Quantitative Apparent Diffusion Coefficient (ADC) Mapping as a Preoperative Biomarker for Tumor Grade and Myometrial Invasion in Endometrial Carcinoma

Vollständiger Abstract

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Abstract Background: The decision to perform systematic lymphadenectomy in endometrial carcinoma (EC) depends heavily on preoperative staging. We investigated the utility of diffusion-weighted imaging (DWI) and quantitative apparent diffusion coefficient (ADC) mapping to noninvasively predict the presence of EC and its histological tumor grade. Materials and Methods: This prospective case–control study included 30 women with histopathologically confirmed EC (cases) and 30 women with normal endometrium (controls). Participants underwent 3.0T pelvic magnetic resonance imaging, including DWI ( b -values: 0, 500, and 1000 s/mm 2 ). Mean ADC values were calculated using manually delineated regions of interest and correlated with final surgical histopathology. Results: The mean ADC value was significantly lower in the EC group (0.89 ± 0.29 × 10 −3 mm 2 /s) compared to the control group (1.70 ± 0.37 × 10 −3 mm 2 /s) ( P < 0.001). Receiver operating characteristic curve analysis identified an optimal upper-threshold ADC cutoff of 1.16 × 10 −3 mm 2 /s, yielding 100% sensitivity, 86.7% specificity, and 94.6% accuracy for malignancy. Crucially, mean ADC values demonstrated a statistically significant inverse correlation with histological tumor grade, decreasing sequentially from Grade 1 (1.04 ± 0.15 × 10 −3 mm 2 /s) to Grade 2 (0.84 ± 0.37 × 10 −3 mm 2 /s) and Grade 3 (0.82 ± 0.29 × 10 −3 mm 2 /s) ( P = 0.015). Conclusion: Quantitative ADC mapping is a highly accurate, noninvasive biomarker for EC. The established cutoff reliably distinguishes malignancy, while the inverse correlation between ADC values and tumor grade provides critical preoperative intelligence to guide the extent of surgical staging.