIONS

Pure DCIS had the highest "ROI-based" ADC measured using 1.5 T or 3.0 T. The ADC was able to identify microinvasion or invasive cancer in biopsy-proven DCIS lesions but not to distinguish the DCIS grades.

Semi-automated quantitative intravoxel incoherent motion analysis and its implementation in breast diffusion-weighted imaging

BACKGROUND

To optimize and validate intravoxel incoherent motion (IVIM) modeled diffusion-weighted imaging (DWI) compared with the apparent diffusion coefficient (ADC) for semi-automated analysis of breast lesions using a multi-reader setup.

MATERIALS AND METHODS

Patients (n = 176) with breast lesions (≥1 cm) and known pathology were prospectively examined (1.5 Tesla) with DWI (b = 0, 50, 200, 500, 800, 1000 s/mm(2) ) between November 2008 and July 2014 and grouped into a training and test set. Three independent readers applied a semi-automated procedure for setting regions-of-interest for each lesion and recorded ADC and IVIM parameters: molecular diffusion (Dslow ), microperfusion (Dfast ), and the fraction of Dfast (ffast ). In the training set (24 lesions, 12 benign), a semi-automated method was optimized to yield maximum true negatives (TN) with minimal false negatives (FN): only the optimal fraction (Fo) of voxels in the lesions was used and optimal thresholds were determined. The optimal Fo and thresholds were then applied to a consecutive test set (139 lesions, 23 benign) to obtain specificity and sensitivity.

RESULTS

In the training set, optimal thresholds were 1.44 × 10(-3) mm(2) /s (Dslow ), 18.55 × 10(-3) mm(2) /s (Dfast ), 0.247 (ffast ) and 2.00 × 10(-3) mm(2) /s (ADC) with Fo set to 0.61, 0.85, 1.0, and 1.0, respectively, this resulted in TN = 5 (IVIM) and TN = 1 (ADC), with FN = 0. In the test set, sensitivity and specificity among the readers were 90.5-93.1% and 43.5-52.2%, respectively, for IVIM, and 94.8-95.7% and 13.0-21.7% for ADC (P ≤ 0.0034) without inter-reader differences (P = 1.000).

CONCLUSION

The presented semi-automated method for breast lesion evaluation is reader independent and yields significantly higher specificity for IVIM compared with the ADC.

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.

Histogram analysis of apparent diffusion coefficient at 3.0t: Correlation with prognostic factors and subtypes of invasive ductal carcinoma

PURPOSE

To evaluate apparent diffusion coefficient (ADC) histogram parameters that show correlations with prognostic factors and subtypes of breast cancer.

MATERIALS AND METHODS

At 3.0T, various ADC histogram parameters were calculated including the entire tumor volume in 173 invasive ductal carcinomas: the minimum, 10th percentile, mean, median, 90th percentile, and maximum. ADC parameters were correlated with prognostic factors and subtype.

RESULTS

The mean ADCmedian value was significantly higher in the group with lymph node metastasis, HER2 positivity, and a Ki-67 value <14% than in the group with negativity for lymph node metastasis, HER2 negativity, and a Ki-67 value ≥14% (0.907, 0.978, and 0.941 vs. 0.735, 0.778, and 0.761 × 10(-3) mm(2) /s, respectively) (P < 0.01). There was no significant correlation between ADCmedian and tumor size, histologic grade, estrogen receptor expression, and progesterone receptor expression (P = 0.272, 0.113, 0.261, and 0.181, respectively). For most ADC parameters except for ADCmin , the mean of variable ADC parameters of HER2-positive, luminal A, luminal B-HER2(+), triple-negative, and luminal B-HER2(-) diseases were arranged in descending order (1.175, 0.936, 0.863, 0.811, and 0.665 × 10(-3) mm(2) /s in ADCmedian , respectively) with statistical significant difference (P < 0.001). In multivariate analysis, histologic grade, the Ki-67 index, and HER2 expression were statistically significant explanatory prognostic factors for ADCmedian and the Ki-67 index had the most robust effects on ADC parameters (standardized coefficient = -0.317).

CONCLUSION

Various ADC parameters were correlated with prognostic factors and subtype, except for ADCmin . HER2 positivity showed high ADC values and high Ki-67 index revealed low ADC values.

Multiparametric evaluation of breast lesions using PET-MRI: initial results and future perspectives

The purpose of this study was to evaluate the diagnostic accuracy of multiparametric evaluation of breast lesions combining information of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), diffusion-weighted imaging (DWI), and F-fluoro-deoxi-glucose (F-FDG) positron emission tomography/computed tomography (PET-CT). After approval of the institutional research ethics committee, 31 patients with suspicious breast lesions on MRI performed F-FDG PET-CT with a specific protocol for breast evaluation. Patients' mean age was 47.8 years (range, 29-77 years). Positron emission tomography and magnetic resonance imaging (PET-MRI) images were fused. A lesion was considered positive on multiparametric evaluation if at least 1 of the following was present: washout/type 3 kinetic curve on DCE-MRI, restricted diffusion on DWI with minimum apparent diffusion coefficient value <1.00 × 10 mm/s, and abnormal metabolism on F-FDG PET-CT (higher than the physiologic uptake of the normal breast parenchyma). Thirty-eight lesions with histologic correlation were evaluated on the 31 included patients, being 32 mass lesions (84.2%), and 6 nonmass lesions (15.8%). Lesions' mean diameter was 31.1 mm (range, 8-94 mm). Multiparametric evaluation provided 100% sensitivity, 55.5% specificity, 87.9% positive predictive value, 100% negative predictive value, and 89.5% accuracy, with 29 true-positives results, 5 true-negatives, 4 false-positives, and no false-negative results. Multiparametric evaluation with PET-MRI functional data showed good diagnostic accuracy to differentiate benign from malignant breast lesions, reducing the number of unnecessary biopsies, without missing any diagnosis of cancer in our case series.

Diffusion magnetic resonance imaging in breast cancer characterisation: correlations between the apparent diffusion coefficient and major prognostic factors

PURPOSE

This study was done to investigate the correlation between the apparent diffusion coefficient (ADC) and prognostic factors of breast cancer.

MATERIALS AND METHODS

From January 2008 to June 2011, all consecutive patients with breast cancer who underwent breast magnetic resonance imaging (MRI) and subsequent surgery in our hospital were enrolled in our study. The MRI protocol included a diffusion-weighted imaging sequence with b values of 0 and 1,000 s/mm(2). For each target lesion in the breast, the ADC value was compared with regard to major prognostic factors: histology, tumour grade, tumour size, lymph node status, and age.

RESULTS

A total of 289 patients with a mean age of 53.49 years were included in the study. The mean ADC value of malignant lesions was 1.02 × 10(-3) mm(2)/s. In situ carcinomas, grade 1 lesions, and tumours without lymph nodal involvement had mean ADC values that were significantly higher than those of invasive carcinomas (p = 0.009), grade 2/3 lesions (p < 0.001), and tumours with nodal metastases (p = 0.001). No significant differences were observed in ADC values among tumours of different sizes or among patient age groups.

CONCLUSIONS

ADC values appear to correlate with tumour grade and some major prognostic factors.

Diffusion weighted magnetic resonance imaging of the breast: protocol optimization, interpretation, and clinical applications

Diffusion-weighted magnetic resonance (MR) imaging (DWI) has shown promise for improving the positive predictive value of breast MR imaging for detection of breast cancer, evaluating tumor response to neoadjuvant chemotherapy, and as a noncontrast alternative to MR imaging in screening for breast cancer. However, data quality varies widely. Before implementing DWI into clinical practice, one must understand the pertinent technical considerations and current evidence regarding clinical applications of breast DWI. This article provides an overview of basic principles of DWI, optimization of breast DWI protocols, imaging features of benign and malignant breast lesions, promising clinical applications, and potential future directions.