Retrospective Assessment of Histogram-Based Diffusion Metrics for Differentiating Benign and Malignant Endometrial Lesions

Andrea S. Kierans, Ankur M. Doshi, Diane Dunst, Dorota Popiolek, Stephanie V. Blank, Andrew B. Rosenkrantz

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

Objective Our study aimed to retrospectively evaluate the utility of volumetric histogram-based diffusion metrics in differentiating benign from malignant endometrial abnormalities. Methods A total of 54 patients underwent pelvic magnetic resonance imaging with diffusion-weighted imaging before endometrial tissue diagnosis. Two radiologists placed volumes of interest on the apparent diffusion coefficient (ADC) map encompassing the entire endometrium and focal endometrial lesions. The mean ADC, percentile ADC values, kurtosis, skewness, and entropy of ADC were compared between benign and malignant abnormalities. Results In premenopausal patients, significant independent predictors of malignancy were whole-endometrium analysis for R1, 10th to 25th ADC percentile (P = 0.012); whole-endometrium analysis for R2, mean ADC (P = 0.001) and skewness (P = 0.004); focal lesion analysis for R1, skewness (P = 0.045); focal lesion analysis for R2, 10th to 25th ADC percentile (P ≤ 0.0001). The area under the curve for malignancy was 90.0% to 97.3% and 76.1% to 77.3% for the more and less experienced radiologists, respectively. In postmenopausal patients, the only significant difference was kurtosis using whole-endometrium analysis for R1 (P = 0.042). Conclusions Volumetric ADC histogram metrics may help radiologists assess the risk of malignancy in endometrial abnormalities on magnetic resonance imaging in premenopausal patients.

Original languageEnglish
Pages (from-to)723-729
Number of pages7
JournalJournal of Computer Assisted Tomography
Volume40
Issue number5
DOIs
StatePublished - 1 Sep 2016
Externally publishedYes

Keywords

  • endometrial abnormality
  • histogram diffusion metrics
  • magnetic resonance imaging

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