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

Deep Learning k-Space-to-Image Reconstruction Facilitates High Spatial Resolution and Scan Time Reduction in Diffusion-Weighted Imaging Breast MRI

BACKGROUND

For time-consuming diffusion-weighted imaging (DWI) of the breast, deep learning-based imaging acceleration appears particularly promising.

PURPOSE

To investigate a combined k-space-to-image reconstruction approach for scan time reduction and improved spatial resolution in breast DWI.

STUDY TYPE

Retrospective.

POPULATION

133 women (age 49.7 ± 12.1 years) underwent multiparametric breast MRI.

FIELD STRENGTH/SEQUENCE

3.0T/T2 turbo spin echo, T1 3D gradient echo, DWI (800 and 1600 sec/mm2).

ASSESSMENT

DWI data were retrospectively processed using deep learning-based k-space-to-image reconstruction (DL-DWI) and an additional super-resolution algorithm (SRDL-DWI). In addition to signal-to-noise ratio and apparent diffusion coefficient (ADC) comparisons among standard, DL- and SRDL-DWI, a range of quantitative similarity (e.g., structural similarity index [SSIM]) and error metrics (e.g., normalized root mean square error [NRMSE], symmetric mean absolute percent error [SMAPE], log accuracy error [LOGAC]) was calculated to analyze structural variations. Subjective image evaluation was performed independently by three radiologists on a seven-point rating scale.

STATISTICAL TESTS

Friedman's rank-based analysis of variance with Bonferroni-corrected pairwise post-hoc tests. P < 0.05 was considered significant.

RESULTS

Both DL- and SRDL-DWI allowed for a 39% reduction in simulated scan time over standard DWI (5 vs. 3 minutes). The highest image quality ratings were assigned to SRDL-DWI with good interreader agreement (ICC 0.834; 95% confidence interval 0.818-0.848). Irrespective of b-value, both standard and DL-DWI produced superior SNR compared to SRDL-DWI. ADC values were slightly higher in SRDL-DWI (+0.5%) and DL-DWI (+3.4%) than in standard DWI. Structural similarity was excellent between DL-/SRDL-DWI and standard DWI for either b value (SSIM ≥ 0.86). Calculation of error metrics (NRMSE ≤ 0.05, SMAPE ≤ 0.02, and LOGAC ≤ 0.04) supported the assumption of low voxel-wise error.

DATA CONCLUSION

Deep learning-based k-space-to-image reconstruction reduces simulated scan time of breast DWI by 39% without influencing structural similarity. Additionally, super-resolution interpolation allows for substantial improvement of subjective image quality.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 1.

Influence of Gadolinium-based Contrast Media and Inter-reader Variation on the Estimation of Intravoxel Incoherent Motion (IVIM) Parameters in Breast MR Imaging

PURPOSE

Gadolinium-based contrast media (GBCM) may affect apparent diffusion coefficient measurements on diffusion-weighted imaging. We aimed at investigating the effect of GBCM and inter-reader variation on intravoxel incoherent motion (IVIM) parameters in breast lesions.

METHODS

A total of 89 patients referred to 3T breast MRI with at least one histologically verified lesion were included. IVIM data were acquired using a single-shot echo planar imaging sequence before and after GBCM administration. D (true diffusion coefficient), D* (pseudo-diffusion coefficient) and f (perfusion fraction) were calculated and measured by two readers (R1, R2). Inter-reader and intra-reader agreements were assessed by intraclass correlation coefficients (ICCs) and Bland-Altman plots.

RESULTS

D was comparable before and after GBCM administration and between readers. D* and f decreased after GBCM administration and showed a lower agreement between readers. Intra-reader agreement before and after GBCM administration was almost perfect for D for both R1 and R2 (ICC 0.955 and 0.887). The intra-reader agreement was substantial to moderate for D* (ICC R1 0.708, R2 0.583) and moderate for f (ICC R1 0.529 and R2 0.425). Inter-reader agreement before GBCM administration was almost perfect for D (ICC 0.905), substantial for D* (ICC 0.733), and moderate for f (ICC 0.404); after contrast media administration, it was almost perfect for D (ICC 0.876) and substantial for D* (ICC 0.654) and f (ICC 0.606). Bland-Altman plots revealed no significant bias.

CONCLUSION

Administration of GBCM seems to have a stronger effect on D* and f values than on D values. This should be considered when applying IVIM in clinical practice.

Non-contrast Breast MR Imaging

Considering the high cost of dynamic contrast-enhanced MR imaging and various contraindications and health concerns related to administration of intravenous gadolinium-based contrast agents, there is emerging interest in non-contrast-enhanced breast MR imaging. Diffusion-weighted MR imaging (DWI) is a fast, unenhanced technique that has wide clinical applications in breast cancer detection, characterization, prognosis, and predicting treatment response. It also has the potential to serve as a non-contrast MR imaging screening method. Standardized protocols and interpretation strategies can help to enhance the clinical utility of breast DWI. A variety of other promising non-contrast MR imaging techniques are in development, but currently, DWI is closest to clinical integration, while others are still mostly used in the research setting.

Performance of Diffusion-weighted Imaging-based Noncontrast MRI Protocols for Diagnosis of Breast Cancer: A Systematic Review and Meta-Analysis

Background Diffusion-weighted imaging (DWI) is increasingly recognized as a powerful diagnostic tool and tested alternative to contrast-enhanced (CE) breast MRI. Purpose To perform a systematic review and meta-analysis that assesses the diagnostic performance of DWI-based noncontrast MRI protocols (ncDWI) for the diagnosis of breast cancer. Materials and Methods A systematic literature search in PubMed for articles published from January 1985 to September 2023 was performed. Studies were excluded if they investigated malignant lesions or selected patients and/or lesions only, used DWI as an adjunct technique to CE MRI, or were technical studies. Statistical analysis included pooling of diagnostic accuracy and investigating between-study heterogeneity. Additional subgroup comparisons of ncDWI to CE MRI and standard mammography were performed. Results A total of 28 studies were included, with 4406 lesions (1676 malignant, 2730 benign) in 3787 patients. The pooled sensitivity and specificity of ncDWI were 86.5% (95% CI: 81.4, 90.4) and 83.5% (95% CI: 76.9, 88.6), and both measures presented with high between-study heterogeneity (I 2 = 81.6% and 91.6%, respectively; P < .001). CE MRI (18 studies) had higher sensitivity than ncDWI (95.1% [95% CI: 92.9, 96.7] vs 88.9% [95% CI: 82.4, 93.1], P = .004) at similar specificity (82.2% [95% CI: 75.0, 87.7] vs 82.0% [95% CI: 74.8, 87.5], P = .97). Compared with ncDWI, mammography (five studies) showed no evidence of a statistical difference for sensitivity (80.3% [95% CI: 56.3, 93.3] vs 56.7%; [95% CI: 41.9, 70.4], respectively; P = .09) or specificity (89.9% [95% CI: 85.5, 93.1] vs 90% [95% CI: 61.3, 98.1], respectively; P = .62), but ncDWI had a higher area under the summary receiver operating characteristic curve (0.93 [95% CI: 0.91, 0.95] vs 0.78 [95% CI: 0.74, 0.81], P < .001). Conclusion A direct comparison with CE MRI showed a modestly lower sensitivity at similar specificity for ncDWI, and higher diagnostic performance indexes for ncDWI than standard mammography. Heterogeneity was high, thus these results must be interpreted with caution. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Kataoka and Iima in this issue.

Added value of the EUSOBI diffusion levels in breast MRI

OBJECTIVES

To investigate whether using the diffusion levels (DLs) proposed by the European Society of Breast Imaging (EUSOBI) improves the diagnostic accuracy of breast MRI.

MATERIALS AND METHODS

This retrospective study included 145 women who, between September 2019 and June 2020, underwent breast 1.5-T MRI with DWI. Reader 1 and reader 2 (R1-R2) independently assessed breast lesions using the BI-RADS on dynamic contrast-enhanced imaging and T2-weighted imaging. DWI was subsequently disclosed, allowing readers able to measure lesions ADC and subjectively express the overall risk of malignancy on a 1-5 Likert scale. ADCs were interpreted as a range of values corresponding to the EUSOBI DLs. The analysis evaluated the inter-reader agreement in measuring ADC and DLs, the per-DL malignancy rate, and accuracy for malignancy using ROC analysis against histological examination or a 3-year follow-up.

RESULTS

Lesions were malignant and showed non-mass enhancement in 67.7% and 76.1% of cases, respectively. ADC was measurable in 63.2%/66.7% of lesions (R1/R2), with a minimal discrepancy on Bland-Altman analysis and 0.948 (95%CI 0.925-0.965)/0.989 (95%CI 0.988-0.991) intraclass correlation coefficient in measuring ADC/DLs. The malignancy rate (R1/R2) increased from 0.5/0.5% ("very high" DL) to 96.0/96.8% ("very low" DL), as expected. Likert categorization showed larger areas under the curve than the BI-RADS for both R1 (0.91 versus 0.87; p = 0.0208) and R2 (0.91 versus 0.89; p = 0.1171), with improved specificity (81.5% versus 78.5% for R1 and 84.4% versus 81.2% for R2).

CONCLUSION

Though ADC was not measurable in about one-third of lesions, DLs were categorized with excellent inter-reader agreement, improving the specificity for malignancy.

CLINICAL RELEVANCE STATEMENT

DLs proposed by the EUSOBI are a reproducible tool to interpret the ADC of breast lesions and, in turn, to improve the specificity of breast MRI and reduce unnecessary breast biopsies.

KEY POINTS

• The European Society of Breast Imaging proposed diffusion levels for the interpretation of the apparent diffusion coefficient in diffusion-weighted imaging of the breast. • Adding diffusion levels to the interpretation of magnetic resonance imaging improved the diagnostic accuracy for breast cancer, especially in terms of specificity. • Diffusion levels can favor a more widespread and standardized use of diffusion-weighted imaging of the breast.

Quantitative characterization of breast lesions and normal fibroglandular tissue using compartmentalized diffusion-weighted model: comparison of intravoxel incoherent motion and restriction spectrum imaging

BACKGROUND

To compare the compartmentalized diffusion-weighted models, intravoxel incoherent motion (IVIM) and restriction spectrum imaging (RSI), in characterizing breast lesions and normal fibroglandular tissue.

METHODS

This prospective study enrolled 152 patients with 157 histopathologically verified breast lesions (41 benign and 116 malignant). All patients underwent a full-protocol preoperative breast MRI, including a multi-b-value DWI sequence. The diffusion parameters derived from the mono-exponential model (ADC), IVIM model (Dt, Dp, f), and RSI model (C1, C2, C3, C1C2, F1, F2, F3, F1F2) were quantitatively measured and then compared among malignant lesions, benign lesions and normal fibroglandular tissues using Kruskal-Wallis test. The Mann-Whitney U-test was used for the pairwise comparisons. Diagnostic models were built by logistic regression analysis. The ROC analysis was performed using five-fold cross-validation and the mean AUC values were calculated and compared to evaluate the discriminative ability of each parameter or model.

RESULTS

Almost all quantitative diffusion parameters showed significant differences in distinguishing malignant breast lesions from both benign lesions (other than C2) and normal fibroglandular tissue (all parameters) (all P < 0.0167). In terms of the comparisons of benign lesions and normal fibroglandular tissues, the parameters derived from IVIM (Dp, f) and RSI (C1, C2, C1C2, F1, F2, F3) showed significant differences (all P < 0.005). When using individual parameters, RSI-derived parameters-F1, C1C2, and C2 values yielded the highest AUCs for the comparisons of malignant vs. benign, malignant vs. normal tissue and benign vs. normal tissue (AUCs = 0.871, 0.982, and 0.863, respectively). Furthermore, the combined diagnostic model (IVIM + RSI) exhibited the highest diagnostic efficacy for the pairwise discriminations (AUCs = 0.893, 0.991, and 0.928, respectively).

CONCLUSIONS

Quantitative parameters derived from the three-compartment RSI model have great promise as imaging indicators for the differential diagnosis of breast lesions compared with the bi-exponential IVIM model. Additionally, the combined model of IVIM and RSI achieves superior diagnostic performance in characterizing breast lesions.

Diffusion tensor imaging: survival analysis prediction in breast cancer patients

PURPOSE

We aimed to explore the performance of diffusion-tensor imaging (DTI) and apparent diffusion coefficient (ADC) parameters in evaluating disease-free survival (DFS) and overall survival (OS) in patients with invasive breast cancer.

MATERIAL AND METHODS

A total of 49 women with invasive breast cancer who were diagnosed between 2017 and 2022 were included. All patients underwent breast magnetic resonance imaging (MRI) with DTI and diffusion-weighted imaging (DWI) features, with examiners blinded to the clinical data. Volume anisotropy (VA), fractional anisotropy (FA), and ADC values were measured to assess intratumoral measured heterogeneity. Correlations and differences in diffusion metrics according to OS and DFS status of the cases were analyzed. The discriminative ability of the quantitative findings was assessed by receiver operating characteristic (ROC) curve analyses and validated in the independent cohort.

RESULTS

We evaluated patients with metastases (n = 13, 36.5%) and those without metastases (n = 36, 73.5%). Differences in the ADC, FA, and VA values were observed. The results of Cox regression survival analysis for all the patients included in the survival analysis revealed that DTI metrics contributed to the prediction of overall survival (OS) in the emerging models (p < 0.05). Both FA and VA were associated with OS (p = 0.037 and p = 0.038, respectively). However, ADC was not associated with OS (p = 0.177) or DFS (p = 0.252).

CONCLUSION

To the best of our knowledge, this is the first study to assess the prognostic value of DTI-MRI in breast cancer with statistical survival analysis techniques. We believe that DTI measurements can be used as a biomarker for OS analysis in breast cancer given the available data.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Introduction of a breast apparent diffusion coefficient category system (ADC-B) derived from a large multicenter MRI database

OBJECTIVES

To develop an intuitive and generally applicable system for the reporting, assessment, and documentation of ADC to complement standard BI-RADS criteria.

METHODS

This was a multicentric, retrospective analysis of 11 independently conducted institutional review board-approved studies from seven institutions performed between 2007 and 2019. Breast Apparent Diffusion coefficient (ADC-B) categories comprised ADC-B0 (ADC non-diagnostic), ADC-B1 (no enhancing lesion), and ADC-B2-5. The latter was defined by plotting ADC versus cumulative malignancy rates. Statistics comprised ANOVA with post hoc testing and ROC analysis. p values ≤ 0.05 were considered statistically significant.

RESULTS

A total of 1625 patients (age: 55.9 years (± 13.8)) with 1736 pathologically verified breast lesions were included. The mean ADC (× 10-3 mm2/s) differed significantly between benign (1.45, SD .40) and malignant lesions (.95, SD .39), and between invasive (.92, SD .22) and in situ carcinomas (1.18, SD .30) (p < .001). The following ADC-B categories were identified: ADC-B0-ADC cannot be assessed; ADC-B1-no contrast-enhancing lesion; ADC-B2-ADC ≥ 1.9 (cumulative malignancy rate < 0.1%); ADC-B3-ADC 1.5 to < 1.9 (0.1-1.7%); ADC-B4-ADC 1.0 to < 1.5 (10-24.5%); and ADC-B5-ADC < 1.0 (> 24.5%). At the latter threshold, a positive predictive value of 95.8% (95% CI 0.94-0.97) for invasive versus non-invasive breast carcinomas was reached.

CONCLUSIONS

The breast apparent diffusion coefficient system (ADC-B) provides a simple and widely applicable categorization scheme for assessment, documentation, and reporting of apparent diffusion coefficient values in contrast-enhancing breast lesions on MRI.

CLINICAL RELEVANCE STATEMENT

The ADC-B system, based on diverse MRI examinations, is clinically relevant for stratifying breast cancer risk via apparent diffusion coefficient measurements, and complements BI-RADS for improved clinical decision-making and patient outcomes.

KEY POINTS

• The breast apparent diffusion coefficient category system (ADC-B) is a simple tool for the assessment, documentation, and reporting of ADC values in contrast-enhancing breast lesions on MRI. • The categories comprise ADC-B0 for non-diagnostic examinations, ADC-B1 for examinations without an enhancing lesion, and ADC-B2-5 for enhancing lesions with an increasing malignancy rate. • The breast apparent diffusion coefficient category system may be used to complement BI-RADS in clinical decision-making.

Feasibility of Diffusion Tensor Imaging for Decreasing Biopsy Rates in Breast Imaging: Interim Analysis of a Prospective Study

Because of the limited specificity of diagnostic imaging, many breast lesions referred for biopsy turn out to be benign. The objective of this study was to evaluate whether diffusion tensor MRI (DTI) parametric maps can be used to safely avoid biopsy of breast lesions. Individuals referred for breast biopsy based on mammogram (MG), ultrasound (US), and/or contrast enhanced (CE)-MRI were recruited. Scans consisting of T2-weighted and DTI sequences were performed. Multiple DTI-derived parametric color maps were evaluated semi-quantitatively to characterize lesions as "definitely benign," "not definitely benign," or "suspicious." All patients subsequently underwent biopsy. In this moderately-sized prospective study, 21 out of 47 pathologically proven benign lesions were characterized by both readers as "definitely benign," which would have precluded the need for biopsy. Biopsy was recommended for 11 out of 13 cancers that were characterized as "suspicious." In the remaining two cancers and 26 of 47 benign lesions, the scans were characterized as "not definitely benign" and hence required biopsy. The main causes for "not definitely benign" scans were small lesion sizes and noise. The results suggest that in appropriately selected patients, DTI may be used to safely reduce the number of unnecessary breast biopsies.

Breast cancer diagnosis and prognosis using a high b-value non-Gaussian continuous-time random-walk model

AIM

To compare the diagnostic performance of mono-exponential model-derived apparent diffusion coefficient (ADC), continuous-time random-walk (CTRW) model-derived D_m, α, β and their combinations in discriminating malignancy of breast lesions, and investigate the association between model-derived parameters and prognosis-related immunohistochemical indices.

MATERIALS AND METHODS

A total of 85 patients with breast lesions (51 malignant, 34 benign) were analysed in this retrospective study. Clinical characteristics include oestrogen receptor (ER), progesterone receptor (PR), human epidermal receptor 2 (HER2), and Ki-67. The ADC was fitted using a mono-exponential model (b-values = 0, 800 s/mm²), while D_m, α, and β were fitted using a CTRW model. Independent Student's t-test and the Mann-Whitney U-test were used for the comparison of parameters. Discrimination performance was accomplished by receiver operating characteristic (ROC) analysis, and Spearman's correlation analysis was used to explore the association between immunohistochemical indices and diffusion parameters, the statistical significance level was p<0.05.

RESULTS

D_m and ADC demonstrated similar performance in differentiating malignant and benign lesions (AUC = 0.928 versus 0.930), while the combination of D_m, α, and β could improve the AUC to 0.969. The combined parameter generated by ADC, D_m, α, and β was effective in identifying the ER+/ER- and PR+/PR- patients. Temporal heterogeneity parameter α correlated significantly with the expression of PR.

CONCLUSION

Diffusion parameters derived from the CTRW model could effectively discriminate the malignancy of breast lesions. Meanwhile, the hormone receptor expression could be distinguished by combined diffusion parameters, and have the potential to reflect the prognosis.

Preoperative prediction of Ki-67 status in invasive breast carcinoma using dynamic contrast-enhanced MRI, diffusion-weighted imaging and diffusion tensor imaging

Background

The Ki-67 is a beneficial marker of tumor aggressiveness. It is proliferation index that has been used to distinguish luminal B from luminal A breast cancers. By fast progress in quantitative radiology modalities, tumor biology and genetics can be assessed in a more accurate, predictive, and cost-effective method. The aim of this study was to assess the role of dynamic contrast-enhanced magnetic resonance imaging, diffusion-weighted imaging and diffusion tensor imaging in prediction of Ki-67 status in patients with invasive breast carcinoma estimate cut off values between breast cancer with high Ki-67 status and those with low Ki-67 status.

Results

Cut off ADC (apparent diffusion co-efficient) value of 0.657 mm2/s had 96.4% sensitivity, 75% specificity and 93.8% accuracy in differentiating cases with high Ki67 from those with low Ki67. Cut off maximum enhancement value of 1715 had 96.4% sensitivity, 75% specificity and 93.8% accuracy in differentiating cases with high Ki67 from those with low Ki67. Cut off washout rate of 0.73 I/S had 60.7% sensitivity, 75% specificity and 62.5% accuracy in differentiating cases with high Ki67 from those with low Ki67. Cut off time to peak value of 304 had 71.4% sensitivity, 75% specificity and 71.9% accuracy in differentiating cases with high Ki67 from those with low Ki67.

Conclusions

ADC, time to peak and maximum enhancement values had high sensitivity, specificity and accuracy in differentiating breast cancer with high Ki-67 status from those with low Ki-67 status.

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

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

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.

The road to breast cancer screening with diffusion MRI

Breast cancer is the leading cause of cancer in women with a huge medical, social and economic impact. Mammography (MMG) has been the gold standard method until now because it is relatively inexpensive and widely available. However, MMG suffers from certain limitations, such as exposure to X-rays and difficulty of interpretation in dense breasts. Among other imaging methods, MRI has clearly the highest sensitivity and specificity, and breast MRI is the gold standard for the investigation and management of suspicious lesions revealed by MMG. Despite this performance, MRI, which does not rely on X-rays, is not used for screening except for a well-defined category of women at risk, because of its high cost and limited availability. In addition, the standard approach to breast MRI relies on Dynamic Contrast Enhanced (DCE) MRI with the injection of Gadolinium based contrast agents (GBCA), which have their own contraindications and can lead to deposit of gadolinium in tissues, including the brain, when examinations are repeated. On the other hand, diffusion MRI of breast, which provides information on tissue microstructure and tumor perfusion without the use of contrast agents, has been shown to offer higher specificity than DCE MRI with similar sensitivity, superior to MMG. Diffusion MRI thus appears to be a promising alternative approach to breast cancer screening, with the primary goal of eliminating with a very high probability the existence of a life-threatening lesion. To achieve this goal, it is first necessary to standardize the protocols for acquisition and analysis of diffusion MRI data, which have been found to vary largely in the literature. Second, the accessibility and cost-effectiveness of MRI examinations must be significantly improved, which may become possible with the development of dedicated low-field MRI units for breast cancer screening. In this article, we will first review the principles and current status of diffusion MRI, comparing its clinical performance with MMG and DCE MRI. We will then look at how breast diffusion MRI could be implemented and standardized to optimize accuracy of results. Finally, we will discuss how a dedicated, low-cost prototype of breast MRI system could be implemented and introduced to the healthcare market.

Investigating the effect of ARHGEF10L gene on tumor growth in gastric cancer in a nude mouse model using quantitative MRI parameters

Background

The quantitative magnetic resonance imaging (MRI) parameters were initially used in the study of central nervous system diseases and has since been widely used in the diagnosis of breast, liver, rectum, and prostate diseases. In our study, we aimed to evaluate the effect of ARHGEF10L gene on tumor growth in gastric cancer in nude mice using quantitative MRI parameters.

Subjects and Methods

A nude mice model of gastric cancer was established, and the mice were divided into a control group and an shARHGEF10L group (N = 10). T2-fs and intravoxel incoherent motions (IVIM) imaging were performed in the mice coil with a 3.0 T MR system. The differences in quantitative parameters (apparent diffusion coefficient [ADC], D, D *, f values) were compared between both groups, and the effect of ARHGEF10L expression on tumor growth in tumor-bearing mice was investigated. The data were analyzed using Statistical Package for the Social Sciences (SPSS) 17.0 software package.

Results

The ADC and D values of tumor imaging in the shARHGEF10L group were higher than those in the control group, and the differences were statistically significant. There was no significant difference in the D* or F values between both groups.

Conclusions

The ADC and D values of the quantitative IVIM imaging parameters can be used to effectively assess the growth of gastric cancer in nude mice, suggesting that ARHGEF10L may promote the growth of tumor cells.

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

Background

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

Results

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

Conclusions

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

The Diagnostic Efficiency of Quantitative Diffusion Weighted Imaging in Differentiating Medulloblastoma from Posterior Fossa Tumors: A Systematic Review and Meta-Analysis

Medulloblastoma (MB) is considered the most common and highly malignant posterior fossa tumor (PFT) in children. The accurate preoperative diagnosis of MB is beneficial in choosing the appropriate surgical methods and treatment strategies. Diffusion-weighted imaging (DWI) has improved the accuracy of differential diagnosis of posterior fossa tumors. Nonetheless, further studies are needed to confirm its value for clinical application. This study aimed to evaluate the performance of DWI in differentiating MB from other PFT. A literature search was conducted using databases PubMed, Embase, and Web of Science for studies reporting the diagnostic performance of DWI for PFT from January 2000 to January 2022. A bivariate random-effects model was employed to evaluate the pooled sensitivities and specificities. A univariable meta-regression analysis was used to assess relevant factors for heterogeneity, and subgroup analyses were performed. A total of 15 studies with 823 patients were eligible for data extraction. Overall pooled sensitivity and specificity of DWI were 0.94 (95% confident interval [CI]: 0.89-0.97) and 0.94 (95% CI: 0.90-0.96) respectively. The area under the curve (AUC) of DWI was 0.98 (95% CI: 0.96-0.99). Heterogeneity was found in the sensitivity (I² = 62.59%) and the specificity (I² = 35.94%). Magnetic field intensity, region of interest definition and DWI diagnostic parameters are the factors that affect the diagnostic performance of DWI. DWI has excellent diagnostic accuracy for differentiating MB from other PFT. Hence, it is necessary to set DWI as a routine examination sequence for posterior fossa tumors.

Identification of the Benignity and Malignancy of BI-RADS 4 Breast Lesions Based on a Combined Quantitative Model of Dynamic Contrast-Enhanced MRI and Intravoxel Incoherent Motion

The aim of this study was to explore whether intravoxel incoherent motion (IVIM) combined with a dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) quantitative model can improve the ability to distinguish between benign and malignant BI-RADS 4 breast lesions. We enrolled 100 patients who underwent breast MRI at our institution and extracted the quantitative parameters of lesions with a post-processing workstation. Statistical differences in these parameters between benign and malignant BI-RADS 4 lesions were assessed using a two independent samples t-test or a Mann-Whitney U test. Binary logistic regression analysis was performed to establish five diagnostic models (model_{_ADC}, model_{_IVIM}, model_{_DCE}, model_{_DCE+ADC}, and model_{_DCE+IVIM}). Receiver operating characteristic (ROC) curves, leave-one-out cross-validation, and the Delong test were used to assess and compare the diagnostic performance of these models. The model_{_DCE+IVIM} showed the highest area under the curve (AUC) of 0.903 (95% confidence interval (CI): 0.828-0.953, sensitivity: 87.50%, specificity: 85.00%), which was significantly higher than that of model_{_ADC} (p = 0.014) and model_{_IVIM} (p = 0.033). The model_{_ADC} had the lowest diagnostic performance (AUC = 0.768, 95%CI: 0.672-0.846) but was not significantly different from model_{_IVIM} (p = 0.168). The united quantitative model with DCE-MRI and IVIM could improve the ability to evaluate the malignancy in BI-RADS 4 lesions, and unnecessary breast biopsies may be obviated.

A survey by the European Society of Breast Imaging on the implementation of breast diffusion-weighted imaging in clinical practice

OBJECTIVES

To perform a survey among all European Society of Breast Imaging (EUSOBI) radiologist members to gather representative data regarding the clinical use of breast DWI.

METHODS

An online questionnaire was developed by two board-certified radiologists, reviewed by the EUSOBI board and committees, and finally distributed among EUSOBI active and associated (not based in Europe) radiologist members. The questionnaire included 20 questions pertaining to technical preferences (acquisition time, magnet strength, breast coils, number of b values), clinical indications, imaging evaluation, and reporting. Data were analyzed using descriptive statistics, the Chi-square test of independence, and Fisher's exact test.

RESULTS

Of 1411 EUSOBI radiologist members, 275/1411 (19.5%) responded. Most (222/275, 81%) reported using DWI as part of their routine protocol. Common indications for DWI include lesion characterization (using an ADC threshold of 1.2-1.3 × 10-3 mm2/s) and prediction of response to chemotherapy. Members most commonly acquire two separate b values (114/217, 53%), with b value = 800 s/mm2 being the preferred value for appraisal among those acquiring more than two b values (71/171, 42%). Most did not use synthetic b values (169/217, 78%). While most mention hindered diffusion in the MRI report (161/213, 76%), only 142/217 (57%) report ADC values.

CONCLUSION

The utilization of DWI in clinical practice among EUSOBI radiologists who responded to the survey is generally in line with international recommendations, with the main application being the differentiation of benign and malignant enhancing lesions, treatment response assessment, and prediction of response to chemotherapy. Report integration of qualitative and quantitative DWI data is not uniform.

KEY POINTS

• Clinical performance of breast DWI is in good agreement with the current recommendations of the EUSOBI International Breast DWI working group. • Breast DWI applications in clinical practice include the differentiation of benign and malignant enhancing, treatment response assessment, and prediction of response to chemotherapy. • Report integration of DWI results is not uniform.

Can DWI provide additional value to Kaiser score in evaluation of breast lesions

OBJECTIVES

To explore added value of diffusion-weighted imaging (DWI) as an adjunct to Kaiser score (KS) for differentiation of benign from malignant lesions on breast magnetic resonance imaging (MRI).

METHODS

Two hundred forty-six patients with 273 lesions (155 malignancies) were included in this retrospective study from January 2015 to December 2019. All lesions were proved by pathology. Two radiologists blind to pathological results evaluated lesions according to KS. Lesions with score > 4 were considered malignant. Four thresholds of ADC values -1.3 × 10^-3mm²/s, 1.4 × 10^-3mm²/s, 1.53 × 10^-3mm²/s, and 1.6 × 10^-3mm²/s were used to distinguish benign from malignant lesions. For combined diagnosis, a lesion with KS > 4 and ADC values below the preset cutoffs was considered as malignant; otherwise, it was benign. Sensitivity, specificity, and area under the curve (AUC) were compared between KS, DWI, and combined diagnosis.

RESULTS

The AUC of KS was significantly higher than that of DWI alone (0.941 vs 0.901, p = 0.04). The sensitivity of KS (96.8%) and DWI (97.4 - 99.4%) was comparable (p > 0.05) while the specificity of KS (83.9%) was significantly higher than that of DWI (19.5-56.8%) (p < 0.05). Adding DWI as an adjunct to KS resulted in a 0-2.5% increase of specificity and a 0.1-1.3% decrease of sensitivity; however, the difference did not reach statistical significance (p > 0.05).

CONCLUSION

KS showed higher diagnostic performance than DWI alone for discrimination of breast benign and malignant lesions. DWI showed no additional value to KS for characterizing breast lesions.

KEY POINTS

• KS showed higher diagnostic performance than DWI alone for differentiation of benign from breast malignant lesions. • DWI alone showed a high sensitivity but a low specificity for characterizing breast lesions. • Diagnostic performance did not improve using DWI as an adjunct to KS.

Diagnostic value of multi-model high-resolution diffusion-weighted MR imaging in breast lesions: Based on simultaneous multi-slice readout-segmented echo-planar imaging

PURPOSE

To investigate the diagnostic value of multi-model high-resolution diffusion-weighted MR imaging (DWI) in breast lesions, with a comparison of simultaneous multi-slice readout-segmented echo-planar imaging (SMS rs-EPI) and single-shot EPI (ss-EPI).

MATERIALS AND METHODS

This retrospective study was approved by the institutional ethics committee and included 120 patients with 122 breast lesions (25 benign and 97 malignant). All patients underwent breast DWI with multi-b values (0, 50, 100, 200, 400, 800, 1200, and 2000 s/mm²) based on both SMS rs-EPI and ss-EPI on a 3.0 T MR scanner. Quantitative DWI-derived parameters including ADC, MK, MD, D, D*, and f were calculated based on mono-exponential (Mono), intravoxel incoherent motion (IVIM), diffusion kurtosis (DKI) models. Meanwhile, both DWI sequences were qualitatively evaluated with respect to overall image quality, lesion conspicuity, image artifact, geometric distortion, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and lesion contrast. The differences in DW-derived parameters, image quality, and diagnostic performance were statistically compared between SMS rs-EPI and ss-EPI groups.

RESULTS

The SMS rs-EPI produced higher Contrast, CNR and lower SNR than ss-EPI (p < 0.01). The image quality of SMS rs-EPI was superior to ss-EPI either in subjective or objective evaluation. There was no significant difference between the SMS rs-EPI and ss-EPI for either MD or the D* (p > 0.05). However, the MK and f between the two sequences showed significant differences (p < 0.05). Spearman's correlation coefficient displayed good linear correlation for MK values (r = 0.73, 95% CI 0.617-0.857), MD values (r = 0.88, 95% CI 0.814-0.926), ADC values (r = 0.93, 95% CI 0.869-0.948) and D values (r = 0.93, 95% CI 0.856-0.948) between SMS rs-EPI and ss-EPI. Spearman's correlation coefficient for f values (r = 0.25, 95% CI 0.226-0.559) and D* values (r = 0.22, 95% CI 0.025-0.348) were fair and no correlation between the two sequences. MK values have the highest diagnostic value in differentiating benign and malignant breast lesions.

CONCLUSIONS

High-resolution multi-model DWI based on SMS rs-EPI technique can provide superior image quality and lesion characterization, with comparable diagnostic performance as compared with ss-EPI DWI in differentiating benign and malignant breast lesions. Of different DWI-derived parameters, MK values showed the best diagnostic performance.

Diffusion Breast MRI: Current Standard and Emerging Techniques

The role of diffusion weighted imaging (DWI) as a biomarker has been the subject of active investigation in the field of breast radiology. By quantifying the random motion of water within a voxel of tissue, DWI provides indirect metrics that reveal cellularity and architectural features. Studies show that data obtained from DWI may provide information related to the characterization, prognosis, and treatment response of breast cancer. The incorporation of DWI in breast imaging demonstrates its potential to serve as a non-invasive tool to help guide diagnosis and treatment. In this review, current technical literature of diffusion-weighted breast imaging will be discussed, in addition to clinical applications, advanced techniques, and emerging use in the field of radiomics.

Sodium accumulation in breast cancer predicts malignancy and treatment response

BACKGROUND

Breast cancer remains a leading cause of death in women and novel imaging biomarkers are urgently required. Here, we demonstrate the diagnostic and treatment-monitoring potential of non-invasive sodium (23Na) MRI in preclinical models of breast cancer.

METHODS

Female Rag2-/- Il2rg-/- and Balb/c mice bearing orthotopic breast tumours (MDA-MB-231, EMT6 and 4T1) underwent MRI as part of a randomised, controlled, interventional study. Tumour biology was probed using ex vivo fluorescence microscopy and electrophysiology.

RESULTS

23Na MRI revealed elevated sodium concentration ([Na+]) in tumours vs non-tumour regions. Complementary proton-based diffusion-weighted imaging (DWI) linked elevated tumour [Na+] to increased cellularity. Combining 23Na MRI and DWI measurements enabled superior classification accuracy of tumour vs non-tumour regions compared with either parameter alone. Ex vivo assessment of isolated tumour slices confirmed elevated intracellular [Na+] ([Na+]i); extracellular [Na+] ([Na+]e) remained unchanged. Treatment with specific inward Na+ conductance inhibitors (cariporide, eslicarbazepine acetate) did not affect tumour [Na+]. Nonetheless, effective treatment with docetaxel reduced tumour [Na+], whereas DWI measures were unchanged.

CONCLUSIONS

Orthotopic breast cancer models exhibit elevated tumour [Na+] that is driven by aberrantly elevated [Na+]i. Moreover, 23Na MRI enhances the diagnostic capability of DWI and represents a novel, non-invasive biomarker of treatment response with superior sensitivity compared to DWI alone.

Machine Learning Based on MRI DWI Radiomics Features for Prognostic Prediction in Nasopharyngeal Carcinoma

Simple Summary

In the past, radiomics studies of nasopharyngeal carcinoma (NPC) were only based on basic MR sequences. Previous studies have shown that radiomics methods based on T2-weighted imaging and contrast-enhanced T1-weighted imaging have been successfully used to improve the prognosis of patients with nasopharyngeal carcinoma. The purpose of this study was to explore the predictive efficacy of radiomics analyses based on readout-segmented echo-planar diffusion-weighted imaging (RESOLVE-DWI) which quantitatively reflects the diffusion motion of water molecules for prognosis evaluation in nasopharyngeal carcinoma. Several prognostic radiomics models were established by using diffusion-weighted imaging, apparent diffusion coefficient maps, T2-weighted and contrast-enhanced T1-weighted imaging to predict the risk of recurrence or metastasis of nasopharyngeal carcinoma, and the predictive effects of different models were compared. The results show that the model based on MRI DWI can successfully predict the prognosis of patients with nasopharyngeal carcinoma and has higher predictive efficiency than the model based on the conventional sequence, which suggests MRI DWI-radiomics can provide a useful and alternative approach for survival estimation.

Abstract

Purpose: This study aimed to explore the predictive efficacy of radiomics analyses based on readout-segmented echo-planar diffusion-weighted imaging (RESOLVE-DWI) for prognosis evaluation in nasopharyngeal carcinoma in order to provide further information for clinical decision making and intervention. Methods: A total of 154 patients with untreated NPC confirmed by pathological examination were enrolled, and the pretreatment magnetic resonance image (MRI)—including diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) maps, T2-weighted imaging (T2WI), and contrast-enhanced T1-weighted imaging (CE-T1WI)—was collected. The Random Forest (RF) algorithm selected radiomics features and established the machine-learning models. Five models, namely model 1 (DWI + ADC), model 2 (T2WI + CE-T1WI), model 3 (DWI + ADC + T2WI), model 4 (DWI + ADC + CE-T1WI), and model 5 (DWI + ADC + T2WI + CE-T1WI), were constructed. The average area under the curve (AUC) of the validation set was determined in order to compare the predictive efficacy for prognosis evaluation. Results: After adjusting the parameters, the RF machine learning models based on extracted imaging features from different sequence combinations were obtained. The invalidation sets of model 1 (DWI + ADC) yielded the highest average AUC of 0.80 (95% CI: 0.79–0.81). The average AUCs of the model 2, 3, 4, and 5 invalidation sets were 0.72 (95% CI: 0.71–0.74), 0.66 (95% CI: 0.64–0.68), 0.74 (95% CI: 0.73–0.75), and 0.75 (95% CI: 0.74–0.76), respectively. Conclusion: A radiomics model derived from the MRI DWI of patients with nasopharyngeal carcinoma was generated in order to evaluate the risk of recurrence and metastasis. The model based on MRI DWI can provide an alternative approach for survival estimation, and can reveal more information for clinical decision-making and intervention.

Diffusion-Weighted Magnetic Resonance Imaging: Clinical Potential and Applications

Since its discovery in the 1980s [...].

3D DCE-MRI Radiomic Analysis for Malignant Lesion Prediction in Breast Cancer Patients

RATIONALE AND OBJECTIVES

To develop and validate a radiomic model, with radiomic features extracted from breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) from a 1.5T scanner, for predicting the malignancy of masses with enhancement. Images were acquired using an 8-channel breast coil in the axial plane. The rationale behind this study is to show the feasibility of a radiomics-powered model that could be integrated into the clinical practice by exploiting only standard-of-care DCE-MRI with the goal of reducing the required image pre-processing (ie, normalization and quantitative imaging map generation).

MATERIALS AND METHODS

107 radiomic features were extracted from a manually annotated dataset of 111 patients, which was split into discovery and test sets. A feature calibration and pre-processing step was performed to find only robust non-redundant features. An in-depth discovery analysis was performed to define a predictive model: for this purpose, a Support Vector Machine (SVM) was trained in a nested 5-fold cross-validation scheme, by exploiting several unsupervised feature selection methods. The predictive model performance was evaluated in terms of Area Under the Receiver Operating Characteristic (AUROC), specificity, sensitivity, PPV and NPV. The test was performed on unseen held-out data.

RESULTS

The model combining Unsupervised Discriminative Feature Selection (UDFS) and SVMs on average achieved the best performance on the blinded test set: AUROC = 0.725±0.091, sensitivity = 0.709±0.176, specificity = 0.741±0.114, PPV = 0.72±0.093, and NPV = 0.75±0.114.

CONCLUSION

In this study, we built a radiomic predictive model based on breast DCE-MRI, using only the strongest enhancement phase, with promising results in terms of accuracy and specificity in the differentiation of malignant from benign breast lesions.

DWI of the Breast - Possibilities and Limitations

BACKGROUND

 The MRI of the breast is of great importance in the diagnosis of disorders of the breast. This can be stated for the primary diagnosis as well as the follow up. Of special interest is diffusion weighted imaging (DWI), which has an increasingly important role. The present review provides results regarding the diagnostic and prognostic relevance of DWI for disorders of the breast.

METHODS

 Under consideration of the recently published literature, the clinical value of DWI of the breast is discussed. Several diagnostic applications are shown, especially for the primary diagnosis of unclear tumors of the breast, the prediction of the axillary lymph node status and the possibility of a native screening. Moreover, correlations between DWI and histopathology features and treatment prediction with DWI are provided.

RESULTS

 Many studies have shown the diagnostic value of DWI for the primary diagnosis of intramammary lesions. Benign lesions of the breast have significantly higher apparent diffusion coefficients (ADC values) compared to malignant tumors. This can be clinically used to reduce unnecessary biopsies in clinical routine. However, there are inconclusive results for the prediction of the histological subtype of the breast cancer. DWI can aid in the prediction of treatment to neoadjuvant chemotherapy.

CONCLUSION

 DWI is a very promising imaging modality, which should be included in the standard protocol of the MRI of the breast. DWI can provide clinically value in the diagnosis as well as for prognosis in breast cancer.

KEY POINTS

  · DWI can aid in the discrimination between benign and malignant tumors of the breast and therefore avoiding unnecessary biopsies.. · The ADC value cannot discriminate between immunhistochemical subtypes of the breast cancer. · The ADC value of breast cancer increases under neoadjuvant chemotherapy and can by this aid in treatment prediction.. · There is definite need of standardisation for clinical translation.

CITATION FORMAT

· Meyer HJ, Martin M, Denecke T. DWI of the Breast - Possibilities and Limitations. Fortschr Röntgenstr 2022; DOI: 10.1055/a-1775-8572.

Correlations between DWI, IVIM, and HIF-1α expression based on MRI and pathology in a murine model of rhabdomyosarcoma

PURPOSE

To investigate the correlation between DWI, intravoxel incoherent motion (IVIM), and hypoxia-inducible factor 1-alpha (HIF-1α) expression in a nude mouse model of rhabdomyosarcoma based on imaging and pathological comparisons.

METHODS

Human rhabdomyosarcoma-derived (RD) cells were inoculated into the right thigh muscle of 20 BALB/c female nude mice. Mice were imaged using 3.0 Tesla MRI system. T₁ -weighted imaging, T₂ -weighted imaging, DWI, and IVIM images were obtained. ADW4.7 (GE Healthcare, ChicagoAQ34, IL, USA) was used for image processing of ADC, D_slow , D_fast , and f values. All parameter values were independently analyzed by 2 observers. Immunohistochemistry of HIF-1α was performed. We used a specific image-pathology comparison method to ensure correct overlap between the image plane and the pathological section. Mann-Whitney U test or independent sample t test, Pearson or Spearman correlation test, the intragroup correlation coefficient, Kolmogorov-Smirnov test, and receiver operating characteristic curve were used. The correlation between DWI and intravoxel incoherent motion parameter values and HIF-1α expression was determined.

RESULTS

There were 10 mice in the low-expression group and 7 in the high-expression group. The ADC and D_slow values were negatively correlated with HIF-1α with correlation coefficients of -0.491 and - 0.702 (P = 0.045 and 0.002). The f value positively correlated with HIF-1α expression (r = 0.485, P = 0.048). ADC, D_slow , and f were significantly different between the high-HIF-1α expression tumors and the low-HIF-1α expression tumors. ADC showed the best predictive performance among all parameters (area under the curve = 0.652, sensitivity = 83.3%, specificity = 63.6%).

CONCLUSION

The parameter values of DWI and intravoxel incoherent motion can be used to evaluate the expression of HIF-1α in rhabdomyosarcoma. ADC, D_slow , and f value showed correlation with the expression of HIF-1α.

Challenges in ensuring the generalizability of image quantitation methods for MRI

Image quantitation methods including quantitative MRI, multiparametric MRI, and radiomics offer great promise for clinical use. However, many of these methods have limited clinical adoption, in part due to issues of generalizability, that is, the ability to translate methods and models across institutions. Researchers can assess generalizability through measurement of repeatability and reproducibility, thus quantifying different aspects of measurement variance. In this article, we review the challenges to ensuring repeatability and reproducibility of image quantitation methods as well as present strategies to minimize their variance to enable wider clinical implementation. We present possible solutions for achieving clinically acceptable performance of image quantitation methods and briefly discuss the impact of minimizing variance and achieving generalizability towards clinical implementation and adoption.

Impact of Alternate b-Value Combinations and Metrics on the Predictive Performance and Repeatability of Diffusion-Weighted MRI in Breast Cancer Treatment: Results from the ECOG-ACRIN A6698 Trial

In diffusion-weighted MRI (DW-MRI), choice of b-value influences apparent diffusion coefficient (ADC) values by probing different aspects of the tissue microenvironment. As a secondary analysis of the multicenter ECOG-ACRIN A6698 trial, the purpose of this study was to investigate the impact of alternate b-value combinations on the performance and repeatability of tumor ADC as a predictive marker of breast cancer treatment response. The final analysis included 210 women who underwent standardized 4-b-value DW-MRI (b = 0/100/600/800 s/mm2) at multiple timepoints during neoadjuvant chemotherapy treatment and a subset (n = 71) who underwent test−retest scans. Centralized tumor ADC and perfusion fraction (fp) measures were performed using variable b-value combinations. Prediction of pathologic complete response (pCR) based on the mid-treatment/12-week percent change in each metric was estimated by area under the receiver operating characteristic curve (AUC). Repeatability was estimated by within-subject coefficient of variation (wCV). Results show that two-b-value ADC calculations provided non-inferior predictive value to four-b-value ADC calculations overall (AUCs = 0.60−0.61 versus AUC = 0.60) and for HR+/HER2− cancers where ADC was most predictive (AUCs = 0.75−0.78 versus AUC = 0.76), p < 0.05. Using two b-values (0/600 or 0/800 s/mm2) did not reduce ADC repeatability over the four-b-value calculation (wCVs = 4.9−5.2% versus 5.4%). The alternate metrics ADCfast (b ≤ 100 s/mm2), ADCslow (b ≥ 100 s/mm2), and fp did not improve predictive performance (AUCs = 0.54−0.60, p = 0.08−0.81), and ADCfast and fp demonstrated the lowest repeatability (wCVs = 6.71% and 12.4%, respectively). In conclusion, breast tumor ADC calculated using a simple two-b-value approach can provide comparable predictive value and repeatability to full four-b-value measurements as a marker of treatment response.

Stratification of axillary lymph node metastasis risk with breast magnetic resonance imaging in breast cancer

Aims: To develop a model based on breast MRI to stratify axillary lymph node metastasis (ALNM) in breast cancer. Patients & methods: A total of 134 eligible patients were used to build a predicting model, which was validated with an independent group of 57 patients and evaluated for accuracy and sensitivity. Results: A model based on breast MRI was developed and yielded total accuracy of 82.5% and sensitivities of 94.3, 64.3 and 62.5% to predict patients with no, low and heavy ALNM burden, respectively, in the validation group. Conclusion: A noninvasive model based on breast MRI was developed to preoperatively stratify ALNM in breast cancer; its performance needs to be validated and improved in future research.

Differentiation of Benign and Malignant Breast Lesions Using ADC Values and ADC Ratio in Breast MRI

Magnetic resonance imaging (MRI) of the breast has been increasingly used for the detailed evaluation of breast lesions. Diffusion-weighted imaging (DWI) gives additional information for the lesions based on tissue cellularity. The aim of our study was to evaluate the possibilities of DWI, apparent diffusion coefficient (ADC) value and ADC ratio (the ratio between the ADC of the lesion and the ADC of normal glandular tissue) to differentiate benign from malignant breast lesions. Materials and methods: Eighty-seven patients with solid breast lesions (52 malignant and 35 benign) were examined on a 1.5 T MR scanner before histopathological evaluation. ADC values and ADC ratios were calculated. Results: The ADC values in the group with malignant tumors were significantly lower (mean 0.88 ± 0.15 × 10−3 mm2/s) in comparison with the group with benign lesions (mean 1.52 ± 0.23 × 10−3 mm2/s). A significantly lower ADC ratio was observed in the patients with malignant tumors (mean 0.66 ± 0.13) versus the patients with benign lesions (mean 1.12 ± 0.23). The cut-off point of the ADC value for differentiating malignant from benign breast tumors was 1.11 × 10−3 mm2/s with a sensitivity of 94.23%, specificity of 94.29%, and diagnostic accuracy of 98%, and an ADC ratio of ≤0.87 with a sensitivity of 94.23%, specificity of 91.43%, and a diagnostic accuracy of 95%. Conclusion: According to the results from our study DWI, ADC values and ADC ratio proved to be valuable additional techniques with high sensitivity and specificity for distinguishing benign from malignant breast lesions.

Diagnostic Performance of Diffusion MRI for differentiating Benign and Malignant Nonfatty Musculoskeletal Soft Tissue Tumors: A Systematic Review and Meta-analysis

Objective: To evaluate the diagnostic performance of standard diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI), for differentiating benign and malignant soft tissue tumors (STTs).

Materials and methods: A thorough search was carried out to identify suitable studies published up to September 2020. The quality of the studies involved was evaluated using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). The pooled sensitivity (SEN), specificity (SPE), and summary receiver operating characteristic (SROC) curve were calculated using bivariate mixed effects models. A subgroup analysis was also performed to explore the heterogeneity.

Results: Eighteen studies investigating 1319 patients with musculoskeletal STTs (malignant, n=623; benign, n=696) were enrolled. Thirteen standard DWI studies using the apparent diffusion coefficient (ADC) showed that the pooled SEN and SPE of ADC were 0.80 (95% CI: 0.77-0.82) and 0.63 (95% CI: 0.60-0.67), respectively. The area under the curve (AUC) calculated from the SROC curve was 0.806. The subgroup analysis indicated that the percentage of myxoid malignant tumors, magnet strength, study design, and ROI placement were significant factors affecting heterogeneity. Four IVIM studies showed that the AUCs calculated from the SROC curves of the parameters ADC and D were 0.859 and 0.874, respectively. The AUCs for the IVIM parameters pseudo diffusion coefficient (D*) and perfusion fraction (f) calculated from the SROC curve were 0.736 and 0.573, respectively. Two DKI studies showed that the AUCs of the DKI parameter mean kurtosis (MK) were 0.97 and 0.89, respectively.

Conclusion: The DWI-derived ADC value and the IVIM DWI-derived D value might be accurate tools for discriminating musculoskeletal STTs, especially for non-myxoid SSTs, using more than two b values, with maximal b value ranging from 600 to 800 s/mm², additionally, a high-field strength (3.0 T) optimizes the diagnostic performance.

Quantitative Multiparametric MRI as an Imaging Biomarker for the Prediction of Breast Cancer Receptor Status and Molecular Subtypes

Objectives

To assess breast cancer receptor status and molecular subtypes by using the CAIPIRINHA-Dixon-TWIST-VIBE and readout-segmented echo-planar diffusion weighted imaging techniques.

Methods

A total of 165 breast cancer patients were retrospectively recruited. Patient age, estrogen receptor, progesterone receptor, human epidermal growth factorreceptor-2 (HER-2) status, and the Ki-67 proliferation index were collected for analysis. Quantitative parameters (K^trans, V_e, K_ep), semiquantitative parameters (W_-in, W_-out, TTP), and apparent diffusion coefficient (ADC) values were compared in relation to breast cancer receptor status and molecular subtypes. Statistical analysis were performed to compare the parameters in the receptor status and molecular subtype groups.Multivariate analysis was performed to explore confounder-adjusted associations, and receiver operating characteristic curve analysis was used to assess the classification performance and calculate thresholds.

Results

Younger age (<49.5 years, odds ratio (OR) =0.95, P=0.004), lower K_ep (<0.704,OR=0.14, P=0.044),and higher TTP (>0.629 min, OR=24.65, P=0.011) were independently associated with progesterone receptor positivity. A higher TTP (>0.585 min, OR=28.19, P=0.01) was independently associated with estrogen receptor positivity. Higher K_ep (>0.892, OR=11.6, P=0.047), lower TTP (<0.582 min, OR<0.001, P=0.004), and lower ADC (<0.719 ×10^-3 mm²/s, OR<0.001, P=0.048) had stronger independent associations with triple-negative breast cancer (TNBC) compared to luminal A, and those parameters could differentiate TNBC from luminal A with the highest AUC of 0.811.

Conclusions

K_ep and TTP were independently associated with hormone receptor status. In addition, the K_ep, TTP, and ADC values had stronger independent associations with TNBC than with luminal A and could be used as imaging biomarkers for differentiate TNBC from Luminal A.

Accuracy of multiparametric MRI in distinguishing the breast malignant lesions from benign lesions: a meta-analysis

BACKGROUND

The sensitivity of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for detecting breast cancer was high and the specificity was relatively low. However, diffusion-weighted imaging (DWI) has a high specificity in the diagnosis of malignant lesions.

PURPOSE

To evaluate the accuracy of the multiparametric MRI (mp-MRI) in distinguishing the breast malignant lesions from the benign lesions.

MATERIAL AND METHODS

A comprehensive search of the PubMed, Embase, and Cochrane Library electronic databases was conducted up to March 2020. Data were analyzed for the following indexes: pooled sensitivity and specificity; positive likelihood ratio; negative likelihood ratio; diagnostic odds ratio; and the area under the curve.

RESULTS

A total of 2356 patients with 1604 malignant and 967 benign breast lesions were included from 22 studies. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and area under the curve for mp-MRI were 0.93, 0.85, 6.3, 0.08, 81, and 0.96, respectively. The pooled sensitivity, specificity, and area under the curve for DCE-MRI alone were 0.95, 0.71, and 0.92, respectively. The pooled sensitivity, specificity, and area under the curve for DWI alone were 0.88, 0.84, and 0.93, respectively.

CONCLUSION

The mp-MRI did not improve the sensitivity but increased the specificity for the diagnosis of breast malignant lesions.

Opportunities in cancer imaging: risk-adapted breast imaging in screening

In the UK, women between 50-70 years are invited for 3-yearly mammography screening irrespective of their likelihood of developing breast cancer. The only risk adaption is for women with >30% lifetime risk who are offered annual magnetic resonance imaging (MRI) and mammography, and annual mammography for some moderate-risk women. Using questionnaires, breast density, and polygenic risk scores, it is possible to stratify the population into the lowest 20% risk, who will develop <4% of cancers and the top 4%, who will develop 18% of cancers. Mammography is a good screening test but has low sensitivity of 60% in the 9% of women with the highest category of breast density (BIRADS D) who have a 2.5- to fourfold breast cancer risk. There is evidence that adding ultrasound to the screening mammogram can increase the cancer detection rate and reduce advanced stage interval and next round cancers. Similarly, alternative tests such as contrast-enhanced mammography (CESM) or abbreviated MRI (ABB-MRI) are much more effective in detecting cancer in women with dense breasts. Scintimammography has been shown to be a viable alternative for dense breasts or for follow-up in those with a personal history of breast cancer and scarring as result of treatment. For supplemental screening to be worthwhile in these women, new technologies need to reduce the number of stage II cancers and be cost effective when tested in large scale trials. This article reviews the evidence for supplemental imaging and examines whether a risk-stratified approach is feasible.

What's Hot in Breast MRI

Several articles in the literature have demonstrated a promising role for breast MRI techniques that are more economic in total exam time than others when used as supplement to mammography for detection and diagnosis of breast cancer. There are many technical factors that must be considered in the shortened breast MRI protocols to cut down time of standard ones, including using optimal fat suppression, gadolinium-chelates intravascular contrast administrations for dynamic imaging with post processing subtractions and maximum intensity projections (MIP) high spatial and temporal resolution among others. Multiparametric breast MRI that includes both gadolinium-dependent, i.e., dynamic contrast-enhanced (DCE-MRI) and gadolinium-free techniques, i.e., diffusion-weighted/diffusion-tensor magnetic resonance imaging (DWI/DTI) are shown by several investigators that can provide extremely high sensitivity and specificity for detection of breast cancer. This article provides an overview of the proven indications for breast MRI including breast cancer screening for higher than average risk, determining chemotherapy induced tumor response, detecting residual tumor after incomplete surgical excision, detecting occult cancer in patients presenting with axillary node metastasis, detecting residual tumor after incomplete breast cancer surgical excision, detecting cancer when results of conventional imaging are equivocal, as well patients suspicious of having breast implant rupture. Despite having the highest sensitivity for breast cancer detection, there are pitfalls, however, secondary to false positive and false negative contrast enhancement and contrast-free MRI techniques. Awareness of the strengths and limitations of different approaches to obtain state of the art MR images of the breast will facilitate the work-up of patients with suspicious breast lesions.

Diffusion-weighted magnetic resonance imaging for differentiating between benign and malignant thoracic lymph nodes: a meta-analysis

Objective

To establish the diagnostic performance of diffusion-weighted magnetic resonance imaging (DWI) in discriminating malignant from non-malignant thoracic lymph nodes.

Materials and Methods

This was a meta-analysis involving systematic searches of the MEDLINE, EMBASE, and Web of Science databases up through April 2020. Studies reporting thoracic DWI and lymph node evaluation were included. The pooled sensitivity, specificity, diagnostic odds ratio, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC) were calculated.

Results

We evaluated six studies, involving a collective total of 356 mediastinal lymph nodes in 214 patients. Thoracic DWI had a pooled sensitivity and specificity of 92% (95% confidence interval [95% CI]: 71-98%) and 93% (95% CI: 79-98%), respectively. The positive and negative likelihood ratios were 13.2 (95% CI: 4.0-43.8) and 0.09 (95% CI: 0.02-0.36), respectively. The diagnostic odds ratio was 149 (95% CI: 18-1,243), and the AUC was 0.97 (95% CI: 0.95-0.98).

Conclusion

DWI is a reproducible technique and has demonstrated high accuracy for differentiating between malignant and benign states in thoracic lymph nodes.

Quantitative whole-body MR imaging for assessment of tumor burden in patients with multiple myeloma: correlation with prognostic biomarkers

Background

To assess the quantification of tumor burden in multiple myeloma (MM) patients using whole-body magnetic resonance imaging (MRI) and to identify the correlation between MRI parameters and prognostic biomarkers.

Methods

We retrospectively analyzed 95 newly diagnosed MM patients treated at our hospital from June 2018 to March 2020. All patients underwent whole-body MRI examination, including diffusion-weighted whole-body imaging with background body signal suppression (DWIBS), modified Dixon chemical-shift imaging (mDIXON), and short TI inversion recovery (STIR) sequences. The MRI presentation was used to determine MM infiltration patterns and calculate apparent diffusion coefficient (ADC) and a fat fraction (FF). The one-way ANOVA and Kruskal-Wallis test were used to compare the differences of these values between DS, ISS, and R-ISS stages in different MM infiltration patterns. Spearman correlation test was used for correlation analysis of ADC and FF against prognostic biomarkers, and two independent sample t-test was used to evaluate the differences of ADC and FF in different free light-chain ratio groups.

Results

The MRI presentation was classified into normal pattern (36 patients; 37.9%), diffuse (27 patients; 28.4%), and focal (32 patients; 33.7%) infiltration patterns. Statistically significant ADC and FF differences between different DS, ISS, and R-ISS stages were observed in normal/diffuse infiltration patterns but not in focal infiltration patterns. The ADC and FF of the normal/diffuse infiltration pattern showed correlations with hemoglobin, β2-microglobulin, bone marrow plasma cells, flow cytometry of bone marrow cells, and serum monoclonal protein. In contrast, ADC in focal infiltration patterns was negatively correlated with β2-microglobulin and C-reactive protein. The FF of patients with a normal/diffuse infiltration pattern was higher in the low free light-chain ratio group than that in the high free light-chain ratio group (P=0.023).

Conclusions

Our observations indicate that quantitative whole-body functional MRI examination may serve as an effective complement to imaging diagnosis based on morphology and provide further information on the tumor burden of patients with MM.

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.

A Multicentric Comparison of Apparent Diffusion Coefficient Mapping and the Kaiser Score in the Assessment of Breast Lesions

MATERIALS AND METHODS

In this multicentric study, individual patient data from 3 different centers were analyzed. Consecutive patients receiving standardized multiparametric breast magnetic resonance imaging for standard nonscreening indications were included. At each center, 2 experienced radiologists with more than 5 years of experience retrospectively interpreted the examinations in consensus and applied the KS to every histologically verified lesion. The corresponding mean ADC of each lesion was measured using a Wielema type 4 region of interest. According to established methods, the KS and ADC were combined, yielding the KS+ score. Diagnostic accuracy was evaluated by the area under the receiver operating characteristics curve (AUROC) and compared between the KS, ADC, and KS+ (DeLong test). Likewise, the potential to help avoid unnecessary biopsies was compared between the KS, ADC, and KS+ based on established high sensitivity thresholds (McNemar test).

RESULTS

A total of 450 lesions in 414 patients (mean age, 51.5 years; interquartile range, 42-60.8 years) were included, with 219 lesions being malignant (48.7%; 95% confidence interval [CI], 44%-53.4%). The performance of the KS (AUROC, 0.915; CI, 0.886-0.939) was significantly better than that of the ADC (AUROC, 0.848; CI, 0.811-0.880; P < 0.001). The largest difference between these parameters was observed when assessing subcentimeter lesions (AUROC, 0.909 for KS; CI, 0.849-0.950 vs 0.811 for ADC; CI, 0.737-0.871; P = 0.02).The use of the KS+ (AUROC, 0.918; CI, 0.889-0.942) improved the performance slightly, but without any significant difference relative to a single KS or ADC reading (P = 0.64).When applying high sensitivity thresholds for avoiding unnecessary biopsies, the KS and ADC achieved equal sensitivity (97.7% for both; cutoff values, >4 for KS and ≤1.4 × 10-3 mm2/s for ADC). However, the rate of potentially avoidable biopsies was higher when using the KS (specificity: 65.4% for KS vs 32.9% for ADC; P < 0.0001). The KS was superior to the KS+ in avoiding unnecessary biopsies.

CONCLUSIONS

Both the KS and ADC may be used to distinguish benign from malignant breast lesions. However, KS proved superior in this task including, most of all, when assessing small lesions less than 1 cm. Using the KS may avoid twice as many unnecessary biopsies, and the combination of both the KS and ADS does not improve diagnostic performance.

Accuracy of quantitative diffusion-weighted imaging for differentiating benign and malignant pancreatic lesions: a systematic review and meta-analysis

BACKGROUND

A variety of imaging techniques can be used to evaluate diffusion characteristics to differentiate malignant and benign pancreatic lesions. The diagnostic performance of diffusion parameters has not been systematic assessed.

PURPOSE

We aimed to investigate the diagnostic efficacy of quantitative diffusion-weighted imaging (DWI) for pancreatic lesions.

METHODS

A literature search was conducted using the PubMed, Embase, and Cochrane Library databases for studies from inception to March 30, 2020, which involves the quantitative diagnostic performance of diffusion-weighted imaging (DWI) and intravoxel incoherent motion (IVIM) in the pancreas. Studies were reviewed according to inclusion and exclusion criteria. The quality of articles was evaluated by the Quality Assessment of Diagnostic Accuracy Studies-2 (QUATAS-2). A bivariate random-effects model was used to evaluate pooled sensitivities and specificities. Univariable meta-regression analysis was used to test the effects of factors that contributed to the heterogeneity.

RESULTS

A total of 31 studies involving 1558 patients were ultimately eligible for data extraction. The lowest heterogeneity was found in specificity of perfusion fraction (f) with the I² value was 17.97% and Cochran p value was 0.28. However, high heterogeneities were found for the other parameters (all I² > 50%). There was no publication bias found in funnel plot (p = 0.30) for the apparent diffusion coefficient (ADC) parameter. The pooled sensitivities for ADC, f, pure diffusion coefficient (D), and pseudo diffusivity coefficient (D*) were 83%, 81%, 76%, and 84%, respectively. The pooled specificities for ADC, f, D, and D* were 87%, 83%, 69%, and 81% respectively. The areas under the curves for ADC, f, D, and D* were 0.92, 0.87, 0.79, and 0.87 respectively.

CONCLUSION

Quantitative DWI and IVIM have a good diagnostic performance for differentiating malignant and benign pancreatic lesions.

KEY POINTS

• IVIM has high sensitivity and specificity (84% and 83%, respectively) for differential diagnosis of pancreatic lesions, which is comparable to that of the ADC (83% and 87%, respectively). • The ADC has an excellent diagnostic performance for differentiating malignant from benign IPMNs (sensitivity, 0.83; specificity, 0.92); the f has the best diagnostic performance for differentiating pancreatic carcinoma from PNET (sensitivity, 0.85; specificity, 0.85). • For the ADC, using a maximal b value < 800 s/mm² has a higher diagnostic accuracy than ≥ 800 s/mm²; performing in a high field strength (3.0 T) system has a higher diagnostic accuracy than a low field strength (1.5 T) for pancreatic lesions.

A meta-analysis comparing the diagnostic performance of abbreviated MRI and a full diagnostic protocol in breast cancer

AIM

To undertake a meta-analysis of the diagnostic performance of abbreviated (ABB) magnetic resonance imaging (MRI) and full diagnostic protocol MRI (FDP-MRI) in breast cancer.

MATERIALS AND METHODS

This meta-analysis was performed using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis for Diagnostic Test Accuracy (PRISMA-DTA) guidelines. The PubMed and EMBASE databases were searched through August 2019 for studies comparing the diagnostic performance of ABB-MRI and FDP-MRI in the breast. Studies were reviewed by two authors independently according to eligibility and exclusion criteria and split into two subgroups (screening population studies and studies using cohorts enriched with known cancers) to avoid bias. Quality assessment and bias for diagnostic accuracy was determined with Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). The diagnostic accuracy for each subgroup was pooled using a bivariate random effects model and summary receiver operating characteristic (sROC) curves produced. Sensitivities and specificities were compared using a paired t-test.

RESULTS

Five screening (62/2,588 cancers/patients) and eight enriched cohort (540/1,432 cancers/patients) studies were included in the meta-analysis. QUADAS-2 assessment showed a low risk of bias in most studies. The pooled sensitivity/specificity/area under the receiver operating characteristic curve (AUC) for screening studies was 0.90/0.92/0.94 for ABB-MRI and 0.92/0.95/0.97 for FDP-MRI. The pooled sensitivity/specificity/AUC for enriched cohort studies was 0.93/0.83/0.94 for ABB-MRI and 0.93/0.84/0.95 for FDP-MRI. There was no significant difference in sensitivity or specificity using ABB-MRI or FDP-MRI (p=0.18 and 0.27, p=0.18 and 0.93, respectively).

CONCLUSION

The diagnostic performances of the ABB-MRI and FDP-MRI protocols used in either screening or enriched cohorts were comparable. There was a large variation in patient population, study methodology, and abbreviated protocols reported.

Diffusion-Weighted Imaging as a Stand-Alone Breast Imaging Modality

확산강조영상은 유방암의 진단과 스크리닝에 있어 독립적 검사 방법으로서의 기대되는 결과를 보여주는 빠른 비조영증강 검사 방법이다. 현재까지의 연구 결과 유방암 진단에 있어 독립적 검사 방법으로서 확산강조영상의 민감도는 역동적 조영증강 검사보다는 낮으나 유방촬영술보다는 높으며, 이로써 유방암 스크리닝에 대한 유용한 대안이 될 수 있을 것으로 보인다. 확산강조영상의 표준화된 영상 획득과 판독을 통해 영상 화질이 개선될 수 있고, 판독 결과의 다양성도 감소할 것으로 기대된다. 또한, 최신 기법과 후처리 기법을 사용한 고해상도 확산강조영상을 시행함으로써 1 cm 미만의 작은 암의 발견율을 증가시킬 수 있고, 가음성 및 가양성 결과를 감소시킬 것으로 보인다. 현재 한국에서 진행 중인 고위험군 여성에서의 확산강조영상 스크리닝에 대한 다기관 연구 결과가 나온다면 독립적 검사로서의 확산강조영상의 사용을 촉진시킬 수 있을 것으로 기대된다.

The association between breast cancer risk factors and background parenchymal enhancement at dynamic contrast-enhanced breast MRI

Background

Background parenchymal enhancement (BPE) of normal tissue at breast magnetic resonance imaging is suggested to be an independent risk factor for breast cancer. Its association with established risk factors for breast cancer is not fully investigated.

Purpose

To study the association between BPE and risk factors for breast cancer in a healthy, non-high-risk screening population.

Material and Methods

We measured BPE and mammographic density and used data from self-reported questionnaires in 214 healthy women aged 43–74 years. We estimated odds ratios for the univariable association between BPE and risk factors. We then fitted an adjusted model using logistic regression to evaluate associations between BPE (high vs. low) and risk factors, including mammographic breast density.

Results

The majority of women had low BPE (84%). In a multivariable model, we found statistically significant associations between BPE and age (P = 0.002) and BMI (P = 0.03). We did find a significant association between systemic progesterone medication and BPE, but due to small numbers, the results should be interpreted with caution. The adjusted odds ratio for high BPE was 3.1 among women with density D (compared to B) and 2.1 for density C (compared to B). However, the association between high BPE and density was not statistically significant. We did not find statistically significant associations with any other risk factors.

Conclusion

Our study confirmed the known association of BPE with age and BMI. Although our results show a higher likelihood for high BPE with increasing levels of mammographic density, the association was not statistically significant.