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Apparent diffusion coefficient histogram analysis for differentiating solid ovarian tumors.

Renwei Liu1, Ruifeng Li1, Jinzhi Fang1

  • 1Department of Radiology, Affiliated Longhua People's Hospital Southern Medical University (Longhua People's Hospital), Shenzhen, China.

Frontiers in Oncology
|August 18, 2022
PubMed
Summary
This summary is machine-generated.

Apparent diffusion coefficient (ADC) histogram analysis effectively differentiates ovarian granulosa cell tumors (GCTs), fibromas, and high-grade serous ovarian carcinomas (HGSOCs). ADCmean values provide quantitative insights for pre-surgical diagnosis of these solid ovarian tumors.

Keywords:
Histogram analysisapparent diffusion coefficient (ADC)diffusion weighted image (DWI)magnetic resonance imaging (MRI)solid ovarian tumors

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Area of Science:

  • Radiology
  • Oncology
  • Medical Imaging

Background:

  • Solid ovarian tumors require accurate pre-surgical differentiation for optimal patient management.
  • Granulosa cell tumors (GCTs), ovarian fibromas, and high-grade serous ovarian carcinomas (HGSOCs) are distinct entities with differing prognoses and treatment strategies.
  • Distinguishing these tumor types based solely on conventional imaging can be challenging.

Purpose of the Study:

  • To assess the diagnostic utility of apparent diffusion coefficient (ADC) histogram analysis in differentiating between GCTs, ovarian fibromas, and HGSOCs.
  • To determine if quantitative ADC parameters can reliably distinguish these three types of solid ovarian tumors.
  • To evaluate the potential of ADC histogram analysis as a non-invasive tool for pre-operative diagnosis.

Main Methods:

  • Retrospective review of diffusion-weighted imaging data from 11 patients with GCTs, 61 with ovarian fibromas, and 14 with HGSOCs.
  • Calculation of histogram parameters (ADCmean, ADCmax, ADCmin) from ADC maps for defined regions of interest.
  • Statistical comparison of ADC parameters using Kruskal-Wallis H and Mann-Whitney U tests, and assessment of diagnostic performance via ROC curve analysis.

Main Results:

  • Significant differences in ADCmean, ADCmax, and ADCmin values were observed among GCTs, ovarian fibromas, and HGSOCs.
  • Specific ADCmean cutoff values were identified for differentiating between each pair of tumor types (e.g., GCT vs. fibroma: 0.95×10-3 mm2/s).
  • ADC histogram analysis demonstrated good diagnostic performance in distinguishing between the three solid ovarian tumor types.

Conclusions:

  • ADC histogram analysis, particularly ADCmean, provides valuable quantitative information for differentiating GCTs, ovarian fibromas, and HGSOCs.
  • This technique enables accurate pre-operative distinction of these ovarian tumors, aiding in treatment planning.
  • ADC histogram analysis represents a promising non-invasive method for improving the diagnostic accuracy of solid ovarian tumors.