Diagnostic Value of ADC Value and Choline Peak MR Spectroscopy in Patient with Uterine Tumors
DOI:
https://doi.org/10.31557/APJCB.2026.11.3.757Keywords:
uterine tumor, magnetic resonance imaging, apparent diffusion coefficient, Magnetic resonance spectroscopyAbstract
Introduction: Accurate differentiation between benign and malignant uterine tumors is critical for determining therapeutic strategies. This study aims to evaluate the diagnostic performance of Apparent Diffusion Coefficient (ADC) values and Magnetic Resonance Spectroscopy (MRS)-derived choline (Cho) peaks in characterizing uterine tumors, using histopathology as the reference standard.
Methods: A diagnostic accuracy study was conducted between February and August 2025, involving 35 patients with uterine tumors. All subjects underwent pelvic MRI, including diffusion-weighted imaging (DWI) and multivoxel MRS. ADC values and Cho peaks were measured from the solid portions of the lesions. Diagnostic performance, including sensitivity, specificity, and optimal cutoff values, was determined using Receiver Operating Characteristic (ROC) curve analysis.
Results: Malignant tumors exhibited significantly lower mean ADC values (0.75×10−3 mm2/s) compared to benign lesions (1.35×10−3 mm2/s; p=0.001). Conversely, mean Cho peaks were significantly higher in the malignant group (3.33 ppm) than in the benign group (1.31 ppm; p=0.001). An ADC cut-off of 0.965×103 mm2/s yielded a sensitivity of 93.3% and a specificity of 100% (AUC=0.960). A Cho peak cut-off of 2.215 ppm demonstrated 100% sensitivity, 96.7% specificity, and 97.1% accuracy (AUC=0.967). Demographic factors such as age, parity, and contraceptive use showed no significant association with histopathological outcomes (p>0.05).
Conclusion: Both ADC quantification and Cho peak MRS serve as highly accurate, non-invasive biomarkers for differentiating uterine malignancies. The integration of these functional MRI parameters significantly enhances diagnostic precision in the preoperative assessment of uterine tumors.
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Copyright (c) 2026 Asian Pacific Journal of Cancer Biology

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