Role of diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) in differentiating between benign and malignant breast lesions

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 Brief Review on Breast Carcinoma and Deliberation on Current Non Invasive Imaging Techniques for Detection

BACKGROUND

Breast carcinoma is a life threatening disease that accounts for 25.1% of all carcinoma among women worldwide. Early detection of the disease enhances the chance for survival.

DISCUSSION

This paper presents comprehensive report on breast carcinoma disease and its modalities available for detection and diagnosis, as it delves into the screening and detection modalities with special focus placed on the non-invasive techniques and its recent advancement work done, as well as a proposal on a novel method for the application of early breast carcinoma detection.

CONCLUSION

This paper aims to serve as a foundation guidance for the reader to attain bird's eye understanding on breast carcinoma disease and its current non-invasive modalities.

Diffusion weighted imaging in breast cancer - Can it be a noninvasive predictor of nuclear grade?

BACKGROUND

DWI and ADC values are noninvasive MRI techniques, which provide quantitative information about tumor heterogeneity.

AIM

To determine the minimum and mean ADC values in breast carcinoma and to correlate ADC values with various prognostic factors.

SETTINGS AND DESIGN

Prospective observational study.

MATERIALS AND METHODS

Fifty-five patients with biopsy-proven breast carcinoma were included in this study. MRI with DWI was performed with Siemens 3T Skyra scanner. ADC values were measured by placing regions of interest (ROIs) within the targeted lesions on ADC maps manually. The histopathological and immunohistochemical analysis of surgical specimen was done to determine the prognostic factors.

STATISTICAL ANALYSIS

Students T test and ANOVA were used to study the difference in ADC between two groups. Pearson correlation coefficient was used to quantify the correlation between ADC values and prognostic factors.

RESULTS

Lower grade (grade I) breast carcinoma had a significantly high ADC value as compared to higher grade carcinoma (grade II and III). For differentiating Grade I tumors from grade II and III, a minimum ADC cut-off value was 0.79 × 10-3 mm2/sec (83% sensitivity and 84% specificity) and a mean ADC cut-off value was 0.82 × 10-3 mm2/sec (83% sensitivity and 71% specificity) was derived. There was no significant correlation between ADC and other prognostic factors.

CONCLUSION

ADC values can be used to differentiate lower grade breast carcinoma (grade I) from higher grades (grade II and III). Minimum ADC values are more accurate in predicting the grade of the breast tumor than mean ADC value.

Optimal Experiment Design for Monoexponential Model Fitting: Application to Apparent Diffusion Coefficient Imaging

The monoexponential model is widely used in quantitative biomedical imaging. Notable applications include apparent diffusion coefficient (ADC) imaging and pharmacokinetics. The application of ADC imaging to the detection of malignant tissue has in turn prompted several studies concerning optimal experiment design for monoexponential model fitting. In this paper, we propose a new experiment design method that is based on minimizing the determinant of the covariance matrix of the estimated parameters (D-optimal design). In contrast to previous methods, D-optimal design is independent of the imaged quantities. Applying this method to ADC imaging, we demonstrate its steady performance for the whole range of input variables (imaged parameters, number of measurements, and range of b-values). Using Monte Carlo simulations we show that the D-optimal design outperforms existing experiment design methods in terms of accuracy and precision of the estimated parameters.