Diffusion-weighted MR imaging of the breast: Advantages and pitfalls

Novel approach to MRI based risk stratification of uterine myometrial lesions

BACKGROUND

Surgery for uterine mesenchymal tumors is common in gynecology. Preoperative diagnosis of malignant tumors can lead to appropriate management for the lesions.

PURPOSE

This study aims to externally validate a previous MRI-based expert consensus algorithm and evaluate the potential modification of MR-based scoring system's accuracy in diagnosing uterine mesenchymal tumors (UMT).

MATERIAL AND METHODS

With institutional ethics committee approval and a waiver of informed consent (CRM-2405-410), a bicentric retrospective observational cohort study was conducted from January 2018 to December 2023. The study included women with a pathological diagnosis of uterine mesenchymal tumor following a pelvic MRI within six months. Clinical and MR criteria were blindly recorded by two radiologists (6- and 3-years' experience in gynaecological MR imaging) who assessed several MR features. Continuous variables were analyzed using a Mann-Whitney test, and categorical variables using Fisher's exact test. Odds ratios (OR) for predicting malignancy were calculated with 95% confidence intervals and p-values.

RESULTS

The cohort included 455 women (mean age: 43 years, range: 15-82 years) with mesenchymal tumors: 437 leiomyomas, 2 STUMPs (0.4 %), and 16 malignant UMT (3.5 %). Using initial criteria (enlarged pelvic lymph nodes, T2W signal intensity, DW signal intensity compared to endometrium, and ADC cutoff value of 0.9 × 10-3 mm2/sec), the model accurately classified 421 out of 455 cases (Accuracy: 92,5% (CI 93,1-94,3) and missed with 7 tumors (5 leiomyosarcomas, 2 STUMP). The sensitivity was 61.1 % (CI95% 38.5-83.6) and specificity was 93.8 % (CI95% 91.2-95.8) A modified algorithmic approach added "irregular tumor margins" and menopausal status, modified DW signal compared to bladder, and an elevated ADC cutoff value of 1.23 × 10-3 mm2/sec, improving classification to 446 out of 455 cases (Accuracy: 98 % (CI95% 97.1 %-98.1 %) with only 3 missed tumors (2 STUMP and one leiomyosarcoma). The sensitivity was 83.3 % (CI95% 79-88) and specificity was 98.6 % (CI95% 98-99). The new algorithm significantly improved accuracy (p = 0.001), allowing the development of a 5-category scoring system.

CONCLUSION

Modified MR imaging evaluation algorithms increase true positive diagnosis of malignant UMTs leading to effective differentiation from benign leiomyomas. The new algorithm can allow for appropriate triage of potentially malignant UMTs, alleviating risk associated with morcellation in patients with uterine leiomyosarcoma.

SUMMARY

Our study demonstrates that combining 5 criteria based on multivariate analysis in a new algorithm (T2W signal, DW signal, ADC cut off value of 1.23 x 10-3 mm2/sec, tumor margins and menopausal status) allows us to distinguish benign from malignant uterine mesenchymal tumors with an accuracy of 98 % (CI95% 97,1%-98,1%), a sensitivity of 83.3 % (CI95% 79-88) and a specificity of 98.6 % (CI95% 98-99). This model allows to build a stratification score that would help in the management of typical and atypical uterine lesions.

The Value of Whole-Volume Radiomics Machine Learning Model Based on Multiparametric MRI in Predicting Triple-Negative Breast Cancer

OBJECTIVE

This study aimed to investigate the value of radiomics analysis in the precise diagnosis of triple-negative breast cancer (TNBC) based on breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and apparent diffusion coefficient (ADC) maps.

METHODS

This retrospective study included 326 patients with pathologically proven breast cancer (TNBC: 129, non-TNBC: 197). The lesions were segmented using the ITK-SNAP software, and whole-volume radiomics features were extracted using a radiomics platform. Radiomics features were obtained from DCE-MRI and ADC maps. The least absolute shrinkage and selection operator regression method was employed for feature selection. Three prediction models were constructed using a support vector machine classifier: Model A (based on the selected features of the ADC maps), Model B (based on the selected features of DCE-MRI), and Model C (based on the selected features of both combined). Receiver operating characteristic curves were used to evaluate the diagnostic performance of the conventional MR image model and the 3 radiomics models in predicting TNBC.

RESULTS

In the training dataset, the AUCs for the conventional MR image model and the 3 radiomics models were 0.749, 0.801, 0.847, and 0.896. The AUCs for the conventional MR image model and 3 radiomics models in the validation dataset were 0.693, 0.742, 0.793, and 0.876, respectively.

CONCLUSIONS

Radiomics based on the combination of whole volume DCE-MRI and ADC maps is a promising tool for distinguishing between TNBC and non-TNBC.

The clinical utility of diffusion-weighted imaging in diagnosing and predicting treatment response of laryngeal and hypopharyngeal carcinoma: A systematic review and meta-analysis

PURPOSE

Laryngeal and Hypopharyngeal Carcinomas (LC/HPC) constitute about 24 % of head and neck cancers, causing more than 90,000 annual deaths worldwide. Diffusion-Weighted Imaging (DWI), is currently widely studied in oncologic imaging and can aid in distinguishing cellular tumors from other tissues. Our objective was to review the effectiveness of DWI in three areas: diagnosing, predicting prognosis, and predicting treatment response in patients with LC/HPC.

METHODS

A systematic search was conducted in PubMed, Web of Science, and Embase. A meta-analysis by calculating Standardized Mean Difference (SMD) and 95 % Confidence Interval (CI) was conducted on diagnostic studies.

RESULTS

A total of 16 studies were included. All diagnostic studies (n = 9) were able to differentiate between the LC/HPC and other benign laryngeal/hypopharyngeal lesions. These studies found that LC/HPC had lower Apparent Diffusion Coefficient (ADC) values than non-cancerous lesions. Our meta-analysis of 7 diagnostic studies, that provided ADC values of malignant and non-malignant tissues, demonstrated significantly lower ADC values in LC/HPC compared to non-malignant lesions (SMD = -1.71, 95 %CI: [-2.00, -1.42], ADC cut-off = 1.2 × 103 mm2/s). Furthermore, among the studies predicting prognosis, 67 % (4/6) accurately predicted outcomes based on pretreatment ADC values. Similarly, among studies predicting treatment response, 50 % (2/4) successfully predicted outcomes based on pretreatment ADC values. Overall, the studies that looked at prognosis or treatment response in LC/HPC found a positive correlation between pretreatment ADC values in larynx/hypopharynx and favorable outcomes.

CONCLUSION

DWI aids significantly in the LC/HPC diagnosis. However, further research is needed to establish DWI's reliability in predicting prognosis and treatment response in patients with LC/HPC.

Imaging and Liquid Biopsy for Distinguishing True Progression From Pseudoprogression in Gliomas, Current Advances and Challenges

RATIONALE AND OBJECTIVES

Gliomas are aggressive brain tumors with a poor prognosis. Assessing treatment response is challenging because magnetic resonance imaging (MRI) may not distinguish true progression (TP) from pseudoprogression (PsP). This review aims to discuss imaging techniques and liquid biopsies used to distinguish TP from PsP.

MATERIALS AND METHODS

This review synthesizes existing literature to examine advances in imaging techniques, such as magnetic resonance diffusion imaging (MRDI), perfusion-weighted imaging (PWI) MRI, and liquid biopsies, for identifying TP or PsP through tumor markers and tissue characteristics.

RESULTS

Advanced imaging techniques, including MRDI and PWI MRI, have proven effective in delineating tumor tissue properties, offering valuable insights into glioma behavior. Similarly, liquid biopsy has emerged as a potent tool for identifying tumor-derived markers in biofluids, offering a non-invasive glimpse into tumor evolution. Despite their promise, these methodologies grapple with significant challenges. Their sensitivity remains inconsistent, complicating the accurate differentiation between TP and PSP. Furthermore, the absence of standardized protocols across platforms impedes the reliability of comparisons, while inherent biological variability adds complexity to data interpretation.

CONCLUSION

Their potential applications have been highlighted, but gaps remain before routine clinical use. Further research is needed to develop and validate these promising methods for distinguishing TP from PsP in gliomas.

Combining b2500 diffusion-weighted imaging with BI-RADS improves the specificity of breast MRI

PURPOSE

The purpose of this study was to evaluate the diagnostic performance of visual assessment of diffusion-weighted images (DWI) obtained with a b value of 2500 s/mm2 in addition to a conventional magnetic resonance imaging (MRI) protocol to characterize breast lesions.

MATERIALS AND METHODS

This single-institution retrospective study included participants who underwent clinically indicated breast MRI and breast biopsy from May 2017 to February 2020. The examination included a conventional MRI protocol including DWI obtained with a b value of 50 s/mm2 (b50DWI) and a b value of 800 s/mm2 (b800DWI) and DWI obtained with a b value of 2500 s/mm2 (b2500DWI). Lesions were classified using Breast Imaging Reporting and Data Systems (BI-RADS) categories. Three independent radiologists assessed qualitatively the signal intensity within the breast lesions relative to breast parenchyma on b2500DW and b800DWI and measured the b50-b800-derived apparent diffusion coefficient (ADC) value. The diagnostic performances of BI-RADS, b2500DWI, b800DWI, ADC and of a model combining b2500DWI and BI-RADS were evaluated using receiver operating characteristic (ROC) curves analysis.

RESULTS

A total of 260 patients with 212 malignant and 100 benign breast lesions were included. There were 259 women and one man with a median age of 53 years (Q1, Q3: 48, 66 years). b2500DWI was assessable in 97% of the lesions. Interobserver agreement for b2500DWI was substantial (Fleiss kappa = 0.77). b2500DWI yielded larger area under the ROC curve (AUC, 0.81) than ADC with a 1 × 10-3 mm2/s threshold (AUC, 0.58; P = 0.005) and than b800DWI (AUC, 0.57; P = 0.02). The AUC of the model combining b2500DWI and BI-RADS was 0.84 (95% CI: 0.79-0.88). Adding b2500DWI to BI-RADS resulted in a significant increase in specificity from 25% (95% CI: 17-35) to 73% (95% CI: 63-81) (P < 0.001) with a decrease in sensitivity from 100% (95% CI: 97-100) to 94% (95% CI: 90-97), (P < 0.001).

CONCLUSION

Visual assessment of b2500DWI has substantial interobserver agreement. Visual assessment of b2500DWI offers better diagnostic performance than ADC and b800DWI. Adding visual assessment of b2500DWI to BI-RADS improves the specificity of breast MRI and could avoid unnecessary biopsies.

Predictive value of intravoxel incoherent motion combined with diffusion kurtosis imaging for breast cancer axillary lymph node metastasis: a retrospective study

BACKGROUND

Non-invasive imaging technologies for assessing axillary lymph node (ALN) metastasis of breast cancer are needed in clinical practice.

PURPOSE

To explore the clinical value of intravoxel incoherent motion (IVIM) and diffusional kurtosis imaging (DKI) for predicting ALN metastasis of breast cancer.

MATERIAL AND METHODS

A total of 194 patients with pathologically confirmed breast cancer who underwent IVIM and DKI examination were reviewed retrospectively. The IVIM derived parameters of D, D*, and f and DKI-derived parameters of MD and MK were measured. The independent samples t-test was used to compare the parameters between the ALN metastasis and non-ALN metastasis groups. Receiver operating characteristic (ROC) curve analysis was also performed.

RESULTS

The D and MD in the ALN metastasis group were significantly lower than those in the non-ALN metastasis group (P < 0.001, P < 0.001). The D*, f, and MK were higher in the ALN metastasis group than in the non-ALN metastasis group (P = 0.015, P = 0.014, and P = 0.001, respectively). The area under the ROC curve (AUC) of D (0.768) was highest. In addition, the diagnostic efficiency of both IVIM and DKI were higher than that of the conventional MRI (P = 0.002, P = 0.048). The diagnostic efficiency of IVIM + DKI were higher than that of the IVIM or DKI alone (P = 0.021, P = 0.004).

CONCLUSION

IVIM and DKI can be used for predicting breast cancer ALN metastasis with D as the most meaningful parameter.

How to optimize MRI breast protocol? The value of combined analysis of ultrafast and diffusion-weighted MRI sequences

PURPOSE

The purpose of this retrospective study was to demonstrate the validity of early enhancement criteria on ultrafast magnetic resonance imaging (MRI) sequence to predict malignancy in a large population, and the benefit of diffusion-weighted imaging (DWI) to improve the performance of breast MRI.

MATERIAL AND METHODS

Women who underwent breast MRI examination between April 2018 and September 2020 and further breast biopsy were retrospectively included. Two readers quoted the different conventional features and classified the lesion according to the BI-RADS classification based on the conventional protocol. Then, the readers checked for the presence of early enhancement (≤ 30 s) on ultrafast sequence and the presence of an apparent diffusion coefficient (ADC) ≥ 1.5 × 10^-3 mm²/s to classify the lesions based on morphology and these two functional criteria only.

RESULTS

Two hundred fifty-seven women (median age: 51 years; range: 16-92 years) with 436 lesions (157 benign, 11 borderline and 268 malignant) were included. A MRI protocol plus two simple functional features, early enhancement (≤ 30 s) and an ADC value ≥ 1.5 × 10^-3 mm²/s, had a greater accuracy than the conventional protocol to distinguish benign from malignant breast lesions with or without ADC value (P = 0.01 and P = 0.001, respectively) on MRI, mainly due to better classification of benign lesions (increased specificity) with increasing diagnostic confidence of 3.7% and 7.8% respectively.

CONCLUSION

BI-RADS analysis based on a simple short MRI protocol plus early enhancement on ultrafast sequence and ADC value has a greaterr diagnostic accuracy than a conventional protocol and may avoid unnecessary biopsy.

Multiple parameters from ultrafast dynamic contrast-enhanced magnetic resonance imaging to discriminate between benign and malignant breast lesions: Comparison with apparent diffusion coefficient

PURPOSE

The purpose of this study was first to assess the diagnostic performance of ultrafast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters compared to apparent diffusion coefficient (ADC) for distinguishing benign from malignant breast lesions and second to investigate the complementarity of ultrafast DCE-MRI with DWI in that task.

MATERIALS AND METHODS

A total of 142 women (mean age, 48.42 ± 11.03 [SD]) years; range: 14-78 years) with 150 breast lesions who underwent breast ultrafast DCE-MRI were prospectively recruited. Ultrafast DCE-MRI semi-quantitative parameters (maximum slope [MS], time to peak [TTP], time to enhancement [TTE], and initial area under curve in 60 s [iAUC]), ultrafast DCE-MRI quantitative parameters (K_ep, K_trans, and V_e), and the ADC were estimated and compared between benign and malignant breast lesions. Classification performances were assessed using area under the receiver operating characteristic curve (AUC) and compared using Delong test.

RESULTS

The ultrafast DCE-MRI semi-quantitative multiparameters (AUC, 0.913; 95% CI: 0.856-0.953) showed better classification performance than the quantitative multiparameters (AUC, 0.818; 95% CI: 0.747-0.876) (P = 0.022). No differences in AUC were found between ultrafast DCE-MRI semi-quantitative multiparameters and ADC (AUC, 0.912; 95% CI: 0.855-0.952) (P = 0.990). The combination of ultrafast DCE-MRI semi-quantitative multiparameters and ADC (AUC, 0.960; 95% CI: 0.915-0.985) showed better classification performance than the ultrafast DCE-MRI semi-quantitative multiparameters (P = 0.014) and quantitative multiparameters (P < 0.001).

CONCLUSION

Ultrafast DCE-MRI can be used as an accurate method for discriminating benign from malignant breast lesions. The combination of ultrafast DCE-MRI and DWI significantly increases the diagnostic value of ultrafast DCE-MRI.

Diagnostic performance of mono-exponential DWI versus diffusion kurtosis imaging in breast lesions: A meta-analysis

BACKGROUND

This meta-analysis aimed to explore the diagnostic value of diffusion kurtosis imaging (DKI) compared to mono-exponential diffusion weighted imaging (DWI) in the diagnosis of breast cancer.

METHODS

A systematic electronic literature search (up to September 2020) was conducted for published English-language studies comparing the diagnostic values of DKI and DWI for the detection of breast cancer. The data of mean kurtosis (MK), mean diffusivity (MD), and apparent diffusion coefficient (ADC) were extracted to construct 2 × 2 contingency tables. The pooled sensitivities, specificities, and areas under the receiver operating characteristic curve (AUCs) were compared between DKI and DWI in the diagnosis of breast cancer.

RESULTS

Eight studies were finally included, with a total of 771 patients in the same population. Pooled sensitivities were 82.0% [95% confidence interval (95% CI), 78.2-85.3%] for ADC, 87.3% (95% CI, 83.9-90.1%) for MK, and 83.9% (95% CI, 80.2-87.1%) for MD. Pooled specificities were 81.1% (95% CI, 76.7-84.9%) for ADC, 85.1% (95% CI, 81.1-88.5%) for MK, and 83.2% (95% CI, 79.0-86.8%) for MD. According to the summary receiver operator characteristic curve analyses, the AUCwas 0.901 for ADC, 0.930 for MK, and 0.918 for MD (ADC vs MK, P = .353; ADC vs MD, P = .611). No notable publication bias was found, while significant heterogeneity was observed.

CONCLUSIONS

Although DKI is feasible for identifying breast cancer, MD and MK offer similar diagnostic performance to ADC values. Thus, we recommend that DKI should not be included in the routine evaluation of breast lesions now.

Value of magnetic resonance diffusion combined with perfusion imaging techniques for diagnosing potentially malignant breast lesions

BACKGROUND

Lesions of breast imaging reporting and data system (BI-RADS) 4 at mammography vary from benign to malignant, leading to difficulties for clinicians to distinguish between them. The specificity of magnetic resonance imaging (MRI) in detecting breast is relatively low, leading to many false-positive results and high rates of re-examination or biopsy. Diffusion-weighted imaging (DWI), combined with perfusion-weighted imaging (PWI), might help to distinguish between benign and malignant BI-RADS 4 breast lesions at mammography.

AIM

To evaluate the value of DWI and PWI in diagnosing BI-RADS 4 breast lesions.

METHODS

This is a retrospective study which included patients who underwent breast MRI between May 2017 and May 2019 in the hospital. The lesions were divided into benign and malignant groups according to the classification of histopathological results. The diagnostic efficacy of DWI and PWI were analyzed respectively and combinedly. The 95 lesions were divided according to histopathological diagnosis, with 46 benign and 49 malignant. The main statistical methods used included the Student t-test, the Mann-Whitney U-test, the chi-square test or Fisher’s exact test.

RESULTS

The mean apparent diffusion coefficient (ADC) values in the parenchyma and lesion area of the normal mammary gland were 1.82 ± 0.22 × 10^-3 mm²/s and 1.24 ± 0.16 × 10^-3 mm²/s, respectively (P = 0.021). The mean ADC value of the malignant group was 1.09 ± 0.23 × 10^-3 mm²/s, which was lower than that of the benign group (1.42 ± 0.68 × 10^-3 mm²/s) (P = 0.016). The volume transfer constant (Ktrans) and rate constant (Kep) values were higher in malignant lesions than in benign ones (all P < 0.001), but there were no significant statistical differences regarding volume fraction (V_e) (P = 0.866). The sensitivity and specificity of PWI combined with DWI (91.7% and 89.3%, respectively) were higher than that of PWI or DWI alone. The accuracy of PWI combined with DWI in predicting pathological results was significantly higher than that predicted by PWI or DWI alone.

CONCLUSION

DWI, combined with PWI, might possibly distinguish between benign and malignant BI-RADS 4 breast lesions at mammography.

Diagnostic value of diffusion-weighted imaging with synthetic b-values in breast tumors: comparison with dynamic contrast-enhanced and multiparametric MRI

Objectives

To assess DWI for tumor visibility and breast cancer detection by the addition of different synthetic b-values.

Methods

Eighty-four consecutive women who underwent a breast-multiparametric-MRI (mpMRI) with enhancing lesions on DCE-MRI (BI-RADS 2–5) were included in this IRB-approved retrospective study from September 2018 to March 2019. Three readers evaluated DW acquired b-800 and synthetic b-1000, b-1200, b-1500, and b-1800 s/mm² images for lesion visibility and preferred b-value based on lesion conspicuity. Image quality (1–3 scores) and breast composition (BI-RADS) were also recorded. Diagnostic parameters for DWI were determined using a 1–5 malignancy score based on qualitative imaging parameters (acquired + preferred synthetic b-values) and ADC values. BI-RADS classification was used for DCE-MRI and quantitative ADC values + BI-RADS were used for mpMRI.

Results

Sixty-four malignant (average = 23 mm) and 39 benign (average = 8 mm) lesions were found in 80 women. Although b-800 achieved the best image quality score, synthetic b-values 1200–1500 s/mm² were preferred for lesion conspicuity, especially in dense breast. b-800 and synthetic b-1000/b-1200 s/mm² values allowed the visualization of 84–90% of cancers visible with DCE-MRI performing better than b-1500/b-1800 s/mm². DWI was more specific (86.3% vs 65.7%, p < 0.001) but less sensitive (62.8% vs 90%, p < 0.001) and accurate (71% vs 80.7%, p = 0.003) than DCE-MRI for breast cancer detection, where mpMRI was the most accurate modality accounting for less false positive cases.

Conclusion

The addition of synthetic b-values enhances tumor conspicuity and could potentially improve tumor visualization particularly in dense breast. However, its supportive role for DWI breast cancer detection is still not definite.

Key Points

• The addition of synthetic b-values (1200–1500 s/mm²) to acquired DWI afforded a better lesion conspicuity without increasing acquisition time and was particularly useful in dense breasts.

• Despite the use of synthetic b-values, DWI was less sensitive and accurate than DCE-MRI for breast cancer detection.

• A multiparametric MRI modality still remains the best approach having the highest accuracy for breast cancer detection and thus reducing the number of unnecessary biopsies.

Electronic supplementary material

The online version of this article (10.1007/s00330-020-07094-z) contains supplementary material, which is available to authorized users.

Differential diagnosis of plasma cell mastitis and invasive ductal carcinoma using multiparametric MRI

Background

Evaluate the potential of multiparametric magnetic resonance imaging (MRI) for the differential diagnosis of plasma cell mastitis (PCM) and invasive ductal carcinoma (IDC).

Methods

A total of 465 female patients, including 197 PCM (42.4%) and 268 IDC (57.6%), were examined using breast MRI scanning using routine sequences, dynamic contrast-enhanced MRI (DCE-MRI), diffusion-weighted imaging (DWI) and MR spectroscopy (MRS). The MRI features of PCM and IDC lesions were analyzed and compared to the histological results.

Results

Compared to IDC, the PCM lesions were more frequent in the subareolar regions, hyperintense on T2WI (P<0.01) and showed an initial signal increase ≤90%, a persistent and plateau pattern of time-intensity curves, non-mass enhancement, multiple rim enhancements, central hyperintensity on DWI, a higher ADC value, and total choline (tCho) peak negative and tCho peak integral <29.95 AU (P<0.01). The following breast-associated findings were also observed frequently in PCM: Ipsilateral breast enlargement, nipple retraction, skin thickening, peripheral edema and axillary lymphadenopathy. However, no significant difference was observed between the two groups for the shape, border and adjacent vessel signs of the lesion.

Conclusions

Some of the MRI features of PCM and IDC lesions were different. An integrated analysis of these multiparametric MRI features can thus assist in the differential diagnosis of PCM and IDC lesions.

Development of a risk-stratification scoring system for predicting lymphovascular invasion in breast cancer

Background

Lymphovascular invasion (LVI) is a vital risk factor for prognosis across cancers. We aimed to develop a scoring system for stratifying LVI risk in patients with breast cancer.

Methods

A total of 301 consecutive patients (mean age, 49.8 ± 11.0 years; range, 29–86 years) with breast cancer confirmed by pathological reports were retrospectively evaluated at the authors’ institution between June 2015 and October 2018. All patients underwent contrast-enhanced Magnetic Resonance Imaging (MRI) examinations before surgery. MRI findings and histopathologic characteristics of tumors were collected for analysis. Breast LVI was confirmed by postoperative pathology. We used a stepwise logistic regression to select variables and two cut-points were determined to create a three-tier risk-stratification scoring system. The patients were classified as having low, moderate and high probability of LVI. The area under the receiver operating characteristic curve (AUC) was used to evaluate the discrimination ability of the scoring system.

Results

Tumor margins, lobulation sign, diffusion-weighted imaging appearance, MRI-reported axillary lymph node metastasis, time to signal intensity curve pattern, and HER-2 were selected as predictors for LVI in the point-based scoring system. Patients were considered at low risk if the score was < 3.5, moderate risk if the score was 3.5 to 6.0, and high risk if the score was ≥6.0. LVI risk was segmented from 0 to 100.0% and was positively associated with an increase in risk scores. The AUC of the scoring system was 0.824 (95% confidence interval [CI]: 0.776--0.872).

Conclusion

This study shows that a simple and reliable score-based risk-stratification system can be practically used in stratifying the risk of LVI in breast cancer.

Differentiation between idiopathic granulomatous mastitis and invasive breast carcinoma, both presenting with non-mass enhancement without rim-enhanced masses: The value of whole-lesion histogram and texture analysis using apparent diffusion coefficient

PURPOSE

The aim of this study was to investigate whether whole-lesion histogram and texture analysis using apparent diffusion coefficient can discriminate between idiopathic granulomatous mastitis (IGM) and invasive breast carcinoma (IBC), both of which appeared as non-mass enhancement lesions without rim-enhanced masses.

METHOD

This retrospective study included 58 pathology-proven female patients at two independent study sites (27 IGM patients and 31 IBC patients). Diffusion-weighted imaging (3b values, 50, 400 or 500, and 800 s/mm²) was performed using 1.5 T or 3 T MR scanners from the same vendor. Whole-lesions were segmented and 11 features were extracted. Univariate analysis and multivariate logistic regression analysis were performed to identify significant variables for differentiating IGM from IBC. Receiver operating characteristic curve was assessed. The interobserver reliability between two observers for the histogram and texture measurement was also reported.

RESULTS

The 5th percentile, difference entropy and entropy of apparent diffusion coefficient showed significant differences between the two groups. An area under the curve of 0.778 (95 % CI: 0.648, 0.908), accuracy of 79.3 %, and sensitivity of 87.1 % was achieved using these three significant features. No significant feature was found with the multivariate analysis. For the interobserver reliability, all apparent diffusion coefficient parameters except skewness and kurtosis indicated good or excellent agreement, while these two features showed moderate agreement.

CONCLUSIONS

Whole-lesion histogram and texture analysis using apparent diffusion coefficient provide a non-invasive analytical approach to the differentiation between IGM and IBC, both presenting with non-mass enhancement without rim-enhanced masses.

Invasive ductal breast cancer: preoperative predict Ki-67 index based on radiomics of ADC maps

PURPOSE

The purpose of this study is to develop a radiomics model for predicting the Ki-67 proliferation index in patients with invasive ductal breast cancer through magnetic resonance imaging (MRI) preoperatively.

MATERIALS AND METHODS

A total of 128 patients who were clinicopathologically diagnosed with invasive ductal breast cancer were recruited. This cohort included 32 negative Ki67 expression (Ki67 proliferation index < 14%) and 96 cases with positive Ki67 expression (Ki67 proliferation index ≥ 14%). All patients had undergone diffusion-weighted imaging (DWI) MRI before surgery on a 3.0T MRI scanner. Radiomics features were extracted from apparent diffusion coefficient (ADC) maps which were obtained by DWI-MRI from patients with invasive ductal breast cancer. 80% of the patients were divided into training set to build radiomics model, and the rest into test set to evaluate its performance. The least absolute shrinkage and selection operator (LASSO) was used to select radiomics features, and then, the logistic regression (LR) model was established using fivefold cross-validation to predict the Ki-67 index. The performance was evaluated by receiver-operating characteristic (ROC) analysis, accuracy, sensitivity and specificity.

RESULTS

Quantitative imaging features (n = 1029) were extracted from ADC maps, and 11 features were selected to construct the LR model. Good identification ability was exhibited by the ADC-based radiomics model, with areas under the ROC (AUC) values of 0.75 ± 0.08, accuracy of 0.71 in training set and 0.72, 0.70 in test set.

CONCLUSIONS

The ADC-based radiomics model is a feasible predictor for the Ki-67 index in patients with invasive ductal breast cancer. Therefore, we proposed that three-dimensional imaging features from ADC maps could be used as candidate biomarker for preoperative prediction the Ki-67 index noninvasively.

Diffusional kurtosis imaging for differentiation of additional suspicious lesions on preoperative breast MRI of patients with known breast cancer

PURPOSE

To investigate the potential of diffusional kurtosis imaging (DKI) and conventional diffusion-weighted imaging (DWI) in the evaluation of additional suspicious lesions at preoperative breast magnetic resonance imaging (MRI) in patients with breast cancer.

MATERIALS AND METHODS

Fifty-three additional suspicious lesions in 45 patients with breast cancer, which were detected on preoperative breast MRI, were examined with a 3-T MR system. DKI and DWI data were obtained using a spin-echo single-shot echo-planar imaging sequence with b-values of 0, 50, 600, 1000, and 3000 s/mm². Histogram parameters (mean, standard deviation, minimum, maximum, 10th, 25th, 50th, 75th, 90th percentiles, kurtosis, skewness and entropy) of ADC from DWI and diffusivity (D), kurtosis (K) from DKI were calculated after postprocessing. Parameters were compared between benign vs. ductal carcinoma in situ (DCIS) vs. invasive breast lesions and diagnostic performances were evaluated by receiver operating characteristic (ROC) analysis. Correlation between the mean values of D and K was analyzed according to lesion type.

RESULTS

Multiple histogram parameters of D (mean, 25th, 50th percentile, 75th percentile, and entropy) differed between benign and invasive breast lesions (all P < 0.005), but none differed between benign vs. DCIS. D-90th percentile differed between DCIS vs. invasive cancer (P = 0.040). K-10th percentile differed between benign vs. DCIS (P = 0.015). ADC-75th percentile differed between benign vs. invasive cancer and ADC-75th percentile, ADC-90th percentile differed between DCIS vs. invasive cancer, respectively (all P < 0.005). ROC curve analysis showed high specificity for discrimination between benign and invasive cancer. D-mean and K-mean showed strong correlation in benign (r_s = -0.813) and invasive lesions (r_s = -0.853), but no significant correlation in DCIS.

CONCLUSION

DKI may aid in the differentiation of additional suspicious lesions at preoperative breast MRI. Both ADC and DKI may have lower potential in differentiating DCIS from benign lesions.

Machine Learning-Based Analysis of MR Multiparametric Radiomics for the Subtype Classification of Breast Cancer

Objective: To investigate whether machine learning analysis of multiparametric MR radiomics can help classify immunohistochemical (IHC) subtypes of breast cancer. Study design: One hundred and thirty-four consecutive patients with pathologically-proven invasive ductal carcinoma were retrospectively analyzed. A total of 2,498 features were extracted from the DCE and DWI images, together with the new calculated images, including DCE images changing over six time points (DCEsequential) and DWI images changing over three b-values (DWIsequential). We proposed a novel two-stage feature selection method combining traditional statistics and machine learning-based methods. The accuracies of the 4-IHC classification and triple negative (TN) vs. non-TN cancers were assessed. Results: For the 4-IHC classification task, the best accuracy of 72.4% was achieved based on linear discriminant analysis (LDA) or subspace discrimination of assembled learning in conjunction with 20 selected features, and only small dependent emphasis of Kendall-tau-b for sequential features, based on the DWIsequential with the LDA model, yielding an accuracy of 53.7%. The linear support vector machine (SVM) and medium k-nearest neighbor using eight features yielded the highest accuracy of 91.0% for comparing TN to non-TN cancers, and the maximum variance for DWIsequential alone, together with a linear SVM model, achieved an accuracy of 83.6%. Conclusions: Whole-tumor radiomics on MR multiparametric images, DCE images changing over time points, and DWI images changing over different b-values provide a non-invasive analytical approach for breast cancer subtype classification and TN cancer identification.

Measurement of Tumor Pressure and Strategies of Imaging Tumor Pressure for Radioimmunotherapy

Tumor interstitial pressure is a fundamental feature of cancer biology. Elevation in tumor pressure affects the efficacy of cancer treatment and results in the heterogenous intratumoral distribution of drugs and macromolecules. Monoclonal antibodies (mAb) play a prominent role in cancer therapy and molecular nuclear imaging. Therapy using mAb labeled with radionuclides—also known as radioimmunotherapy (RIT)—is an effective form of cancer treatment. RIT is clinically effective for the treatment of lymphoma and other blood cancers; however, its clinical use for solid tumor was limited because their high interstitial pressure prevents mAb from penetrating into the tumor. This pressure can be decreased using anti-cancer drugs or additional external therapy. In this paper, we reviewed the intratumoral pressure using direct tumor-pressure measurement strategies, such as the wick-in-needle and pressure catheter transducer method, and indirect tumor-pressure measurement strategies via magnetic resonance.

Contrast enhanced digital mammography versus magnetic resonance imaging for accurate measurement of the size of breast cancer

OBJECTIVE

To compare the accuracy of contrast-enhanced digital mammography (CEDM) and MRI, including maximal intensity projection (MIP) images, for measuring the tumour size of breast cancer.

METHODS

We included 52 females (mean age, 50.9 years) with surgery due to breast cancer, and measured maximum diameter of main mass on mediolateral oblique (MLO) and craniocaudal (CC) views of mammography and CEDM; sagittal, axial MIP images, and early dynamic contrast-enhanced MRI (CEMRI) before surgery. Bland-Altman plot, intraclass correlation coefficient, and univariate linear regression analysis were used to evaluate the maximum size between imaging and pathology including only invasive component (OP_invasive) or with carcinoma in situ (OP_max).

RESULTS

Mean OP_invasive was 15.5 mm, and overestimation rate was similar or higher than underestimation rate on all images except CC view of mammography and axial MIP image of CEDM. Mean OP_max was 21.7 mm, and underestimation rate was higher than the overestimation rate. All parameters of CEDM and CEMRI showed good agreement ( k > 0.75) with OP_invasive, with the most favourable result being the CC view of CEDM and axial MIP image of CEMRI.

CONCLUSION

All views of CEDM and MRI provided accurate measurements of tumour size. Axial plane CEDM and MRI would be the first choice for image review and treatment planning, with the highest accuracy obtained by using CC view of CEDM.

ADVANCES IN KNOWLEDGE

Previous studies have not compared the measurement of the tumour size using detailed sequences; in our study, we discovered that CEDM can be an alternative modality to CEMRI.

A nomogram for individual prediction of vascular invasion in primary breast cancer

OBJECTIVES

To explore the feasibility of preoperative prediction of vascular invasion (VI) in breast cancer patients using nomogram based on multiparametric MRI and pathological reports.

METHODS

We retrospectively collected 200 patients with confirmed breast cancer between January 2016 and January 2018. All patients underwent MRI examinations before the surgery. VI was identified by postoperative pathology. The 200 patients were randomly divided into training (n = 100) and validation datasets (n = 100) at a ratio of 1:1. Least absolute shrinkage and selection operator (LASSO) regression was used to select predictors most associated with VI of breast cancer. A nomogram was constructed to calculate the area under the curve (AUC) of receiver operating characteristics, sensitivity, specificity, accuracy, positive prediction value (PPV) and negative prediction value (NPV). We bootstrapped the data for 2000 times without setting the random seed to obtain corrected results.

RESULTS

VI was observed in 79 patients (39.5%). LASSO selected 10 predictors associated with VI. In the training dataset, the AUC for nomogram was 0.94 (95% confidence interval [CI]: 0.89-0.99, the sensitivity was 78.9% (95%CI: 72.4%-89.1%), the specificity was 95.3% (95%CI: 89.1%-100.0%), the accuracy was 86.0% (95%CI: 82.0%-92.0%), the PPV was 95.7% (95%CI: 90.0%-100.0%), and the NPV was 77.4% (95%CI: 67.8%-87.0%). In the validation dataset, the AUC for nomogram was 0.89 (95%CI: 0.83-0.95), the sensitivity was 70.3% (95%CI: 60.7%-79.2%), the specificity was 88.9% (95%CI: 80.0%-97.1%), the accuracy was 77.0% (95%CI: 70.0%-83.0%), the PPV was 91.8% (95%CI: 85.3%-98.0%), and the NPV was 62.7% (95%CI: 51.7%-74.0%). The nomogram calibration curve shows good agreement between the predicted probability and the actual probability.

CONCLUSION

The proposed nomogram could be used to predict VI in breast cancer patients, which was helpful for clinical decision-making.

Differentiation of breast tuberculosis and breast cancer using diffusion-weighted, T2-weighted and dynamic contrast-enhanced magnetic resonance imaging

Background

The use of multi-parametric magnetic resonance imaging (MRI) in the evaluation of breast tuberculosis (BTB).

Objectives

To evaluate the value of diffusion-weighted imaging (DWI), T2-weighted (T2W) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in differentiating breast cancer (BCA) from BTB.

Method

We retrospectively studied images of 17 patients with BCA who had undergone pre-operative MRI and 6 patients with pathologically proven BTB who underwent DCE-MRI during January 2014 to January 2015.

Results

All patients were female, with the age range of BTB patients being 23–43 years and the BCA patients being 31–74 years. Breast cancer patients had a statistically significant lower mean apparent diffusion coefficient (ADC) value (1072.10 ± 365.14), compared to the BTB group (1690.77 ± 624.05, p = 0.006). The mean T2-weighted signal intensity (T2SI) was lower for the BCA group (521.56 ± 233.73) than the BTB group (787.74 ± 196.04, p = 0.020). An ADC mean cut-off value of 1558.79 yielded 66% sensitivity and 94% specificity, whilst the T2SI cut-off value of 790.20 yielded 83% sensitivity and 83% specificity for differentiating between BTB and BCA. The homogeneous internal enhancement for focal mass was seen in BCA patients only.

Conclusion

Multi-parametric MRI incorporating the DWI, T2W and DCE-MRI may be a useful tool to differentiate BCA from BTB.

Contrast-enhanced and unenhanced diffusion-weighted imaging of the breast at 3 T

AIM

To evaluate the effect of intravenous gadolinium contrast agent on diffusion-weighted sequences and apparent diffusion coefficient (ADC) measurements at 3 T.

MATERIALS AND METHODS

Sixty-two biopsy-proven breast lesions were included in this prospective study. Magnetic resonance imaging (MRI) was performed at 3 T, using four echo-planar diffusion-weighted sequences (7,100 ms repetition time, 84 ms echo time) with b-values of 0 and 850, and 0 and 1,000 s/mm². The first pair of DWI sequences was taken before intravenous contrast medium injection. The second pair of sequences was taken 6.5 minutes after intravenous contrast medium administration (right after the dynamic T1 sequence). A freeform region of interest (ROI) was drawn inside the lesion excluding adjacent normal tissue, necrotic, or cystic components and ADC values were calculated. The paired samples t-test was used to assess differences between ADC measurements before and after intravenous contrast medium administration. Sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve were calculated for each diffusion sequence.

RESULTS

Twenty-seven malignant and 35 benign lesions were analysed. Fifty-eight lesions were masses, and four lesions were non-mass-like enhancements (NMLEs). Two of the NMLEs were malignant, and two were benign lesions. The contrast-enhanced ADC measurements were lower than the unenhanced measurements on b=850 and 1,000 s/mm² (p<0.05). The receiver operating characteristic (ROC) analysis displayed similar area under the curve values between the different diffusion sequences.

CONCLUSION

The injection of intravenous contrast medium reduces ADC values; however, the effect of contrast medium is modest. Sensitivity and specificity are not significantly affected.

Diffusion-weighted breast imaging

Magnetic resonance imaging (MRI) of the breast represents one of the most sensitive imaging modalities in breast cancer detection. Diffusion-weighted imaging (DWI) is a sequence variation introduced as a complementary MRI technique that relies on mapping the diffusion process of water molecules thereby providing additional information about the underlying tissue. Since water diffusion is more restricted in most malignant tumors than in benign ones owing to the higher cellularity of the rapidly proliferating neoplasia, DWI has the potential to contribute to the identification and characterization of suspicious breast lesions. Thus, DWI might increase the diagnostic accuracy of breast MRI and its clinical value. Future applications including optimized DWI sequences, technical developments in MR devices, and the application of radiomics/artificial intelligence algorithms may expand the potential of DWI in breast imaging beyond its current supplementary role.

Variability of non-Gaussian diffusion MRI and intravoxel incoherent motion (IVIM) measurements in the breast

We prospectively examined the variability of non-Gaussian diffusion magnetic resonance imaging (MRI) and intravoxel incoherent motion (IVIM) measurements with different numbers of b-values and excitations in normal breast tissue and breast lesions. Thirteen volunteers and fourteen patients with breast lesions (seven malignant, eight benign; one patient had bilateral lesions) were recruited in this prospective study (approved by the Internal Review Board). Diffusion-weighted MRI was performed with 16 b-values (0–2500 s/mm² with one number of excitations [NEX]) and five b-values (0–2500 s/mm², 3 NEX), using a 3T breast MRI. Intravoxel incoherent motion (flowing blood volume fraction [fIVIM] and pseudodiffusion coefficient [D*]) and non-Gaussian diffusion (theoretical apparent diffusion coefficient [ADC] at b value of 0 sec/mm² [ADC₀] and kurtosis [K]) parameters were estimated from IVIM and Kurtosis models using 16 b-values, and synthetic apparent diffusion coefficient (sADC) values were obtained from two key b-values. The variabilities between and within subjects and between different diffusion acquisition methods were estimated. There were no statistical differences in ADC₀, K, or sADC values between the different b-values or NEX. A good agreement of diffusion parameters was observed between 16 b-values (one NEX), five b-values (one NEX), and five b-values (three NEX) in normal breast tissue or breast lesions. Insufficient agreement was observed for IVIM parameters. There were no statistical differences in the non-Gaussian diffusion MRI estimated values obtained from a different number of b-values or excitations in normal breast tissue or breast lesions. These data suggest that a limited MRI protocol using a few b-values might be relevant in a clinical setting for the estimation of non-Gaussian diffusion MRI parameters in normal breast tissue and breast lesions.

Stromal fibrosis: imaging features with diagnostic contribution of diffusion-weighted MRI

OBJECTIVE

To describe magnetic resonance imaging (MRI) and ultrasonography findings of breast stromal fibrosis (SF) and compare apparent diffusion coefficient (ADC) stromal fibrosis values with breast cancer and normal parenchyma.

METHODS

45 patients (ages 22‒74) with histopathologically proven SF who underwent MRI were included in this study. Their MRI and ultrasonography features were examined and categorized. The mean ADC values for SF, contralateral normal parenchyma, and breast malignancy of the control group values were calculated and compared among each other.

RESULTS

The vast majority of SF on sonography showed features suggestive of malignancy: (1) irregular in shape 25/45 (55%); (2) indistinct in margin 27/45 (60%); and (3) hypoechoic 39/45 (87%) with posterior acoustic shadowing 11/45 (24%). An SF MRI showed a mass in 12/45 (26%) and non-mass enhancement in 33/45 (74%), mostly with irregular (8/12; 67%) shape. Non-mass lesions showed heterogeneous (12/33), clumped (9/33), and homogenous (9/33) enhancement. The initial SF contrast uptake rate varied between slow (57%), rapid (22%), and medium (21%). Delayed SF enhancement may be persistent (66%) or plateau (34%). Small cysts were located around/near 21 (47%) of lesions. Ductal ectasia was found in 14 (31%) of all patients. Mean ADCs of parenchyma, SF, and malignancy were 1.32 ± 0.32, 1.23 ± 0.25, and 0.99 ± 0.24 × 10^-3 mm² sec^-1, respectively.

CONCLUSION

SF often mimics breast carcinoma on imaging and leads the radiology‒pathology disagreement. In terms of distinguishing SF from malignancy, ADC could be a significant and promising value in diffusion-weighted MRI along with conventional sequences. Slow initial uptake with delayed persistent contrast enhancement in a non-mass lesion with relatively higher ADC values are very helpful for differentiating SF from malignancy. The presence of small cysts and ductal ectasia were common findings around/near the SF. Advances in knowledge: A quantitative analysis for measuring ADC values along with additional MRI features can be very helpful in distinguishing SF from malignant lesions.

Retrospective analysis of the utility of multiparametric MRI for differentiating between benign and malignant breast lesions in women in China

We explored the utility of time-resolved angiography with interleaved stochastic trajectories dynamic contrast-enhanced magnetic resonance imaging (TWIST DCE-MRI), readout segmentation of long variable echo-trains diffusion-weighted magnetic resonance imaging- diffusion-weighted magnetic resonance imaging (RESOLVE-DWI), and echo-planar imaging- diffusion-weighted magnetic resonance imaging (EPI-DWI) for distinguishing between malignant and benign breast lesions.This retrospective analysis included female patients with breast lesions seen at a single center in China between January 2016 and April 2016. Patients were allocated to a benign or malignant group based on pathologic diagnosis. All patients received routine MRI, RESOLVE-DWI, EPI-DWI, and TWIST DCE-T1WI. Variables measured included quantitative parameters (K, Kep, and Ve), semiquantitative parameters (rate of contrast enhancement for contrast agent inflow [W-in], rate of contrast decay for contrast agent outflow [W-out], and time-to-peak enhancement after contrast agent injection [TTP]) and apparent diffusion coefficient (ADC) values for RESOLVE-DWI (ADCr) and EPI-DWI (ADCe). Receiver-operating characteristic (ROC) curve analysis was used to evaluate the diagnostic utility of each parameter for differentiating malignant from benign breast lesions.A total of 87 patients were included (benign, n = 20; malignant, n = 67). Compared with the benign group, the malignant group had significantly higher K, Kep and W-in and significantly lower W-out, TTP, ADCe, and ADCr (all P < .05); Ve was not significantly different between groups. RESOLVE-DWI was superior to conventional EPI-DWI at illustrating lesion boundary and morphology, while ADCr was significantly lower than ADCe in all patients. Kep, W-out, ADCr, and ADCe showed the highest diagnostic efficiency (based on AUC value) for differentiating between benign and malignant lesions. Combining 3 parameters (Kep, W-out, and ADCr) had a higher diagnostic efficiency (AUC, 0.965) than any individual parameter and distinguished between benign and malignant lesions with high sensitivity (91.0%), specificity (95.0%), and accuracy (91.9%).An index combining Kep, W-out, and ADCr could potentially be used for the differential diagnosis of breast lesions.

Reproducibility of apparent diffusion coefficient measurements evaluated with different workstations

AIM

To evaluate apparent diffusion coefficient (ADC) measurements of breast lesions on different computer platforms to address post-processing influences on ADC measurement reproducibility.

MATERIALS AND METHODS

One hundred biopsy-proven breast lesions were included in this prospective study. MRI examination was performed at 3 T using standard sequences and an echo planar diffusion-weighted imaging sequence with b-values of 0 and 850 s/mm². The images were reviewed by two radiologists in consensus. Regions of interest were placed manually within the lesion, following its contour. Care was taken to exclude adjacent normal tissue or necrotic tissue and cystic components within the lesion. The mean ADC value was measured for each lesion on two different platforms: On the MRI workstation that came with the scanner and on a commercially available DICOM (digital imaging and communication in medicine) viewer. Agreement between workstation measurements was evaluated using intraclass correlation coefficient and Bland-Altman plots.

RESULTS

Fifty-nine malignant and 41 benign lesions were analysed. Of the benign lesions, 28 were mass lesions and 13 were non-mass-like enhancements. In addition, 46 of the malignant lesions were masses and 13 were non-mass-like enhancements. Agreement between the two workstation measurements was high (intraclass correlation coefficients=0.981). Using Bland-Altman plots, no systematic differences were identified between workstations. Limits of agreement ranged between a minimum of -0.071×10^-3 mm²/s and a maximum of 0.102×10^-3 mm²/s.

CONCLUSION

ADC measurements are reproducible among the workstations considered in this study.

Short tau inversion recovery in breast diffusion-weighted imaging: signal-to-noise ratio and apparent diffusion coefficients using a breast phantom in comparison with spectral attenuated inversion recovery

OBJECTIVE

This study aimed to compare the signal-to-noise ratios (SNRs) and apparent diffusion coefficients (ADCs) obtained using two fat suppression techniques in breast diffusion-weighted imaging (DWI) of a phantom.

MATERIALS AND METHODS

The breast phantom comprised agar gels with four different concentrations of granulated sugar (samples 1, 2, 3, and 4). DWI with short tau inversion recovery (STIR-DWI) and that with spectral attenuated inversion recovery (SPAIR-DWI) were performed using 3.0-T magnetic resonance imaging, and the obtained SNRs and ADCs were compared. ADCs were also compared between the right and left breast phantoms.

RESULTS

For samples 3 and 4, SNRs obtained using STIR-DWI were lower than those obtained using SPAIR-DWI. For samples 2, 3, and 4, overall ADCs obtained using STIR-DWI were significantly higher than those obtained using SPAIR-DWI (p < 0.001 for all), although no significant difference was observed for sample 1 (p = 0.62). STIR-DWI shows a positive bias and wide limits of agreement in Bland-Altman plot. The coefficients of variance of overall ADCs were good in STIR-DWI and SPAIR-DWI. For all samples, STIR-DWI demonstrated slightly larger percentage differences in ADCs between the right and left phantoms than SPAIR-DWI.

CONCLUSION

SNRs and ADCs obtained using STIR-DWI are influenced by the T ₁ value; a shorter T ₁ value decreases SNRs, overestimates ADCs, and induces the measurement error in ADCs. STIR-DWI showed a larger difference in ADCs between the right and left phantoms than SPAIR-DWI.

The influence of diffusion gradient direction on diffusion-weighted imaging of breast mass-like lesions at 3.0T

Background It has been challenging to achieve ideal breast diffusion-weighted imaging (DWI). The optimization of diffusion gradient direction is of great importance. Purpose To evaluate the effect of diffusion gradient direction on the apparent diffusion coefficient (ADC) values of breast mass-like lesions and the visual grades of image quality, lesion visibility, and sharpness of breast contour at 3.0T. Material and Methods Sixty consecutive patients with mass-like lesions were enrolled in this study. In addition to typical breast magnetic resonance imaging (MRI) protocols, the breasts were scanned with conventional orthogonal DWI (c-DWI), tetrahedral DWI (t-DWI), and 3in1 DWI (3in1-DWI) sequences. The DW images were observed and visually graded by two radiologists independently. For ADC measurement, one radiographer manually selected the region of interest (ROI). Results For both readers, t-DWI had better image quality and sharpness of breast contour than c-DWI. Regarding lesion visibility, no significant differences were observed among three sequences. The mean ADC values were 1.462 × 10^-3, 1.490 × 10^-3, and 1.446 × 10^-3 mm² s^-1 for c-DWI, t-DWI, and 3in1-DWI, respectively. The ADC values extracted from both t-DWI and 3in1-DWI were not statistically different compared with those from c-DWI. In all DWI sequences, the ADC of malignant lesions was significantly reduced compared with benign lesions. Conclusion DWI with tetrahedral or 3in1 diffusion gradients is a more useful technique in clinical breast MRI than c-DWI because the image quality and sharpness of breast contour are improved. ADC is comparable to c-DWI.

Diffusion-weighted imaging of suspicious (BI-RADS 4) breast lesions: stratification based on histopathology

Objective:

To test the use of diffusion-weighted imaging (DWI) in stratifying suspicious breast lesions (BI-RADS 4), correlating them with histopathology. We also investigated the performance of DWI related to the main enhancement patterns (mass and non-mass) and tested its reproducibility.

Materials and Methods:

Seventy-six patients presented 92 lesions during the sampling period. Two independent examiners reviewed magnetic resonance imaging studies, described the lesions, and determined the apparent diffusion coefficient (ADC) values. Differences among benign, indeterminate- to high-risk, and malignant findings, in terms of the ADCs, were assessed by analysis of variance. Using receiver operating characteristic (ROC) curves, we compared the performance of ADC values in masses and non-mass lesions, and tested the reproducibility of measurements by determining the coefficient of variation and smallest real difference.

Results:

Among the 92 lesions evaluated, the histopathology showed that 37 were benign, 11 were indeterminate- to high-risk, and 44 were malignant. The mean ADC differed significantly among those histopathological groups, the value obtained for the malignant lesions (1.10 × 10^-3 mm²/s) being significantly lower than that obtained for the other groups (p < 0.001). ROC curves demonstrated that DWI performed better when applied to masses than when applied to non-mass lesions (area under the curve, 0.88 vs. 0.67). Reproducibility was good (coefficient of variation, 7.03%; and smallest real difference, ± 0.242 × 10^-3 mm²/s).

Conclusion:

DWI can differentiate between malignant and nonmalignant (benign or indeterminate- to high-risk) lesions, showing better performance for masses. Nevertheless, stratification based on histopathological criteria that are more refined has yet to be achieved.

On a fractional order calculus model in diffusion weighted breast imaging to differentiate between malignant and benign breast lesions detected on X-ray screening mammography

OBJECTIVE

To evaluate a fractional order calculus (FROC) model in diffusion weighted imaging to differentiate between malignant and benign breast lesions in breast cancer screening work-up using recently introduced parameters (βFROC, DFROC and μFROC).

MATERIALS AND METHODS

This retrospective analysis within a prospective IRB-approved study included 51 participants (mean 58.4 years) after written informed consent. All patients had suspicious screening mammograms and indication for biopsy. Prior to biopsy, full diagnostic contrast-enhanced MRI examination was acquired including diffusion-weighted-imaging (DWI, b = 0,100,750,1500 s/mm2). Conventional apparent diffusion coefficient Dapp and FROC parameters (βFROC, DFROC and μFROC) as suggested further indicators of diffusivity components were measured in benign and malignant lesions. Receiver operating characteristics (ROC) were calculated to evaluate the diagnostic performance of the parameters.

RESULTS

29/51 patients histopathologically revealed malignant lesions. The analysis revealed an AUC for Dapp of 0.89 (95% CI 0.80-0.98). For FROC derived parameters, AUC was 0.75 (0.60-0.89) for DFROC, 0.59 (0.43-0.75) for βFROC and 0.59 (0.42-0.77) for μFROC. Comparison of the AUC curves revealed a significantly higher AUC of Dapp compared to the FROC parameters DFROC (p = 0.009), βFROC (p = 0.003) and μFROC (p = 0.001).

CONCLUSION

In contrast to recent description in brain tumors, the apparent diffusion coefficient Dapp showed a significantly higher AUC than the recently proposed FROC parameters βFROC, DFROC and μFROC for differentiating between malignant and benign breast lesions. This might be related to the intrinsic high heterogeneity within breast tissue or to the lower maximal b-value used in our study.

Diffusion imaging of the vertebral bone marrow

Diffusion-weighted MRI (DWI) of the vertebral bone marrow is a clinically important tool for the characterization of bone-marrow pathologies and, in particular, for the differentiation of benign (osteoporotic) and malignant vertebral compression fractures. DWI of the vertebral bone marrow is, however, complicated by some unique MR and tissue properties of vertebral bone marrow. Due to both the spongy microstructure of the trabecular bone and the proximity of the lungs, soft tissue, or large vessels, substantial magnetic susceptibility variations occur, which severely reduce the magnetic field homogeneity as well as the transverse relaxation time T^*₂ , and thus complicate MRI in particular with echoplanar imaging (EPI) techniques. Therefore, alternative diffusion-weighting pulse sequence types such as single-shot fast-spin-echo sequences or segmented EPI techniques became important alternatives for quantitative DWI of the vertebral bone marrow. This review first describes pulse sequence types that are particularly important for DWI of the vertebral bone marrow. Then, data from 24 studies that made diffusion measurements of normal vertebral bone marrow are reviewed; summarizing all results, the apparent diffusion coefficient (ADC) of normal vertebral bone marrow is typically found to be between 0.2 and 0.6 × 10^-3  mm² /s. Finally, DWI of vertebral compression fractures is discussed. Numerous studies demonstrate significantly greater ADCs in osteoporotic fractures (typically between 1.2 and 2.0 × 10^-3  mm² /s) than in malignant fractures or lesions (typically 0.7-1.3 × 10^-3  mm² /s). Alternatively, several studies used the (qualitative) image contrast of diffusion-weighted acquisitions for differentiation of lesion etiology: a very good lesion differentiation can be achieved, particularly with diffusion-weighted steady-state free precession sequences, which depict malignant lesions as hyperintense relative to normal-appearing vertebral bone marrow, in contrast to hypointense or isointense osteoporotic lesions. Copyright © 2015 John Wiley & Sons, Ltd.

[Selected clinically established and scientific techniques of diffusion-weighted MRI. In the context of imaging in oncology]

BACKGROUND

Diffusion-weighted imaging (DWI) is a magnetic resonance imaging (MRI) technique that was established in the clinical routine primarily for the detection of brain ischemia. In the past 15 years its clinical use has been extended to oncological radiology, as tumor and metastases can be depicted in DWI due to their hypercellular nature.

PRINCIPLES

The basis of DWI is the Stejskal-Tanner experiment. The diffusion properties of tissue can be visualized after acquisition of at least two diffusion-weighted series using echo planar imaging and a specific sequence of gradient pulses.

CLINICAL APPLICATIONS

The use of DWI in prostate MRI was reported to be one of the first established applications that found its way into internationally recognized clinical guidelines of the European Society of Urological Radiology (ESUR) and the prostate imaging reporting and data system (PI-RADS) scale. Due to recently reported high specificity and negative predictive values of 94% and 92%, respectively, its regular use for breast MRI is expected in the near future. Furthermore, DWI can also reliably be used for whole-body imaging in patients with multiple myeloma or for measuring the extent of bone metastases.

OUTLOOK

New techniques in DWI, such as intravoxel incoherent motion imaging, diffusion kurtosis imaging and histogram-based analyses represent promising approaches to achieve a more quantitative evaluation for tumor detection and therapy response.

The potential of multiparametric MRI of the breast

MRI is an essential tool in breast imaging, with multiple established indications. Dynamic contrast-enhanced MRI (DCE-MRI) is the backbone of any breast MRI protocol and has an excellent sensitivity and good specificity for breast cancer diagnosis. DCE-MRI provides high-resolution morphological information, as well as some functional information about neoangiogenesis as a tumour-specific feature. To overcome limitations in specificity, several other functional MRI parameters have been investigated and the application of these combined parameters is defined as multiparametric MRI (mpMRI) of the breast. MpMRI of the breast can be performed at different field strengths (1.5-7 T) and includes both established (diffusion-weighted imaging, MR spectroscopic imaging) and novel MRI parameters (sodium imaging, chemical exchange saturation transfer imaging, blood oxygen level-dependent MRI), as well as hybrid imaging with positron emission tomography (PET)/MRI and different radiotracers. Available data suggest that multiparametric imaging using different functional MRI and PET parameters can provide detailed information about the underlying oncogenic processes of cancer development and progression and can provide additional specificity. This article will review the current and emerging functional parameters for mpMRI of the breast for improved diagnostic accuracy in breast cancer.

Effects of magnetic field strength and b value on the sensitivity and specificity of quantitative breast diffusion-weighted MRI

BACKGROUND

To evaluate the effect of b value or the magnetic field strength (B0) on the sensitivity and specificity of quantitative breast diffusion-weighted imaging (DWI).

METHODS

A total of 126 patients underwent clinical breast MRI that included pre-contrast DWI imaging using b values of both 1,000 and 1,500 s/mm² at either 1.5 T (n=86) or 3.0 T (n=40). Quantitative apparent diffusion coefficients (ADC) were measured and compared for 18 benign, 33 malignant lesions, and 126 normal breast tissues. Optimal ADCmean threshold for differentiating benign and malignant lesions was estimated and the effect of b values and B0 were examined using a generalized estimating equations (GEE) model.

RESULTS

The optimal ADCmean threshold was 1.235×10^-3 mm²/s for b value of 1,000 and 0.934×10^-3 mm²/s for b value of 1,500. Using these thresholds, the sensitivities and specificities were 96% and 89% (b value =1,000, B0 =1.5 T), 89% and 98% (b value =1,000, B0 =3.0 T), 88% and 96% (b value =1,500, B0 =1.5 T), and 67% and 100% (b value =1,500, B0 =3.0 T). No significant difference was found between different B0 (P=0.26) or b values (P=0.28).

CONCLUSIONS

Better sensitivity is achieved with DWI of b value =1,000 than with b value =1,500. However, b value and B0 do not significantly impact diagnostic performance of DWI when using appropriate thresholds.

Analysis of factors influencing the degree of detectability on diffusion-weighted MRI and diffusion background signals in patients with invasive breast cancer

To determine the factors influencing the degree of detectability of lesions and diffusion background signals on magnetic resonance diffusion-weighted imaging (DWI) in invasive breast cancer.Institutional review board approval was obtained and patient consent was waived. Patients with newly diagnosed invasive ductal carcinoma, who underwent preoperative breast magnetic resonance imaging with DWI were included in this study (n = 167). Lesion detectability on DWI and contrast-enhanced subtracted T1-weighted images, the degree of background parenchymal enhancement (BPE), and diffusion background signal were qualitatively rated. Detectability of lesions on DWI was compared with clinicopathological findings including menopausal status, mammographic density, and molecular subtype of breast cancer. Multivariate linear regression analysis was performed to determine variables independently associated with detectability of lesions on DWI and diffusion background signals.Univariate analysis showed that the detectability of lesions on DWI was significantly associated with lesion size (P = 0.001), diffuse background signal (P < 0.0001), and higher detectability scores for contrast-enhanced T1-weighted subtraction images (P = 0.000). The degree of diffusion background signal was significantly affected by age (P < 0.0001), BPE (P < 0.0001), mammographic density (P = 0.002), and menopausal status (P < 0.0001). On multivariate analysis, the diffusion background signal (P < 0.0001) and histologic grade (P < 0.0001) were correlated with the detectability on DWI of invasive breast cancer. Only BPE was correlated with the amount of diffusion background signal on DWI (P < 0.0001).For invasive breast cancers, detectability on DWI was significantly affected by the diffusion background signal. BPE, menopausal status, menstrual cycle, or mammographic density did not show statistically significant correlation with the diffusion detectability of lesions on DWI.

Independent value of image fusion in unenhanced breast MRI using diffusion-weighted and morphological T2-weighted images for lesion characterization in patients with recently detected BI-RADS 4/5 x-ray mammography findings

OBJECTIVES

The aim of this study was to evaluate the accuracy and applicability of solitarily reading fused image series of T2-weighted and high-b-value diffusion-weighted sequences for lesion characterization as compared to sequential or combined image analysis of these unenhanced sequences and to contrast- enhanced breast MRI.

METHODS

This IRB-approved study included 50 female participants with suspicious breast lesions detected in screening X-ray mammograms, all of which provided written informed consent. Prior to biopsy, all women underwent MRI including diffusion-weighted imaging (DWIBS, b = 1500s/mm²). Images were analyzed as follows: prospective image fusion of DWIBS and T2-weighted images (FU), side-by-side analysis of DWIBS and T2-weighted series (CO), combination of the first two methods (CO+FU), and full contrast-enhanced diagnostic protocol (FDP). Diagnostic indices, confidence, and image quality of the protocols were compared by two blinded readers.

RESULTS

Reading the CO+FU (accuracy 0.92; NPV 96.1 %; PPV 87.6 %) and the CO series (0.90; 96.1 %; 83.7 %) provided a diagnostic performance similar to the FDP (0.95; 96.1 %; 91.3 %; p > 0.05). FU reading alone significantly reduced the diagnostic accuracy (0.82; 93.3 %; 73.4 %; p = 0.023).

CONCLUSIONS

MR evaluation of suspicious BI-RADS 4 and 5 lesions detected on mammography by using a non-contrast-enhanced T2-weighted and DWIBS sequence protocol is most accurate if MR images were read using the CO+FU protocol.

KEY POINTS

• Unenhanced breast MRI with additional DWIBS/T2w-image fusion allows reliable lesion characterization. • Abbreviated reading of fused DWIBS/T2w-images alone decreases diagnostic confidence and accuracy. • Reading fused DWIBS/T2w-images as the sole diagnostic method should be avoided.

Diffusion Weighted MR Imaging of Breast and Correlation of Prognostic Factors in Breast Cancer

BACKGROUND

Through Diffusion Weighted Imaging (DWI), information related to early molecular changes, changes in the permeability of cell membranes, and early morphologic and physiologic changes such as cell swelling can be obtained.

AIMS

We investigated the correlation between the prognostic factors of breast cancer and apparent diffusion coefficient (ADC) in DWI sequences of malignant lesions.

STUDY DESIGN

Retrospective cross-sectional study.

METHODS

Patients who were referred to our clinic between September 2012 and September 2013, who underwent dynamic breast MRI before or after biopsy and whose biopsy results were determined as malignant, were included in our study. Before the dynamic analysis, DWI sequences were taken. ADC relationship with all prognostic factors was investigated. Pearson correlation test was used to compare the numerical data, while Spearman correlation and Fisher exact tests were used to compare the categorical data. The advanced relationships were evaluated with linear regression analysis and univariate analysis. The efficiency of the parameters was evaluated using ROC analysis. The significance level (P) was accepted as 0.05.

RESULTS

In total, 41 female patients with an average age of 49.4 years (age interval 21-77) and 44 lesions were included into the study. In the Pearson correlation test, no statistically significant difference was determined between ADC and the patient's age and tumor size. In the Spearman correlation test, a statistically significant difference was determined between nuclear grade (NG) and ADC (r=-0.424, p=0.04); no statistically significant correlation was observed between the other prognostic factors with each other and ADC values. In the linear regression analysis, the relationship of NG with ADC was found to be more significant alone than when comparing all parameters (corrected r2=0.196, p=0.005). Further evaluations between the NG and ADC correlation were carried out with ROC analysis. A statistically significant difference was determined when NG 1 separately was compared with NG 2 and 3 (p=0.03). A statistically significant difference was also determined (p=0.05) in the comparison of NG 1 with only NG 3. No statistically significant difference was determined when NG 2 separately was compared with NG 1 and NG 3 and when NG 3 separately was compared with NG 1 and 2 (p=0.431, p=0.097).

CONCLUSION

We found that ADC values obtained by breast DWI showed a higher correlation with the NG of breast cancer, which is an important factor in the patient's treatment. Predictions can be made about NG by analyzing the ADC values. Additional studies are needed, however, and the ADC value of the lesion can be used as a prognostic factor proving the aggressiveness.

Significance of the ADC ratio in the differential diagnosis of breast lesions

BACKGROUND

Magnetic resonance imaging (MRI) has high sensitivity but low specificity for breast cancer, and consequently, new techniques to improve the specificity of breast MRI in diagnosing breast cancer are under development.

PURPOSE

To assess the ability of the apparent diffusion coefficient (ADC) compared with the ADC ratio (ADCr) to differentially diagnose benign compared with malignant breast lesions.

MATERIAL AND METHODS

Forty-eight women with breast lesions (average age, 45 years) underwent MRI scanning including T1-weighted dynamic contrast-enhanced (DCE) scanning and diffusion-weighted imaging (DWI). The average ADC and ADCr values for both lesions and pectoralis major muscles (ADCrmuscle and ADCrmuscle) were measured in patients with malignant (n = 25) and benign (n = 23) breast lesions. The ADCr of the contralateral breast (ADCr contralateral) was also evaluated. All histology was confirmed by pathological analysis of biopsied tissue. ADC and ADCr values were analyzed using receiver-operating characteristic (ROC) curves.

RESULTS

For benign lesions compared with malignant lesions, lesion-side ADC was 1.45 vs. 1.05, respectively (P < 0.001), normal-side ADC was 1.82 vs.1.64 (P = 0.002), ADCrmuscle was 1.35 vs. 0.9 (P < 0.001), and ADCrcontralateral was 0.79 vs. 0.64 (P = 0.001). ADCrmuscle showed higher sensitivity (82.61%) and specificity (96.00%) than ADCrcontralateral (60.87% and 92.00%, respectively) and ADC (69.57% and 96.00%) for discriminating malignant from benign lesions. The AUC using ADCrmuscle had higher discriminatory power (0.92, P < 0.001) for malignant versus benign breast lesions compared with either ADC (0.82, P < 0.001) or ADCrcontralateral (0.78, P = 0.001).

CONCLUSION

The ADCrmuscle value showed higher sensitivity and specificity and improved diagnostic accuracy compared with either ADC or ADCrcontralateral in differentiating benign from malignant breast lesions.

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.

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.

Assessment of axillary lymph nodes in patients with breast cancer with diffusion-weighted MR imaging in combination with routine and dynamic contrast MR imaging

PURPOSE

To assess axillary lymph nodes in patients with breast cancer with diffusion-weighted MR imaging in combination with routine and dynamic contrast MR imaging.

MATERIALS AND METHODS

Prospective study was conducted on 65 enlarged axillary lymph nodes in 34 consecutive female patients (28-64 years: mean 51 years) with breast cancer. They underwent T2-weighted, dynamic contrast-enhanced and diffusion-weighted MR imaging of the breast and axilla using a single-shot echo-planar imaging with a b factor of 0500 and 1000 s/mm². Morphologic and quantitative parameters included ADC value of the axillary lymph node which was calculated and correlated with surgical findings.

RESULTS

The mean ADC value of metastatic axillary lymph nodes was 1.08 ± 0.21 × 10⁻³ mm²/s and of benign lymph nodes was 1.58 ± 0.14 × 10⁻³ mm²s. There was statistically difference in mean ADC values between metastatic and of benign axillary lymph nodes (P = 0.001). Metastatic nodes were associated with low ADC ≤ 1.3 (OR = 8.0), short axis/long axis (TS/LS) > 0.6 (OR = 7.0) and absent hilum (OR = 6.21). When ADC of 1.3 × 10⁻³ mm²/s was used as a threshold value for differentiating metastatic from benign axillary lymph nodes, the best result was obtained with an accuracy of 95.6%, sensitivity of 93%, specificity of 100%, positive predictive value of 100 %, negative predictive value of 87.5 % and area under the curve of 0.974. Multivariate model involving combined ADC value and TS/LS improved the diagnostic performance of MR imaging with AUC of 1.00.

CONCLUSION

We concluded that combination of diffusion-weighted MR imaging with morphological and dynamic MR imaging findings helps for differentiation of metastatic from benign axillary lymph nodes.