Histogram analysis of diffusion kurtosis imaging in the differentiation of malignant from benign breast lesions

Prediction of neoadjuvant chemotherapy efficacy in breast cancer: integrating multimodal imaging and clinical features

OBJECTIVES

To assess the predictive value of combining DCE-MRI, DKI, IVIM parameters, and clinical characteristics for neoadjuvant chemotherapy (NAC) efficacy in invasive ductal carcinoma.

METHODS

We conducted a retrospective study of 77 patients with invasive ductal carcinoma, analyzing MRI data collected before NAC. Parameters extracted included DCE-MRI (Ktrans, Kep, Ve, wash-in, wash-out, TTP, iAUC), DKI (MK, MD), and IVIM (D, D*, f). Differences between NAC responders and non-responders were assessed using t-tests or Mann-Whitney U tests. ROC curves and Spearman correlation analyses evaluated predictive accuracy.

RESULTS

NAC responders had higher DCE-MRI-Kep, DKI-MD, IVIM-D, and IVIM-f values. Non-responders had higher DCE-MRI-Ve, DKI-MK, IVIM-D (kurtosis, skewness, entropy), and IVIM-f (entropy). The mean DKI-MK had the highest AUC (0.724), and IVIM-D interquartile range showed the highest sensitivity (94.12%). Combined parameters had the highest AUC (0.969), sensitivity (94.12%), and specificity (90.70%). HER2 status (OR, 0.187; 95% CI: 0.038, 0.914; P = 0.038) and tumor margin (OR, 20.643; 95% CI: 2.892, 147.365; P = 0.003) were identified as independent factors influencing the lack of significant efficacy of neoadjuvant chemotherapy (NAC) in breast cancer.

CONCLUSIONS

Combining DCE-MRI, DKI, and IVIM parameters effectively predicts NAC efficacy, providing valuable preoperative assessment insights.

CLINICAL TRIAL NUMBER

Not applicable.

Application of a combined clinical prediction model based on enhanced T1-weighted image(T1WI) full volume histogram in peripheral nerve invasion (PNI) and lymphatic vessel invasion (LVI) in rectal cancer

PURPOSE

This study aims to use a combined clinical prediction model based on enhanced T1-weighted image(T1WI) full volume histogram to predict preoperative peripheral nerve invasion (PNI) and lymphatic vessel invasion (LVI) in rectal cancer.

METHODS

We included a total of 68 PNI patients and 80 LVI patients who underwent surgical resection and pathological confirmation of rectal cancer. According to the PNI/LVI status, patients were divided into PNI positive group (n = 39), the PNI negative group (n = 29), LVI positive group (n = 48), and the LVI negative group (n = 32). External validation included a total of 42 patients with nerve and vascular invasion in patients with surgically resected and pathologically confirmed rectal cancer at another healthcare facility, with a PNI positive group (n = 32) and a PNI-negative group (n = 10) as well as an LVI positive group (n = 35) and LVI-negative group (n = 7). All patients underwent 3.0T magnetic resonance T1WI enhanced scanning. We use Firevoxel software to delineate the region of interest (ROI), extract histogram parameters, and perform univariate analysis, LASSO regression, and multivariate logistic regression analysis in sequence to screen for the best predictive factors. Then, we constructed a clinical prediction model and plotted it into a column chart for personalized prediction. Finally, we evaluate the performance and clinical practicality of the model based on the area under curve (AUC), calibration curve, and decision curve.

RESULTS

Multivariate logistic regression analysis found that variance and the 75th percentile were independent risk factors for PNI, while maximum and variance were independent risk factors for LVI. The clinical prediction model constructed based on the above factors has an AUC of 0.734 (95% CI: 0.591-0.878) for PNI in the training set and 0.731 (95% CI: 0.509-0.952) in the validation set; The training set AUC of LVI is 0.701 (95% CI: 0.561-0.841), and the validation set AUC is 0.685 (95% CI: 0.439-0.932). External validation showed an AUC of 0.722 (95% CI: 0.565-0.878) for PNI; and an AUC of 0.706 (95% CI: 0.481-0.931) for LVI.

CONCLUSIONS

This study indicates that the combination of enhanced T1WI full volume histogram and clinical prediction model can be used to predict the perineural and lymphovascular invasion status of rectal cancer before surgery, providing valuable reference information for clinical diagnosis.

Histogram analysis of intravoxel incoherent motion imaging: Correlation with molecular prognostic factors and combined subtypes of breast cancer

PURPOSE

To look for links between diffusion and IVIM parameters and different molecular subtypes and prognostic factors through histogram analysis.

MATERIALS AND METHODS

A total of 139 patients with breast cancer who had pre-operative MRI examinations were enrolled in this retrospective study. Histograms of the diffusion and IVIM parameters were analyzed for the whole tumor, and an association was investigated between the parameters and the different molecular prognostic factors and subtypes using the nonparametric test, Spearman's rank correlation, and receiver operating characteristic (ROC) curve.

RESULTS

The histogram metrics of the diffusion and IVIM parameters were significantly different for molecular prognostic factors such as human epidermal receptor factor-2 (HER2), progesterone receptor, estrogen receptor, and ki-67. All histogram metrics displayed a poor correlation with all groups (r = -0.28-0.29). There were significant differences in the histogram metrics for the Luminal B-HER2 (-) vs. HER2-positive (non-luminal) subtypes in the mean and 10th percentile D, with the area under the curves (AUCs) of 0.742 and 0.700, respectively, and for the Luminal A and HER2-positive (non-luminal) subtypes in the 90th percentile and entropy of D*, with AUCs of 0.769 and 0.727, respectively.

CONCLUSION

The histogram metrics of IVIM parameters exhibited links with breast cancer prognosis factors and combined subtypes.

Histogram analysis comparison of readout-segmented and single-shot echo-planar imaging for differentiating luminal from non-luminal breast cancer

To compare diffusion-kurtosis imaging (DKI) and diffusion-weighted imaging (DWI) parameters of single-shot echo-planar imaging (ss-EPI) and readout-segmented echo-planar imaging (rs-EPI) in the differentiation of luminal vs. non-luminal breast cancer using histogram analysis. One hundred and sixty women with 111 luminal and 49 non-luminal breast lesions were enrolled in this study. All patients underwent ss-EPI and rs-EPI sequences on a 3.0T scanner. Histogram metrics were derived from mean kurtosis (MK), mean diffusion (MD) and the apparent diffusion coefficient (ADC) maps of two DWI sequences respectively. Student's t test or Mann-Whitney U test was performed for differentiating luminal subtype from non-luminal subtype. The ROC curves were plotted for evaluating the diagnostic performances of significant histogram metrics in differentiating luminal from non-luminal BC. The histogram metrics MKmean, MK50th, MK75th of luminal BC were significantly higher than those of non-luminal BC for both two DWI sequences (all P<0.05). Histogram metrics from rs-EPI sequence had better diagnostic performance in differentiating luminal from non-Luminal breast cancer compared to those from ss-EPI sequence. MK75th derived from rs-EPI sequence was the most valuable single metric (AUC, 0.891; sensitivity, 78.4%; specificity, 87.8%) for differentiating luminal from non-luminal BC among all the histogram metrics. Histogram metrics of MK derived from rs-EPI yielded better diagnostic performance for distinguishing luminal from non-luminal BC than that from ss-EPI. MK75th was the most valuable metric among all the histogram metrics.

Kinetic Heterogeneity Improves the Specificity of Dynamic Enhanced MRI in Differentiating Benign and Malignant Breast Tumours

RATIONALE AND OBJECTIVES

To investigate whether kinetic heterogeneity in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) improves the specificity of breast cancer (BC) diagnosis.

MATERIALS AND METHODS

The DCE-MRI data of patients with benign breast tumours and BC from June 2020 to July 2022 were retrospectively evaluated. MATLAB and SPM were used to determine six major kinetic parameters: peak, enhancement volume, heterogeneity, as well as persistent, plateau, and washout proportions. Continuous variables were compared using the Student's t-test or Mann-Whitney U tests, and categorical variables were compared using the chi-square or Fisher's exact tests. Receiver operating characteristic curves were plotted. The intraclass correlation coefficient (ICC) was used to evaluate agreement between the two observers. Multivariate logistic regression analysis was conducted to calculate the odds ratios (ORs) with 95% confidence intervals (CIs) for the association between benign and malignant breast tumours.

RESULTS

In total, 147 patients (mean age, 47 years old) were included in the study, 76 of whom had BC. Data analysis by the two observers showed good consistency in the peak, enhancement volume, persistent proportion, plateau proportion, washout proportion, and heterogeneity, with ICCs of 0.865, 0.988, 0.906, 0.940, 0.740, and 0.867, respectively (p < 0.001). In the DCE kinetic analysis, differences in all the six kinetic parameters were statistically significant (p < 0.05). The area under the curve for heterogeneity was 0.92 (95% CI:0.88,0.97), and the sensitivity and specificity were 0.895 and 0.845, respectively. Multivariate logistic regression analysis showed that heterogeneity was an independent predictor of BC compared to benign breast tumours (OR=2.020; 95% CI:1.316, 3.100; p = 0.001).

CONCLUSION

The kinetic heterogeneity of DCE-MRI can effectively distinguish between benign and malignant breast tumours and improve the specificity of BC diagnosis.

Predicting histopathological types and molecular subtype of breast tumors: A comparative study using amide proton transfer-weighted imaging, intravoxel incoherent motion and diffusion kurtosis imaging

PURPOSE

To evaluate the predictive performance of multiparameter and histogram features derived from amide proton transfer-weighted imaging (APTWI), intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) for histopathological types of breast tumors.

METHODS

Region of interest (ROI) was delineated by outlining the largest slice of the tumor on the false-color images of the DKI, IVIM and APTWI parameters, and extracted the histogram features. Receiver operating characteristic (ROC) curve was used to evaluate the performance of parameters in predicting benign and malignant breast lesions, molecular prognostic biomarkers, lymph node status, and subtypes of breast lesions. The Spearman correlation coefficient was used to determine the correlations between each parameter and clinical-pathological factors.

RESULTS

All 52 breast lesions were enrolled in this prospective study, including 8 benign lesions and 44 breast cancers. To diagnose malignant and benign breast lesions, the value of APT (min) performed best, with the AUC reaching 0.983. According to the different imaging methods, the APTWI performed best. To predict the positive status of ER, PR, Ki67, the value of Dapp (uniformity), Dapp (uniformity), f (entropy) performed best, with the AUC values reaching 0.743, 0.770, 0.848, respectively. For the identification of Luminal B, HER2-enriched, and TNBC breast cancers, Kapp (max), f (kurtosis), and Dapp (uniformity) performed best, with AUC values reaching 0.679, 0.826, 0.771, respectively.

CONCLUSION

This study found the APTWI, IVIM and DKI parameters could diagnose breast cancer. The histogram features of DKI and IVIM, based on tumor heterogeneity, may help to predict breast cancer subtypes.

Intraobserver and Interobserver Reproducibility of Breast Diffusion-Weighted Imaging Quantitative Parameters: Readout-Segmented vs. Single-Shot Echo-Planar Imaging

BACKGROUND

The recommended technique for breast diffusion-weighted imaging (DWI) acquisitions is not sufficiently standardized in clinical practice.

PURPOSE

To investigate the intraobserver and interobserver reproducibility of DWI measurements, diffusion-kurtosis imaging (DKI) parameters, and image quality evaluation in breast lesions between single-shot echo-planar imaging (ss-EPI) and readout-segmented echo-planar imaging (rs-EPI).

STUDY TYPE

Prospective.

POPULATION

A total of 295 women with 209 malignant and 86 benign breast lesions.

FIELD STRENGTH/SEQUENCE

A 3-T; fat-saturated T2-weighted MR imaging (T2WI); multi-b-value DWI with both ss-EPI and rs-EPI readouts; T1-weighted dynamic contrast-enhanced MRI (DCE-MRI).

ASSESSMENT

Mean kurtosis (MK), mean diffusion (MD), and apparent diffusion coefficient (ADC) values were measured for each lesion on ss-EPI and rs-EPI, respectively. Image quality was visually evaluated regarding image sharpness, geometric distortion, lesion conspicuity, visualization of anatomic structures, and overall quality. Quantitative and qualitative analyses were performed twice with a time interval of 2 weeks.

STATISTICAL TESTS

Intraobserver and interobserver reproducibility were evaluated using intra-class correlation coefficients (ICC), within-subject coefficient of variation (wCV), and Bland-Altman plots.

RESULTS

MK, MD, and ADC quantitative parameters for breast lesions showed excellent intraobserver and interobserver reproducibility, with ICCs >0.75 and wCV values ranging from 2.51% to 7.08% for both sequences. The wCV values in both intraobserver and interobserver measurements were higher in the ss-EPI sequence (3.63%-7.08%) than that of the rs-EPI sequence (2.51%-3.62%). The wCV values differed in subgroups with different histopathological types of lesions, breast density, lesion morphology, and lesion sizes, respectively. Furthermore, rs-EPI (ICCs, 0.76-0.97; wCV values, 2.41%-6.04%) had better intraobserver and interobserver reproducibility than ss-EPI (ICCs, 0.54-0.90; wCV values, 6.18%-13.69%) with regard to image quality.

DATA CONCLUSION

Compared to the ss-EPI, the rs-EPI sequence showed higher intraobserver and interobserver reproducibility for quantitative diffusion-related parameters and image quality assessments measured in breast DWI and DKI.

EVIDENCE LEVEL

2.

TECHNICAL EFFICACY

Stage 2.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

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.

Differentiating Benign and Malignant Breast Lesions in Diffusion Kurtosis MRI: Does the Averaging Procedure Matter?

BACKGROUND

Diffusion kurtosis imaging (DKI) is used to differentiate between benign and malignant breast lesions. DKI fits are performed either on voxel-by-voxel basis or using volume-averaged signal.

PURPOSE

Investigate and compare DKI parameters' diagnostic performance using voxel-by-voxel and volume-averaged signal fit approach.

STUDY TYPE

Retrospective.

STUDY POPULATION

A total of 104 patients, aged 24.1-86.4 years.

FIELD STRENGTH/SEQUENCE

A 3 T Spin-echo planar diffusion-weighted sequence with b-values: 50 s/mm² , 750 s/mm² , and 1500 s/mm² . Dynamic contrast enhanced (DCE) sequence.

ASSESSMENT

Lesions were manually segmented by M.P. under supervision of S.O. (2 and 5 years of experience in breast MRI). DKI fits were performed on voxel-by-voxel basis and with volume-averaged signal. Diagnostic performance of DKI parameters D K (kurtosis corrected diffusion coefficient) and kurtosis K was compared between both approaches.

STATISTICAL TESTS

Receiver operating characteristics analysis and area under the curve (AUC) values were computed. Wilcoxon rank sum and Students t-test tested DKI parameters for significant (P <0.05) difference between benign and malignant lesions. DeLong test was used to test the DKI parameter performance for significant fit approach dependency. Correlation between parameters of the two approaches was determined by Pearson correlation coefficient.

RESULTS

DKI parameters were significantly different between benign and malignant lesions for both fit approaches. Median benign vs. malignant values for voxel-by-voxel and volume-averaged approach were 2.00 vs. 1.28 ( D K in μm² /msec), 2.03 vs. 1.26 ( D K in μm² /msec), 0.54 vs. 0.90 ( K ), 0.55 vs. 0.99 ( K ). AUC for voxel-by-voxel and volume-averaged fit were 0.9494 and 0.9508 ( D K ); 0.9175 and 0.9298 ( K ). For both, AUC did not differ significantly (P = 0.20). Correlation of values between the two approaches was very high (r = 0.99 for D K and r = 0.97 for K ).

DATA CONCLUSION

Voxel-by-voxel and volume-averaged signal fit approach are equally well suited for differentiating between benign and malignant breast lesions in DKI.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 3.

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.

Diagnostic Performance of Diffusion Kurtosis Imaging for Benign and Malignant Breast Lesions: A Systematic Review and Meta-Analysis

Purpose

Magnetic resonance imaging (MRI) has a high sensitivity for differentiating between malignant and non-malignant breast lesions but is sometimes limited due to its low specificity. Here, we performed a meta-analysis to evaluate the diagnostic performance of mean kurtosis (MK) and mean diffusivity (MD) values in magnetic resonance diffusion kurtosis imaging (DKI) for benign and malignant breast lesions.

Methods

Original articles on relevant topics, published from 2010 to 2019, in PubMed, EMBASE, and WanFang databases were systematically reviewed. According to the purpose of the study and the characteristics of DKI reported, the diagnostic performances of MK and MD were evaluated, and meta-regression was conducted to explore the source of heterogeneity.

Results

Fourteen studies involving 1,099 (451 benign and 648 malignant) lesions were analyzed. The pooled sensitivity, pooled specificity, positive likelihood ratio, and negative likelihood ratio for MD were 0.84 (95% confidence interval (CI), 0.81-0.87), 0.83 (95% CI, 0.79-0.86), 4.44 (95% CI, 3.54-5.57), and 0.18 (95% CI, 0.13-0.26), while those for MK were 0.89 (95% CI, 0.86-0.91), 0.86 (95% CI, 0.82-0.89), 5.72 (95% CI, 4.26-7.69), and 0.13 (95% CI, 0.09-0.19), respectively. The overall area under the curve (AUC) was 0.91 for MD and 0.95 for MK.

Conclusions

Analysis of the data from 14 studies showed that MK had a higher pooled sensitivity, pooled specificity, and diagnostic performance for differentiating between breast lesions, compared with MD.

Association Between DCE-MRI Perfusion Histogram Parameters and EGFR and VEGF Expressions in Different Lauren Classifications of Advanced Gastric Cancer

Objective: To investigate the correlations between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) perfusion histogram parameters and vascular endothelial growth factor (VEGF) and epidermal growth factor receptor (EGFR) expressions in advanced gastric cancer (AGC).

Methods: This retrospective study included 80 pathologically confirmed patients with AGC who underwent DCE-MRI before surgery from February 2017 to May 2021. The DCE-MRI perfusion histogram parameters were calculated by Omni Kinetics software in four quantitative parameter maps. Immunohistochemical methods were used to detect VEGF and EGFR expressions and calculate the immunohistochemical score.

Results: VEGF expression was relatively lower in patients with intestinal-type AGC than those with diffuse-type AGC (p < 0.05). For VEGF, Receiver operating characteristics (ROC) curve analysis revealed that Quantile 90 of Ktrans, Meanvalue of Kep and Quantile 50 of Ve provided the perfect combination of sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for distinguishing high and low VEGF expression, For EGFR, Skewness of Ktrans, Energy of Kep and Entropy of Vp provided the perfect combination of sensitivity, specificity, PPV and NPV for distinguishing high and low EGFR expression. Ktrans (Quantile 90, Entropy) showed the strongest correlation with VEGF and EGFR in patients with intestinal-type AGC (r = 0.854 and r = 0.627, respectively); Ktrans (Mean value, Entropy) had the strongest correlation with VEGF and EGFR in patients with diffuse-type AGC (r = 0.635 and 0.656, respectively).

Conclusion: DCE-MRI perfusion histogram parameters can serve as imaging biomarkers to reflect VEGF and EGFR expressions and estimate their difference in different Lauren classifications of AGC.

Evaluation of suspicious breast lesions with diffusion kurtosis MR imaging and connection with prognostic factors

PURPOSE

To investigate the additional value of DKI in discriminating suspicious breast lesions on DCE-MRI, as compared with conventional DWI; and to explore connection between DKI-parameters and prognostic factors of breast cancers.

METHODS

The institutional review board approved this retrospective study and written informed consent was waived. Totally, 300 women (mean age, 43.2 ± 10.4 years) with suspicious breast lesions on DCE-MRI were enrolled from November 2014 to September 2019. With pathology as reference, performance of ADC, K_app and D_app in discriminating suspicious breast lesions were analyzed by receiver operating characteristic (ROC) analysis with area under ROC curve (AUC). The specificities of parameters were compared by Chi-square test. The ADC, K_app and D_app of breast cancers with different receptor status were compared using Student's t or Mann-Whitney U or Kruskal-Wallis test.

RESULTS

There were 344 suspicious breast lesions (220 malignant, 124 benign) in 300 women. No significant differences were found for AUCs of ADC and DKI-parameters in discriminating suspicious breast lesions (0.882 vs. 0.888, p = 0.480). The specificities were significantly higher with ADC and D_app than that with DCE-MRI (p = 0.003 and 0.005). The ADC, K_app and D_app were correlated with HER2 expression and lymph node status, and ADC and K_app differed between ER-positive and negative tumors (all p < 0.05). Except K_app, DKI/DWI-parameters showed relation with Ki-67 expression. None of the DKI/DWI-parameters showed relation with lesion grade (all p > 0.05).

CONCLUSION

The more complicated and time-consuming DKI is not superior to conventional DWI in differentiating suspicious breast lesions and reflecting prognostic information of breast cancer.

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.

Accelerating acquisition of readout-segmented echo planar imaging with a simultaneous multi-slice (SMS) technique for diagnosing breast lesions

OBJECTIVES

To investigate the feasibility and effectiveness of SMS rs-EPI for evaluating breast lesions.

METHODS

This prospective study was approved by IRB. Ninety-six patients had 102 histopathologically verified lesions (80 malignant and 22 benign) that were evaluated. Conventional rs-EPI and SMS rs-EPI data were acquired on a 3T scanner. Mean kurtosis (MK), mean diffusion (MD), and apparent diffusion coefficient (ADC) values were quantitatively calculated for each lesion on both sequences. Images were qualitatively and quantitatively analyzed with respect to image sharpness, geometric distortion, lesion conspicuity, anatomic structure, overall image quality, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). Student's t test, Pearson correlation, receiver operating characteristic curve, Wilcoxon rank sum test, and paired-sample t tests were used for statistical analysis.

RESULTS

Compared to conventional rs-EPI, the acquisition time of SMS rs-EPI was markedly reduced (2:17 min vs 4:27 min). Pearson's correlations showed excellent linear relationships for each parameter between conventional rs-EPI and SMS rs-EPI (MK, r = 0.908; MD, r = 0.938; and ADC, r = 0.975; p < 0.01 for all). Furthermore, SMS rs-EPI had similar diagnostic performance compared with conventional rs-EPI. SMS rs-EPI had comparable visual image quality as conventional rs-EPI, with excellent inter-reader reliability (ICC = 0.851-0.940). No differences existed between conventional rs-EPI and SMS rs-EPI for either SNR or CNR (p > 0.05).

CONCLUSIONS

Applying the SMS technique can significantly reduce the acquisition time and produce similar diagnostic accuracy while generating comparable image quality as the conventional rs-EPI.

KEY POINTS

• SMS rs-EPI reduces scan time from 4:27 min to 2:17 min compared with conventional rs-EPI. • SMS rs-EPI has a comparable diagnostic performance to conventional rs-EPI in the differentiation between malignant and benign breast lesions. • SMS rs-EPI demonstrates comparable image quality to conventional rs-EPI with shorter scan time.

The Diagnostic Performance of Diffusion Kurtosis Imaging in the Characterization of Breast Tumors: A Meta-Analysis

Rationale and Objectives: Diffusion kurtosis imaging (DKI) is a promising imaging technique, but the results regarding the diagnostic performance of DKI in the characterization and classification of breast tumors are inconsistent among published studies. This study aimed to pool all published results to provide more robust evidence of the differential diagnosis between malignant and benign breast tumors using DKI. Methods: Studies on the differential diagnosis of breast tumors using DKI-derived parameters were systemically retrieved from PubMed, Embase, and Web of Science without a time limit. Review Manager 5.3 was used to calculate the standardized mean differences (SMDs) and 95% confidence intervals of the mean kurtosis (MK), mean diffusivity (MD), and apparent diffusion coefficient (ADC). Stata 12.0 was used to pool the sensitivity, specificity, and diagnostic odds ratio (DOR) as well as the publication bias and heterogeneity of each parameter. Fagan's nomograms were plotted to predict the post-test probabilities. Results: Thirteen studies including 867 malignant and 460 benign breast lesions were analyzed. Most of the included studies showed a low to unclear risk of bias and low concerns regarding applicability. Breast cancer showed a higher MK (SMD = 1.23, P < 0.001) but a lower MD (SMD = -1.29, P < 0.001) and ADC (SMD = -1.21, P < 0.001) than benign tumors. The MK (SMD = -1.36, P = 0.006) rather than the MD (SMD = 0.29, P = 0.20) or ADC (SMD = 0.26, P = 0.24) can further differentiate invasive ductal carcinoma from ductal carcinoma in situ. The DKI-derived MK (sensitivity = 90%, specificity = 88%, DOR = 66) and MD (sensitivity = 86% and specificity = 88%, DOR = 46) demonstrated superior diagnostic performance and post-test probability (65, 64, and 56% for MK, MD, and ADC) in differentiating malignant from benign breast lesions, with a higher sensitivity and specificity than the DWI-derived ADC (sensitivity = 85% and specificity = 83%, DOR = 29). Conclusion: The DKI-derived MK and MD demonstrate a comparable diagnostic performance in the discrimination of breast tumors based on their microstructures and non-Gaussian characteristics. The MK can further differentiate invasive ductal carcinoma from ductal carcinoma in situ.

A comparative study of the value of amide proton transfer-weighted imaging and diffusion kurtosis imaging in the diagnosis and evaluation of breast cancer

OBJECTIVES

To compare the value of amide proton transfer-weighted imaging (APTWI) and diffusion kurtosis imaging (DKI) in differentiating benign and malignant breast lesions and analyze the correlations between the derived parameters and prognostic factors of breast cancer.

METHODS

One hundred thirty-five women underwent breast APTWI and DKI. The magnetization transfer ratio asymmetry (MTRasym (3.5 ppm)), apparent kurtosis coefficient (K_app), and non-Gaussian diffusion coefficient (D_app) were calculated according to the histological subtype, grade, and prognostic factors (Ki-67, estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor-2 (HER-2), lymph node metastasis, and maximum lesion diameter). The differences, efficacy, and correlation between the parameters were determined.

RESULTS

The K_app value was higher and the D_app and MTRasym (3.5 ppm) values were lower in the malignant group than in the benign group (all p < 0.001; AUC (K_app) = 0.913, AUC (D_app) = 0.910, and AUC (MTRasym (3.5 ppm)) = 0.796). The differences in the AUC between K_app and MTRasym (3.5 ppm) and between D_app and MTRasym (3.5 ppm) were significant (p = 0.023, 0.046). K_app was moderately correlated with the pathological grade (|r| = 0.724) and mildly correlated with Ki-67 and HER-2 expression (|r| = 0.454, 0.333). D_app was moderately correlated with the pathological grade (|r| = 0.648) and mildly correlated with Ki-67 expression (|r| = 0.400). MTRasym (3.5 ppm) was only mildly correlated with the pathological grade (|r| = 0.468).

CONCLUSION

DKI is superior to APTWI in differentiating between benign and malignant breast lesions. Each parameter is correlated with some prognostic factors to a certain extent.

KEY POINTS

• DKI and APTWI provide valuable information regarding lesion characterization. • K_app, D_app, and MTRasym (3.5 ppm) are valid parameters for the characterization of tissue microstructure. • DKI is superior to APTWI in the study of breast cancer.

Magnetic resonance imaging features for predicting axillary lymph node metastasis in patients with breast cancer

PURPOSE

The purpose of this study was to assess the clinical value of conventional magnetic resonance imaging (MRI) and intravoxel incoherent motion (IVIM) features for predicting the risk of axillary lymph node (ALN) metastasis in patients with breast cancer.

METHODS

This retrospective study involved 265 patients with breast cancer who underwent 3.0 T breast magnetic resonance imaging examinations prior to surgery and other treatment. Of these, 119 underwent IVIM examination. The features of MRI and IVIM and postoperative pathologic results were collected. The association of MRI features of breast cancer with ALN metastasis were determined by univariate and multivariate analyses. Comparison of IVIM parameters between breast cancer patients with and without ALN metastasis was performed using the Mann-Whitney U test.

RESULTS

Among the 265 patients, 144 (54.3%) had ALN metastasis, and 121 (45.7%) did not. The size and shape of the tumours, T2WI signal, inhomogeneous enhancement, washout intensity-time curves and the values of slow ADC, fast ADC and fraction of fast ADC parameters were significantly associated with ALN metastasis. The AUC of conventional MRI for diagnosing axillary lymph node metastasis was 0.722. The AUC of MRI combined with slow ADC, fast ADC and fraction of fast ADC parameters that were used to diagnose breast cancer with ALN metastasis were 0.814, 0.803 and 0.900, respectively.

CONCLUSIONS

The features of IVIM parameters and conventional MRI can be used to predict the ALN metastasis in patients with breast cancer. MRI combined with fraction of fast ADC showed higher diagnostic efficiency for ALN metastasis in breast cancer than MRI did.

Radiomics Based on Multimodal MRI for the Differential Diagnosis of Benign and Malignant Breast Lesions

BACKGROUND

MRI-based radiomics has been used to diagnose breast lesions; however, little research combining quantitative pharmacokinetic parameters of dynamic contrast-enhanced MRI (DCE-MRI) and diffusion kurtosis imaging (DKI) exists.

PURPOSE

To develop and validate a multimodal MRI-based radiomics model for the differential diagnosis of benign and malignant breast lesions and analyze the discriminative abilities of different MR sequences.

STUDY TYPE

Retrospective.

POPULATION

In all, 207 female patients with 207 histopathology-confirmed breast lesions (95 benign and 112 malignant) were included in the study. Then 159 patients were assigned to the training group, and 48 patients comprised the validation group.

FIELD STRENGTH/SEQUENCE

T₂ -weighted (T₂ W), T₁ -weighted (T₁ W), diffusion-weighted MR imaging (b-values = 0, 500, 800, and 2000 seconds/mm² ) and quantitative DCE-MRI were performed on a 3.0T MR scanner.

ASSESSMENT

Radiomics features were extracted from T₂ WI, T₁ WI, DKI, apparent diffusion coefficient (ADC) maps, and DCE pharmacokinetic parameter maps in the training set. Models based on each sequence or combinations of sequences were built using a support vector machine (SVM) classifier and used to differentiate benign and malignant breast lesions in the validation set.

STATISTICAL TESTS

Optimal feature selection was performed by Spearman's rank correlation coefficients and the least absolute shrinkage and selection operator algorithm (LASSO). Receiver operating characteristic (ROC) curves were used to assess the diagnostic performance of the radiomics models in the validation set.

RESULTS

The area under the ROC curve (AUC) of the optimal radiomics model, including T₂ WI, DKI, and quantitative DCE-MRI parameter maps was 0.921, with an accuracy of 0.833. The AUCs of the models based on T₁ WI, T₂ WI, ADC map, DKI, and DCE pharmacokinetic parameter maps were 0.730, 0.791, 0.770, 0.788, and 0.836, respectively.

DATA CONCLUSION

The model based on radiomics features from T₂ WI, DKI, and quantitative DCE pharmacokinetic parameter maps has a high discriminatory ability for benign and malignant breast lesions.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:596-607.