Meta-Analysis of Abbreviated MRI Scanning Reveals a High Specificity and Sensitivity in Detecting Breast Cancer

Background: Breast cancer (BC) is the most common malignancy and the leading cause of mortality among women. Various diagnostic tools for early diagnosis and tumor progression detection have helped patients receive precise and timely treatment. This meta-analysis aims to evaluate the accuracy of abbreviated magnetic resonance imaging (ABB-MRI) data in the diagnosis of BC. Methods: The Preferred Reporting Items for Systematic Reviews and Comparative Meta-Analysis for Diagnostic Test Accuracy (PRISMA-DTA) guidelines were followed to perform the meta-analysis. Data from articles published in the last 5 years reporting ABB-MRI as a diagnostic tool were collected by searching the relevant databases using multiple subject keywords and free words. Meta-Disc 1.4 software was used to analyze the data and plot the relevant graphs. Results: We identified 1250 articles while searching the relevant electronic databases. After removing duplicate studies and data, the titles and abstracts of 1149 studies were reviewed. A total of 18 publications that satisfied the inclusion criteria were finally included in the meta-analysis. The calculated pooled specificity and sensitivity were 0.90 and 0.87, respectively. This study confirms the ABB-MRI protocol's high diagnostic sensitivity and specificity in malignant breast cancer detection. Conclusions: Abbreviated MRI seems to function as a useful supplement because it is highly sensitive and specific, on par with the full MRI scan, as shown in multiple independent studies as well as this pooled analysis. These findings confirm the utility of ABB-MRI in the accurate detection of malignant BC lesions in high-risk women that cannot be detected by mammography or ultrasound.

Automated summary

This meta-analysis evaluates the accuracy of abbreviated magnetic resonance imaging (ABB-MRI) in the diagnosis of breast cancer. The results show that ABB-MRI has high sensitivity and specificity in detecting breast cancer, making it a useful tool for the accurate detection of malignant lesions in high-risk women that cannot be detected by mammography or ultrasound.