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article · Zagazig University Medical Journal

Postoperative Enhancement on Breast Magnetic Resonance Imaging: How to Interpret in Pre-Treatment Mapping

2024Open accessZagazig University

Abstract

Background: Magnetic resonance imaging (MRI) is effective in evaluating the breast postoperatively since operation and radiotherapy subjected the treated breast to a variety of alterations. This can confound the analysis of ultrasonography and mammographic imaging, particularly for suspected local recurrence. This study aimed to estimate the diagnostic accuracy of DCE-MRI in differentiating post-operative changes from recurrent breast cancer (BC) cases after various operative techniques to improve the mapping criteria of breast treatmentPatients and Methods: We enrolled 18 cases with history of BC and recieved conservative surgery for treatment in this prospective study. Radiological investigations included Sonomammographic examination and breast Dynamic contrast enhanced MRI (DCE-MRI). Diffusion-weighted MRI were collected, and ADC maps were produced. The tumors were categorized using the Breast Imaging Reporting and Data System (BI-RADS) method. The findings of histological investigations were regarded as our reference.Results: On measuring ADC values, ADC value was remarkably reduced in recurrent breast cancer (0.92 x10-3 mm2/s) compared to postoperative changes (1.85 x10-3 mm2/s). Moreover, ADC at cutoff level of 1.07 ×10-3 mm2/s was remarkable in detecting recurrent BC with sensitivity of 92% and specificity of 80%. DCE-MRI achieved specificity, sensitivity, PPV, NPV, and accuracy of 100, 90, 100, 88.9, and 94.4%, respectively for differentiating postoperative benign changes from recurrence breast cancer.Conclusion: DCE-MRI may serve as a beneficial tool in the assessment of postoperative breast since it has excellent sensitivity and specificity in distinguishing between benign postoperative alterations and recurrent malignant tumors

Research topics

  • Advanced MRI Techniques and Applications
  • MRI in cancer diagnosis
  • Radiomics and Machine Learning in Medical Imaging

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DOI: 10.21608/zumj.2024.273899.3219

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