Share：
Share this content in WeChat
X
[Chinese] [PDF] 33 1
• Clinical Article •
The value of IVIM-DWI combined with clinical parameters in predicting disease-free survival of endometrial cancer
WANG Peipei  ZHANG Qi  WANG Tingting  CHEN Dong  ZHANG Zhongyuan  DONG Jiangning 

Cite this article as: WANG P P, ZHANG Q, WANG T T, et al. The value of IVIM-DWI combined with clinical parameters in predicting disease-free survival of endometrial cancer[J]. Chin J Magn Reson Imaging, 2026, 17(10): 109-116. DOI:10.12015/issn.1674-8034.2026.10.013.


[Abstract] Objective To explore the value of intra-voxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in conjunction with clinical and pathological parameters for predicting 3-year postoperative disease-free survival (DFS) in patients with endometrial carcinoma (EC).Materials and Methods This work represents the development and internal validation of a prognostic prediction model. A retrospective analysis was conducted on the clinical and imaging data of 116 patients diagnosed with EC via surgical pathology between 1 Jan 2018 and 1 Jan 2022. Based on the follow-up results, the patients were divided into the remission group (n = 90) and the progression group (n = 26). All patients underwent preoperative standard routine 3.0 T MRI and IVIM-DWI examinations. Differences in parameters such as imaging-based International Federation of Gynecology and Obstetrics (FIGO) staging, IVIM-DWI quantitative parameter values, histological grade, age, maximum tumor diameter, and lymphovascular space invasion (LVSI) between the two groups were compared using independent samples t-test, Mann-Whitney U test, chi-square test, and Fisher's exact test. Stratified sampling was used to divide the data into a training set and a validation set at a ratio of 7∶3. Parameters with statistical significance in the univariate analysis were further analyzed by multivariate logistic regression to identify independent risk factors affecting 3-year DFS in patients with EC. A combined prediction model was then constructed based on these factors. The model performance was evaluated through the following methods: (1) The receiver operating characteristic (ROC) curve was adopted to assess the predictive ability of the model; (2) Calibration curves were used to evaluate the model accuracy; (3) Decision curve analysis (DCA) was applied to assess the clinical net benefit of the model for auxiliary decision-making. A two-tailed P < 0.05 was considered statistically significant.Results There were no significant differences in IVIM-DWI quantitative parameters and clinicopathological parameters between the training cohort and validation cohort (P > 0.05). The true diffusion coefficient D value, histological type and LVSI were independent risk factors for 3-year DFS in patients with EC (all P < 0.05). The area under the ROC curve (AUC) of the combined model was 0.972 [95% confidence interval (CI): 0.942 to 1.000] in the training cohort and 0.870 (95% CI: 0.753 to 0.988) in the validation cohort. DeLong test in the training cohort revealed that the discrimination ability of the combined model was superior to that of the single clinicopathological model and the single D value model (all P < 0.05). In the validation cohort, DeLong test showed no significant difference in AUC between the combined model and the single D value model (P = 0.700), while the combined model was significantly better than histological type (P = 0.002) and LVSI (P = 0.003). The calibration curve was close to the ideal diagonal line, and the Hosmer-Lemeshow test yielded P > 0.05, indicating favorable calibration performance of the model. DCA demonstrated that the combined model achieved higher net benefit within the threshold probability range of 0.10 to 0.80, suggesting promising clinical application value.Conclusions The predictive model incorporating the IVIM-DWI true diffusion coefficient D value, histological type and LVSI shows satisfactory predictive efficacy for 3-year DFS among patients with EC. With favorable calibration and clinical utility, this model may serve as a reference for individualized prognostic assessment and therapeutic decision-making in clinical practice.
[Keywords] endometrial carcinoma；disease-free survival；magnetic resonance imaging；diffusion-weighted imaging；intravoxel incoherent motion

WANG Peipei1   ZHANG Qi2   WANG Tingting1   CHEN Dong1   ZHANG Zhongyuan1   DONG Jiangning1*  

1 Department of radiology, West Branch of the First Affiliated Hospital of University of Science and Technology of China (Anhui Provincial Cancer Hospital), Hefei 230031, China

2 Department of Radiation Oncology, Ion Medical Center (Hefei Ion Medical Center), The First Affiliated Hospital of University of Science and Technology of China, Hefei 230088, China

Corresponding author: DONG J N, E-mail: dongjn@.ustc.edu

Conflicts of interest   None.

Received  2026-04-15
Accepted  2026-09-10
DOI: 10.12015/issn.1674-8034.2026.10.013
Cite this article as: WANG P P, ZHANG Q, WANG T T, et al. The value of IVIM-DWI combined with clinical parameters in predicting disease-free survival of endometrial cancer[J]. Chin J Magn Reson Imaging, 2026, 17(10): 109-116. DOI:10.12015/issn.1674-8034.2026.10.013.

[1]
SIEGEL R L, KRATZER T B, GIAQUINTO A N, et al. Cancer statistics, 2025[J]. CA Cancer J Clin, 2025, 75(1): 10-45. DOI: 10.3322/caac.21871.
[2]
HAN B F, ZHENG R S, ZENG H M, et al. Cancer incidence and mortality in China, 2022[J]. J Natl Cancer Cent, 2024, 4(1): 47-53. DOI: 10.1016/j.jncc.2024.01.006.
[3]
OAKNIN A, BOSSE T J, CREUTZBERG C L, et al. Endometrial cancer: ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up[J]. Ann Oncol, 2022, 33(9): 860-877. DOI: 10.1016/j.annonc.2022.05.009.
[4]
SINGH N, HIRSCHOWITZ L, ZAINO R, et al. Pathologic prognostic factors in endometrial carcinoma (other than tumor type and grade)[J]. Int J Gynecol Pathol, 2019, 38(Supplement 1): S93-S113. DOI: 10.1097/pgp.0000000000000524.
[5]
GHANEM A I, LIN C H, HIJAZ M, et al. Recurrence pattern in women with 2023 FIGO stage IIC endometrial carcinoma with negative nodal evaluation and adjuvant therapies[J/OL]. Int J Radiat Oncol, 2025, 123(1): S186 [2026-04-14]. https://www.redjournal.org/article/S0360-3016(25)05861-4/. DOI: 10.1016/j.ijrobp.2025.07.1265.
[6]
NOUGARET S, SALA E, LAKHMAN Y, et al. Updated ESUR guidelines for endometrial cancer: integrating MRI with the 2023 FIGO staging revolution[J]. Eur Radiol, 2026, 36(1): 1-16. DOI: 10.1007/s00330-025-11700-3.
[7]
BAKSHI N, MODI R, ARYA V, et al. Molecular classification-driven stage migration and prognostic risk stratification in endometrial cancers: FIGO 2023 vs FIGO 2009[J]. Virchows Arch, 2026, 489(2): 341-351. DOI: 10.1007/s00428-025-04366-1.
[8]
LEVINE D A. Integrated genomic characterization of endometrial carcinoma[J]. Nature, 2013, 497(7447): 67-73. DOI: 10.1038/nature12113.
[9]
DENG Y C, BI Q, WANG Q, et al. The performance of DWI and ADC values with different b-values for the diagnosis and staging of endometrial carcinoma at 3T[J/OL]. Cancer Imaging, 2025, 25(1): 126 [2026-04-14]. https://pubmed.ncbi.nlm.nih.gov/41194176/. DOI: 10.1186/s40644-025-00942-5.
[10]
WANG F, WANG Y F, RAN C J, et al. ZOOMit diffusion kurtosis imaging combined with diffusion weighted imaging for the assessment of microsatellite instability in endometrial cancer[J]. Abdom Radiol, 2025, 50(6): 2720-2731. DOI: 10.1007/s00261-024-04720-y.
[11]
ZHAO J F, CAO X S. Research progress of radiomics based on multi-parameter MRI in lymphatic space invasion of endometrial cancer[J]. Chin J Magn Reson Imaging, 2023, 14(6): 171-175. DOI: 10.12015/issn.1674-8034.2023.06.031.
[12]
JIANG X Y, DONG J N. Advances in the assessment of biological behaviors of endometrial carcinoma by IVIM-DWI and texture analysis[J]. Chin J Magn Reson Imaging, 2023, 14(5): 191-195. DOI: 10.12015/issn.1674-8034.2023.05.034.
[13]
WANG H X, YAN R F, LI Z, et al. Quantitative dynamic contrast-enhanced parameters and intravoxel incoherent motion facilitate the prediction of TP53 status and risk stratification of early-stage endometrial carcinoma[J]. Radiol Oncol, 2023, 57(2): 257-269. DOI: 10.2478/raon-2023-0023.
[14]
JACOB H, DYBVIK J A, YTRE-HAUGE S, et al. An MRI-based radiomic prognostic index predicts poor outcome and specific genetic alterations in endometrial cancer[J/OL]. J Clin Med, 2021, 10(3): 538 [2026-04-14]. https://pubmed.ncbi.nlm.nih.gov/33540589/. DOI: 10.3390/jcm10030538.
[15]
XU X Q, LIU F H, KANG L Q. Value of DCE-MRI and IVIM-DWI in predicting pelvic lymph node metastasis from early cervical cancer[J]. Chin J Magn Reson Imaging, 2024, 15(5): 141-147. DOI: 10.12015/issn.1674-8034.2024.05.022.
[16]
KERTES N, ZAFFRANI-REZNIKOV Y, AFACAN O, et al. IVIM-Morph: Motion-compensated quantitative Intra-voxel Incoherent Motion (IVIM) analysis for functional fetal lung maturity assessment from diffusion-weighted MRI data[J/OL]. ArXiv, 2025: arXiv: 2401.07126v3 [2026-04-14]. https://pubmed.ncbi.nlm.nih.gov/39756266/. DOI: 10.1002/jmri.25294.
[17]
LIU Q X, WU S J, YUAN Q, et al. Explainable machine learning model based on DKI, IVIM, and clinical features for preoperative prediction of lymphovascular invasion in rectal cancer[J]. Chin J Magn Reson Imaging, 2025, 16(11): 129-134, 141. DOI: 10.12015/issn.1674-8034.2025.11.019.
[18]
WANG Y A, HE M G, CAO P, et al. Tissue characteristics of endometrial carcinoma analyzed by quantitative synthetic MRI and diffusion-weighted imaging[J/OL]. Diagnostics, 2022, 12(12): 2956 [2026-04-14]. https://pubmed.ncbi.nlm.nih.gov/36552962/. DOI: 10.3390/diagnostics12122956.
[19]
VARSHA R, PRIYANKA, GURUVARE S, et al. Diagnostic accuracy of intravoxel incoherent motion and diffusion kurtosis imaging in evaluating endometrial lesions-A systematic review[J/OL]. Radiography, 2025, 31: 103125 [2026-04-14]. https://pubmed.ncbi.nlm.nih.gov/40819972/. DOI: 10.1016/j.radi.2025.103125.
[20]
MAGDY S, GHONEIM M, FOUDA N, et al. Can IVIM MRI be superior to ADC in predicting aggressive subtypes of endometrial carcinoma: new insights into FIGO 2023 classification[J/OL]. Egypt J Radiol Nucl Med, 2025, 56(1): 182 [2026-04-14]. https://link.springer.com/article/10.1186/s43055-025-01601-2/. DOI: 10.1186/s43055-025-01601-2.
[21]
MENG N, FANG T, FENG P Y, et al. Amide proton transfer-weighted imaging and multiple models diffusion-weighted imaging facilitates preoperative risk stratification of early-stage endometrial carcinoma[J]. J Magn Reson Imaging, 2021, 54(4): 1200-1211. DOI: 10.1002/jmri.27684.
[22]
MENG H, DING S X, ZHANG Y, et al. Value of multimodal diffusion-weighted imaging in preoperative evaluation of ki-67 expression in endometrial carcinoma[J/OL]. Curr Med Imaging, 2024, 20: e110823219686 [2026-04-14]. https://pubmed.ncbi.nlm.nih.gov/37876269/. DOI: 10.2174/1573405620666230811142710.
[23]
ZHANG Q, YU X D, LIN M, et al. Multi-b-value diffusion weighted imaging for preoperative evaluation of risk stratification in early-stage endometrial cancer[J/OL]. Eur J Radiol, 2019, 119: 108637 [2026-04-14]. https://pubmed.ncbi.nlm.nih.gov/31446209/. DOI: 10.1016/j.ejrad.2019.08.006.
[24]
SHI B, DONG J N, ZHANG L X, et al. A combination analysis of IVIM-DWI biomarkers and T2WI-based texture features for tumor differentiation grade of cervical squamous cell carcinoma[J/OL]. Contrast Media Mol Imaging, 2022, 2022: 2837905 [2026-04-14]. https://pubmed.ncbi.nlm.nih.gov/35360261/. DOI: 10.1155/2022/2837905.
[25]
ZHANG L, YANG L Q, WEN L, et al. Noninvasively evaluating the grading of glioma by multiparametric magnetic resonance imaging[J/OL]. Acad Radiol, 2021, 28(5): e137-e146 [2026-04-14]. https://pubmed.ncbi.nlm.nih.gov/32417035/. DOI: 10.1016/j.acra.2020.03.035.
[26]
XIE L L, LIN Z Q. Interpretation of 2025 NCCN clinical practice guidelines for uterine tumors (1st edition)[J]. Chin J Pract Gynecol Obstet, 2025, 41(1): 103-109. DOI: 10.19538/j.fk2025010123.
[27]
FAVIER A, RAZAKAMANANTSOA L, MEREAUX J, et al. Validation of MRI short axis analysis for predicting lymphovascular invasion in endometrial cancer patients[J/OL]. Eur J Radiol, 2025, 190: 112259 [2026-04-14]. https://pubmed.ncbi.nlm.nih.gov/40561675/. DOI: 10.1016/j.ejrad.2025.112259.
[28]
YI Q Q, WANG Y, ZOU S S, et al. Super-resolution reconstruction technique enhances the diagnostic efficacy of deep learning-based prediction of lymphvascular space invasion in endometrial cancer[J]. Chin J Magn Reson Imaging, 2025, 16(10): 83-88. DOI: 10.12015/issn.1674-8034.2025.10.013.
[29]
WANG Y D, LIU W, LU Y Y, et al. Fully automated identification of lymph node metastases and lymphovascular invasion in endometrial cancer from multi-parametric MRI by deep learning[J]. J Magn Reson Imaging, 2024, 60(6): 2730-2742. DOI: 10.1002/jmri.29344.
[30]
LONG L, LIU M L, DENG X J, et al. Tumor stiffness measurement at multifrequency MR elastography to predict lymphovascular space invasion in endometrial cancer[J/OL]. Radiology, 2024, 311(3): e232242 [2026-04-14]. https://pubmed.ncbi.nlm.nih.gov/38832881/. DOI: 10.1148/radiol.232242.
[31]
YUE X N, HE X Y, WU J J, et al. Endometrioid adenocarcinoma: combined multiparametric MRI and tumour marker HE4 to evaluate tumour grade and lymphovascular space invasion[J/OL]. Clin Radiol, 2023, 78(8): e574-e581 [2026-04-14]. https://pubmed.ncbi.nlm.nih.gov/37183140/. DOI: 10.1016/j.crad.2023.04.005.
[32]
RESTAINO S, TORTORELLA L, DINOI G, et al. Semiquantitative evaluation of lymph-vascular space invasion in patients affected by endometrial cancer: Prognostic and clinical implications[J]. Eur J Cancer, 2021, 142: 29-37. DOI: 10.1016/j.ejca.2020.10.011.
[33]
LEE M, YOON H, KIM U, et al. Improved prognostic stratification with 2023 international federation of gynecology and obstetrics staging in endometrial cancer reflecting poor prognosis of aggressive histological types and p53 abnormality[J/OL]. Lab Investig, 2025, 105(9): 104189 [2026-04-14]. https://pubmed.ncbi.nlm.nih.gov/40311877/. DOI: 10.1016/j.labinv.2025.104189.
[34]
FERRARI F, GOZZINI E, CONFORTI J, et al. Impact of the FIGO 2023 staging system on the adjuvant treatment of endometrial cancer: a comparative analysis with FIGO 2009[J/OL]. Cancers, 2025, 17(6): 934 [2026-04-14]. https://pubmed.ncbi.nlm.nih.gov/40149271/. DOI: 10.3390/cancers17060934.
[35]
UREYEN I, KARALOK A, TURKMEN O, et al. Factors predicting recurrence in patients with stage IA endometrioid endometrial cancer: what is the importance of LVSI [J]. Arch Gynecol Obstet, 2020, 301(3): 737-744. DOI: 10.1007/s00404-019-05418-z.

PREV Venous phase spectral CT parameters combined with MRI for preoperative assessment of Ki-67 expression levels in breast tumors
NEXT Preoperative prediction of extra-pelvic peritoneal metastasis in epithelial ovarian cancer using habitat radiomics from T2-weighted MRI
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn