Whole-lesion apparent diffusion coefficient (ADC) histogram as a quantitative biomarker to preoperatively differentiate stage IA endometrial carcinoma from benign endometrial lesions

• DOI: 10.1186/s12880-022-00864-9
• Publication Date: 2022-08-08T04:02:28Z
• Authors: Jieying Zhang; Xiaoduo Yu; Xiaomiao Zhang; Shuang Chen; Yan Song; Lizhi Xie; Yan Chen; Han Ouyang
• Keywords: Research; Diffusion-weighted MRI; Histogram analysis; Endometrial Neoplasms; 3. Good health; 03 medical and health sciences; Diffusion Magnetic Resonance Imaging; 0302 clinical medicine; Apparent diffusion coefficient; ROC Curve; Medical technology; Humans; Female; R855-855.5; Endometrial neoplasms; Biomarkers; Retrospective Studies

Abstract Background To assess the value of whole-lesion apparent diffusion coefficient (ADC) histogram analysis in differentiating stage IA endometrial carcinoma (EC) from benign endometrial lesions (BELs) and characterizing histopathologic features of stage IA EC preoperatively.

Methods One hundred and six BEL and 126 stage IA EC patients were retrospectively enrolled. Eighteen volumetric histogram parameters were extracted from the ADC map of each lesion. The Mann–Whitney U or Student’s t-test was used to compare the differences between the two groups. Models based on clinical parameters and histogram features were established using multivariate logistic regression. Receiver operating characteristic (ROC) analysis and calibration curves were used to assess the models.

Results Stage IA EC showed lower ADC10th, ADC90th, ADCmin, ADCmax, ADCmean, ADCmedian, interquartile range, mean absolute deviation, robust mean absolute deviation (rMAD), root mean squared, energy, total energy, entropy, variance, and higher skewness, kurtosis and uniformity than BELs (all p < 0.05). ADCmedian yielded the highest area under the ROC curve (AUC) of 0.928 (95% confidence interval [CI] 0.895–0.960; cut-off value = 1.161 × 10−3 mm2/s) for differentiating stage IA EC from BELs. Moreover, multivariate analysis demonstrated that ADC-score (ADC10th + skewness + rMAD + total energy) was the only significant independent predictor (OR = 2.641, 95% CI 2.045–3.411; p < 0.001) for stage IA EC when considering clinical parameters. This ADC histogram model (ADC-score) achieved an AUC of 0.941 and a bias-corrected AUC of 0.937 after bootstrap resampling. The model performed well for both premenopausal (accuracy = 0.871) and postmenopausal (accuracy = 0.905) patients. Besides, ADCmin and ADC10th were significantly lower in Grade 3 than in Grade 1/2 stage IA EC (p = 0.022 and 0.047). At the same time, no correlation was found between ADC histogram parameters and the expression of Ki-67 in stage IA EC (all p > 0.05).

Conclusions Whole-lesion ADC histogram analysis could serve as an imaging biomarker for differentiating stage IA EC from BELs and assisting in tumor grading of stage IA EC, thus facilitating personalized clinical management for premenopausal and postmenopausal patients.