Role of diffusion weighted and perfusion weighted imaging in characterization of ovarian tumours | ||
| The Egyptian Journal of Hospital Medicine | ||
| Article 24, Volume 73, Issue 2, October 2018, Pages 6128-6135 PDF (465.12 K) | ||
| Document Type: Original Article | ||
| Authors | ||
| Randa Hussein Abdallah; Nermeen Nasry Halim; Eman Sameh Hussein* ; Eman Ahmed Fouad | ||
| Radiodiagnosis Department, Ain Shams University, Cairo, Egypt | ||
| Abstract | ||
| Background: ovarian cancer is the second most common gynecological cancer and the fifth most common cancer in women. Proper management depends on proper preoperative assessment with the help of clinical examination, laboratory tests and different imaging modalities. Radiological evaluation includes ultrasonography (US), computed tomography (CT) and recently magnetic resonance imaging (MRI). Diffusion weighted imaging (DWI) has been established as a useful functional imaging tool in neurologic applications for a number of years, but recent technical advances now allow its use in abdominal and pelvic applications. Purpose: it was to evaluate the role of diffusion weighted and perfusion weighted MRI imaging in the characterization of ovarian tumours and differentiation between benign and malignant tumours. Patients and Methods: this study was performed on 24 patients. All patients had US finding of solid or complex adnexal lesions. Results: DWI & dynamic MRI are significant promising tool factors for characterization of ovarian tumours and differentiation between benign & malignant lesions with high sensitivity, specificity. The sensitivity, specificity and accuracy of detection of the nature of the lesions. Have been increases after adding of DWI & DCE-MRI to the conventional imaging. Conclusion: Adding of DWI & DCE-MRI to the conventional MRI improves the sensitivity and specificity of diagnosis and allows confident diagnosis and differentiation between benign and malignant lesions. | ||
| Keywords | ||
| Ovarian tumours; MRI; DWI; ADC; DCE; TTP; MRE%; SI max | ||
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