Differential Diagnosis of Benign and Malignant Breast Tumors Using Apparent Diffusion Coefficient Value Measured Through Diffusion-Weighted Magnetic Resonance Imaging:

Preoperative Dynamic Contrast-Enhanced and Diffusion-Weighted Breast Magnetic Resonance Imaging Findings for Prediction of Lymphovascular Invasion of the Lesions in Node-Negative Invasive Breast Cancer

Purpose: Our single-center retrospective study aimed to investigate the relationship between preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) findings and apparent diffusion coefficient (ADC) values and lymphovascular invasion (LVI) status of the lesions in patients with clinically-radiologically lymph node-negative invasive breast cancer. Methods: A total of 250 breast lesions diagnosed in preoperative magnetic resonance imaging were identified. All patients were divided into 2 subgroups: LVI-negative and LVI-positive according to the pathological findings of surgical specimens. The 2 groups' DCE-MRI findings, ADC values, and histopathological results of lesions were compared. Results: LVI was detected in 100 of 250 lesions. Younger age than 45 years and larger lesion size than 20 mm were found to be associated with the presence of LVI (P < .001). High histological and nuclear grade (P = .001), HER2-enriched molecular subtype (P = .001), and Ki-67 positivity (P = .016) were significantly associated with LVI. The LVI positivity rate was significantly higher in the lesions with medium-rapid initial phase kinetic curve and washout delayed phase kinetic curve (P = .001). The presence of LVI was significantly associated with the presence of peritumoral edema, sentinel lymph node metastasis, adjacent vessel sign, and increased whole breast vascularity (P < .001). When diffusion-weighted imaging findings were evaluated, it was determined that tumoral ADC values lower than 1068 × 10-6 mm2/second (P = .002) and peritumoral-tumoral ADC ratios higher than 1.5 (P = .001) statistically increased the probability of LVI. Conclusion: The patient's age, various histopathological and DCE-MRI findings, tumoral ADC value, and peritumoral-tumoral ADC ratio may be useful in the preoperative prediction of LVI status in breast cancer lesions.

Clinical utility of apparent diffusion coefficient and diffusion-weighted magnetic resonance imaging for resectability assessment of head and neck tumors with skull base invasion

BACKGROUND

The usefulness of apparent diffusion coefficient (ADC) and diffusion-weighted magnetic resonance imaging (DWI) in the detection of malignant tumors has been reported. The purpose of this study is to clarify the role of ADC and DWI for diagnosis of skull base tumors.

METHODS

A total of 27 patients with head and neck tumors with skull base invasions undergoing skull base surgery were enrolled in this study. Pathological findings of dural invasion and bone invasion were compared with the diagnostic imaging.

RESULTS

Advanced magnetic resonance imaging techniques revealed that ADC values in regions of pathological bone and dural invasions were significantly lower than in regions of no invasion. The area under the curve of ADC in bone invasions and dural invasions were 0.957 and 0.894, respectively.

CONCLUSIONS

Our findings indicate that ADC and DWI are useful tools for the diagnosis of head and neck tumors with skull base invasion.

The Role of Combined Diffusion-Weighted Imaging and Dynamic Contrast-Enhanced MRI for Differentiating Malignant From Benign Breast Lesions Presenting Washout Curve

PURPOSE

The aim of this study is to evaluate the diagnostic performance of combined breast magnetic resonance imaging (MRI) protocol including dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted imaging (DWI) in patients with enhancing lesions that demonstrated washout curve and to determine whether applying apparent diffusion coefficient (ADC) cutoff value could improve the diagnostic value of breast MRI.

METHODS

The retrospective study included 116 patients with 116 suspicious breast lesions, which showed washout curve on DCE-MRI, who underwent subsequent biopsy. Morphologic characteristics on DCE-MRI and ADC values on DWI were evaluated. Apparent diffusion coefficient values and morphologic features of benign and malignant lesions were compared. Diagnostic values of DCE-MRI and combined MRI, including DCE-MRI and DWI (applying an ADC cutoff value) for distinguishing malignancy from benign lesions, were calculated.

RESULTS

Of the 116 breast lesions, 79 were malignant and 37 were benign. The ADC value of malignant tumors (median ADC, 0.72 × 10^-3 mm²/s) was significantly lower than that of benign lesions (median ADC, 1.03 × 10^-3 mm²/s; P < .000). The sensitivity and specificity of an ADC cutoff value of 0.89 × 10^-3 mm²/s were 92% and 95%, respectively. Dynamic contrast-enhanced MRI alone presented 100% sensitivity and 59.4% specificity. Adding an ADC cutoff value of 0.89 × 10^-3 mm²/s provided 100% sensitivity and 81% specificity, which would have prevented biopsy for 21.6% of benign lesions without missing any malignancies.

CONCLUSION

Applying an ADC cutoff value to DCE-MRI provides an improvement in the diagnostic value of breast MRI for differentiating among lesions presenting washout curve.

Can apparent diffusion coefficient (ADC) distinguish breast cancer from benign breast findings? A meta-analysis based on 13 847 lesions

BACKGROUND

The purpose of the present meta-analysis was to provide evident data about use of Apparent Diffusion Coefficient (ADC) values for distinguishing malignant and benign breast lesions.

METHODS

MEDLINE library and SCOPUS database were screened for associations between ADC and malignancy/benignancy of breast lesions up to December 2018. Overall, 123 items were identified. The following data were extracted from the literature: authors, year of publication, study design, number of patients/lesions, lesion type, mean value and standard deviation of ADC, measure method, b values, and Tesla strength. The methodological quality of the 123 studies was checked according to the QUADAS-2 instrument. The meta-analysis was undertaken by using RevMan 5.3 software. DerSimonian and Laird random-effects models with inverse-variance weights were used without any further correction to account for the heterogeneity between the studies. Mean ADC values including 95% confidence intervals were calculated separately for benign and malign lesions.

RESULTS

The acquired 123 studies comprised 13,847 breast lesions. Malignant lesions were diagnosed in 10,622 cases (76.7%) and benign lesions in 3225 cases (23.3%). The mean ADC value of the malignant lesions was 1.03 × 10^- 3 mm²/s and the mean value of the benign lesions was 1.5 × 10^- 3 mm²/s. The calculated ADC values of benign lesions were over the value of 1.00 × 10^- 3 mm²/s. This result was independent on Tesla strength, choice of b values, and measure methods (whole lesion measure vs estimation of ADC in a single area).

CONCLUSION

An ADC threshold of 1.00 × 10^- 3 mm²/s can be recommended for distinguishing breast cancers from benign lesions.

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

Background

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

Objectives

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

Method

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

Results

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

Conclusion

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

MR diffusion kurtosis imaging for cancer diagnosis: A meta-analysis of the diagnostic accuracy of quantitative kurtosis value and diffusion coefficient

PURPOSE

To perform a meta-analysis for assessing the accuracy of diffusion kurtosis imaging (DKI)-derived quantitative parameters (kurtosis values, K; and corrected diffusion coefficients non-Gaussian bias, D) in separating malignant cancers from benign lesions.

METHODS

Relevant studies were searched in PubMed and Cochrane Library databases and were analyzed by Meta-DiSc software.

RESULTS

Fourteen eligible studies involving 1847 lesions in 1107 patients (895 were benign and 952 were malignant) were included. Pooled analysis showed the sensitivity, specificity, positive likelihood ratio (LR), and negative LR were respectively 0.83 (95% CI, 0.79-0.85), 0.83 (95% CI, 0.80-0.86), 4.61 (95% CI, 2.98-7.14), and 0.22 (95% CI, 0.18-0.28) for K, with the overall area under curve (AUC) of 0.89. The sensitivity, specificity, positive LR, and negative LR were 0.85 (95% CI, 0.80-0.88), 0.85 (95% CI, 0.79-0.89), 6.39 (95% CI, 3.14-12.99), and 0.18 (95% CI, 0.14-0.23) for D, with the overall AUC of 0.92. The sensitivity, specificity, positive LR, and negative LR for apparent diffusion coefficient (ADC) derived from standard diffusion-weighted imaging (DWI) were 0.82 (95% CI, 0.79-0.84), 0.85 (95% CI, 0.82-0.88), 4.75 (95% CI, 3.38-6.68), and 0.24 (95% CI, 0.19-0.29), with the overall AUC of 0.89. The superiority of D to K and ADC was also confirmed by the subgroup analysis of prostate cancer.

CONCLUSION

Our findings suggest that DKI should be added to the routine imaging protocol for screening cancer, with the highest diagnostic accuracy of diffusion coefficients.

Prediction of tumor differentiation using sequential PET/CT and MRI in patients with breast cancer

OBJECTIVE

The aim of this study is to assess tumor differentiation using parameters from sequential positron emission tomography/computed tomography (PET/CT) and magnetic resonance imaging (MRI) in patients with breast cancer.

METHODS

This retrospective study included 78 patients with breast cancer. All patients underwent sequential PET/CT and MRI. For fluorodeoxyglucose (FDG)-PET image analysis, the maximum standardized uptake value (SUV_max) of FDG was assessed at both 1 and 2 h and metabolic tumor volume (MTV) and total lesion glycolysis (TLG). The kinetic analysis of dynamic contrast-enhanced MRI parameters was performed using dynamic enhancement curves. We assessed diffusion-weighted imaging (DWI)-MRI parameters regarding apparent diffusion coefficient (ADC) values. Histologic grades 1 and 2 were classified as low-grade, and grade 3 as high-grade tumor.

RESULTS

Forty-five lesions of 78 patients were classified as histologic grade 3, while 26 and 7 lesions were grade 2 and grade 1, respectively. Patients with high-grade tumors showed significantly lower ADC-mean values than patients with low-grade tumors (0.99 ± 0.19 vs.1.12 ± 0.32, p = 0.007). With respect to SUV_max1, MTV2.5, and TLG2.5, patients with high-grade tumors showed higher values than patients with low-grade tumors: SUV_max1 (7.92 ± 4.5 vs.6.19 ± 3.05, p = 0.099), MTV2.5 (7.90 ± 9.32 vs.4.38 ± 5.10, p = 0.095), and TLG2.5 (40.83 ± 59.17 vs.19.66 ± 26.08, p = 0.082). However, other parameters did not reveal significant differences between low-grade and high-grade malignancies. In receiver-operating characteristic (ROC) curve analysis, ADC-mean values showed the highest area under the curve of 0.681 (95%CI 0.566-0.782) for assessing high-grade malignancy.

CONCLUSIONS

Lower ADC-mean values may predict the poor differentiation of breast cancer among diverse PET-MRI functional parameters.

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

Objective:

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

Materials and Methods:

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

Results:

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

Conclusion:

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

Differential diagnosis of prostate cancer and noncancerous tissue in the peripheral zone and central gland using the quantitative parameters of DCE-MRI: A meta-analysis

BACKGROUND

The objective of this meta-analysis was to evaluate the clinical usefulness of K, Kep, and Ve values in the differential diagnosis of prostate cancer (PCa) and noncancerous tissue in the peripheral zone (PZ) and central gland (CG).

METHODS

A search was conducted of the PubMed, MEDLINE, EMBASE, Cochrane Library, China National Knowledge Infrastructure, and Wanfang databases from January 2000 to October 2015 using the search terms "prostate cancer," " dynamic contrast-enhanced (DCE)," "magnetic resonance imaging," "K," "Kep," and "Ve." Studies were selected and included according to strict eligibility criteria. Standardized mean differences (SMDs) and 95% confidence intervals (CIs) were used to compare K, Kep, and Ve values between PCa and noncancerous tissue.

RESULTS

Fourteen studies representing 484 patients highly suspicious for prostate adenocarcinoma were selected for the meta-analysis. We found that K values measured by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) were significantly higher in PCa tissue than in noncancerous tissue in the PZ (SMD 1.57, 95% CI 0.98-2.16; z = 5.21, P <0.00001) and CG (SMD 1.19, 95% CI 0.46-1.91; z = 3.21, P = 0.001). Kep values measured by DCE-MRI were significantly higher in PCa than in noncancerous tissue in the PZ (SMD 1.41, 95% CI 0.92-1.91; z = 5.59, P < 0.00001) and CG (SMD 1.57, 95% CI 0.69-2.46; z = 3.49, P = 0.0005). Ve values generated by DCE-MRI were slightly higher in PCa than in noncancerous tissue in the PZ (SMD 0.72, 95% CI 0.17-1.27; z = 2.58, P = 0.010), but sensitivity analysis found that the Ve value was unstable for differentiation between PCa and noncancerous PZ tissue. However, there was no significant difference in the Ve value between PCa and noncancerous CG tissue (SMD -0.29, 95% CI -1.18, 0.59; z = 0.65, P = 0.51).

CONCLUSION

Our meta-analysis shows that K and Kep were the most reliable parameters for differentiating PCa from noncancerous tissue and were critical for evaluation of the internal structure of cancer. The Ve value was not helpful for distinguishing PCa from noncancerous CG tissue; its ability to distinguish between PCa and noncancerous PZ tissue remains uncertain.

Volumetric segmentation of ADC maps and utility of standard deviation as measure of tumor heterogeneity in soft tissue tumors

PURPOSE

Determine interobserver concordance of semiautomated three-dimensional volumetric and two-dimensional manual measurements of apparent diffusion coefficient (ADC) values in soft tissue masses (STMs) and explore standard deviation (SD) as a measure of tumor ADC heterogeneity.

RESULTS

Concordance correlation coefficients for mean ADC increased with more extensive sampling. Agreement on the SD of tumor ADC values was better for large regions of interest and multislice methods. Correlation between mean and SD ADC was low, suggesting that these parameters are relatively independent.

CONCLUSION

Mean ADC of STMs can be determined by volumetric quantification with high interobserver agreement. STM heterogeneity merits further investigation as a potential imaging biomarker that complements other functional magnetic resonance imaging parameters.