Using diffusion-weighted imaging and blood inflammatory markers to preoperatively differentiate between...

Area of Science:

• Radiology and Imaging
• Oncology
• Hematology

Background:

• Leiomyosarcoma (LMS) and atypical/degenerate leiomyoma (LM) are uterine tumors that require accurate differentiation for appropriate patient management.
• Distinguishing between LMS and LM preoperatively can be challenging, necessitating the exploration of advanced diagnostic biomarkers.

Purpose of the Study:

• To compare apparent diffusion coefficient (ADC) values between LMS and LM using diffusion-weighted magnetic resonance imaging (DW-MRI).
• To evaluate the diagnostic utility of ADC, specifically whole volume of interest (VOI) and focal region of interest (ROI) measurements, in differentiating LMS from LM.
• To investigate the potential of preoperative neutrophil lymphocyte ratio (NLR) as a hematological marker to aid in this differentiation.

Main Methods:

• A retrospective analysis of patients with histologically proven LMS and LM between 2013-2023.
• Calculation of NLR from pre-operative full blood counts for all included patients (191 LM, 18 LMS).
• Manual segmentation of VOI and focal ROI on DW-MRI sequences for a subset of patients (52 LM, 12 LMS) to derive mean ADC values. Statistical analysis included Mann-Whitney and Fisher's exact tests, with ROC curves for diagnostic performance.

Main Results:

• Mean ADC values, both for VOI and ROI, were significantly lower in LMS compared to LM.
• Focal ROI mean ADC demonstrated higher diagnostic accuracy (AUC 0.817) than VOI mean ADC (AUC 0.755).
• A threshold ROI mean ADC of ≤1.00 x10⁻³ mm²/sec yielded 88.3% sensitivity and 65.4% specificity for LMS. Higher NLR was observed in LMS (median 2.8) versus LM (median 1.7).

Conclusions:

• ADC, particularly when measured in focal ROIs, is a valuable biomarker for differentiating LMS from LM.
• Observed differences in preoperative NLR suggest a potential link between inflammation and malignancy in these uterine conditions.
• Future research should explore integrated risk stratification models incorporating both ADC metrics and hematological parameters for improved diagnostic accuracy.