Quantitative Time-Dependent Diffusion MRI for Diagnosis and Aggressiveness Assessment of Endometrial Cancer: A Prospective Study

Abstract

Objective: Preoperative differentiation of benign and malignant endometrial lesions, along with the identification of aggressive histological types of endometrial cancer (EC), is crucial for guiding treatment strategies. Time-dependent diffusion magnetic resonance imaging (TDD-MRI), which allows the characterization of tissue microstructure at the cellular level, is not currently applied for endometrial lesions. This study aimed to evaluate TDD-MRI-derived microstructural parameters for noninvasively distinguishing benign and malignant endometrial lesions and predicting aggressive histological types of EC.

Materials and methods: This prospective study enrolled 177 patients with clinically suspected EC who underwent TDD-MRI between January 2024 and March 2025. The Imaging Microstructural Parameters Using Limited Spectrally Edited Diffusion method was used to extract microstructural parameters, including the cell diameter (d), intracellular volume fraction (v_in), cellularity (number of cells per unit area), cellularity index (v_in/d), and extracellular diffusivity (D_ex), along with three apparent diffusion coefficient measurements. The area under the receiver operating characteristic curve (AUC) was used to assess diagnostic performance. The Pearson correlation coefficient between the microstructural parameters and histopathological measurements was calculated.

Results: A total of 130 women (mean ± standard deviation age: 56 ± 14 years) administered uterine curettage or surgery were included in the final analysis. All microstructural parameters showed significant differences between benign endometrial lesions and EC (P < 0.05), as well as between nonaggressive and aggressive EC (P < 0.05). Cellularity exhibited the highest AUC of 0.86 for distinguishing benign endometrial lesions from EC, whereas the cellularity index showed the highest AUC of 0.88 for distinguishing aggressive histological types. D_0Hz was positively correlated with D_ex (P < 0.05) and negatively correlated with diameter (P < 0.05), cellularity index (P < 0.01) and v_in (P < 0.001) in patients with benign endometrial lesions. D_0Hz was positively correlated with D_ex (P < 0.001) and negatively correlated with v_in (P < 0.001) in patients with EC. Microstructural parameters strongly correlated with corresponding pathological features (r = 0.77-0.83; P < 0.001).

Conclusion: TDD-MRI-derived microstructural parameters demonstrated high performance in differentiating benign from malignant endometrial diseases and identifying aggressive types of EC.

Keywords: Endometrial cancer; Histological types; Magnetic resonance imaging; Microstructural parameters; Time-dependent diffusion MRI.

Fig. 1. Flowchart shows participant enrollment. EC = endometrial cancer

Fig. 2. Schematic shows the pulse sequences and differentiation of benign endometrial lesions and endometrial cancer using time-dependent diffusion magnetic resonance imaging-based microstructural mapping. A: The diagram presented illustrates the pulse sequences utilized for imaging microstructural parameters using a limited spectrally edited diffusion method. In addition to conventional PGSE ACQ, OGSE ACQ at two frequencies (n = 1 and 2) were employed. Diffusion signals, which are dependent on diffusion time, can be captured using both pulsed and OGSE diffusion encoding schemes across various diffusion times. B: The diffusivity of water molecules in a cellular environment is influenced by diffusion time, with this effect becoming more noticeable as cellular density increases. By employing pulsed and OGSE diffusion encoding schemes at varying diffusion times, diffusion signals can be captured. These signals enable the reconstruction of microstructural properties using biophysical modeling approaches. PGSE = pulsed gradient spin-echo, ACQ = acquisitions, OGSE = oscillating gradient spin-echo, td = diffusion time, NK = natural killer

Fig. 3. Bland–Altman plots compare the reproducibility of time-dependent diffusion magnetic resonance imaging measurements from two independent readers. A: Plot shows that, for diameter, the mean difference is -0.08 µm (95% CI: -0.91, 0.75). B: Plot shows that, for D_ex, the mean difference is 0.02 µm²/ms (95% CI: -0.13, 0.18). C: Plot shows that, for cellularity, the mean difference is 0.04 (95% CI: -0.36, 0.44). D: Plot shows that, for the cellularity index, the mean difference is -0.00 (95% CI: -0.35, 0.34). E: Plot shows that, for v_in, the mean difference is 0 µm²/ms (95% CI: -0.04, 0.04). F: Plot shows that, for D_0Hz, the mean difference is 0.01 µm²/ms (95% CI: -0.23, 0.25). G: Plot shows that, for D_17Hz, the mean difference is 0.03 µm²/ms (95% CI: -0.23, 0.29). H: Plot shows that, for D_33Hz, the mean difference is -0.02 µm²/ms (95% CI: -0.27, 0.23). The dotted line represents the mean difference, and the upper and lower solid lines represent the upper and lower limits of agreement, respectively. CI = confidence interval, D_ex = extracellular diffusivity, v_in = intracellular volume fraction, SD = standard deviation

Fig. 4. Microstructural parameter maps of benign lesions and nonaggressive and aggressive endometrial cancer, including diameter, D_ex, v_in, cellularity, cellularity index and the diffusivity maps from pulsed gradient spin-echo (D_0Hz) and oscillating gradient spin-echo (D_17Hz and D_33Hz) data. Corresponding T2W images at the similar axial locations are shown in the first column. D_ex = extracellular diffusivity, v_in, = intracellular volume fraction, T2W = T2-weighted, IMPULSED = Imaging Microstructural Parameters Using Limited Spectrally Edited Diffusion, ADC = apparent diffusion coefficient

Fig. 5. Box and whisker plots show comparisons of microstructural parameters, including (A) diameter, (B) D_ex, (C) cellularity, (D) cellularity index, (E) v_in, (F) D_0Hz, (G) at D_17Hz, and (H) at D_33Hz among benign lesions and EC, (I) diameter, (J) D_ex, (K) cellularity, (L) cellularity index, (M) v_in, (N) at D_0Hz, (O) at D_17Hz, and (P) at D_33Hz among nonaggressive EC and aggressive EC. ^*P < 0.05, ^†P < 0.01, ^‡P < 0.001. Dots represent individual data points, boxes indicate the standard deviation, and midlines are the median. D_ex = extracellular diffusivity, v_in = intracellular volume fraction, EC = endometrial cancer

Fig. 6. Correlations between the fitted microstructural parameters and D_0Hz at the participant level (A-E) among benign lesions and EC and (F-J) among nonaggressive and aggressive EC. ^*P < 0.05, ^†P < 0.01, ^‡P < 0.001. EC = endometrial cancer

Fig. 7. Correlations between time-dependent diffusion MRI-derived microstructural parameters and pathology-based microstructural features (n = 16). A: Hematoxylin-eosin-stained image (×40) shows pathological specimen from one participant with benign lesion. B: Hematoxylin-eosin-stained image (×40) illustrates nuclei segmented by a pretrained conditional generative adversarial network. C: Automated quantification of the pathological microstructural features. D-F: The graph depicts the correlations between time-dependent diffusion MRI-derived (D) diameter, (E) cellularity, (F) v_in, and the pathology-based microstructural features. f_nuclei = nuclei fraction, v_in = intracellular volume fraction