Share:
Share this content in WeChat
X
Clinical Article
Application research of cellular microstructural parameters based on magnetic resonance in predicting lymph node metastasis and tumor deposit of rectal cancer
YI Siqi  LI Yanwan  CHEN Meining  ZHENG Ping  LI Hang  ZHOU Peng  DONG Xiaolei  CHEN Xiaoli 

DOI:10.12015/issn.1674-8034.2025.11.020.


[Abstract] Objective To evaluate the efficacy of rectal cancer cell microstructural parameters fitted by time-dependent diffusion magnetic resonance imaging (td-dMRI) in preoperative prediction of lymph node metastasis (LNM) and tumor deposit (TD).Materials and Methods A retrospective analysis was conducted on the imaging and clinical data of 88 patients with rectal cancer who underwent surgery in our hospital from December 2023 to March 2025. All patients received preoperative td-dMRI examinations. Using the IMPULSED model, cellular microstructural parameters and apparent diffusion coefficient (ADC) were extracted, including cell diameter (d), intracellular volume fraction (Vin), extracellular diffusion coefficient (Dex), cellularity, ADCOGSE25Hz/PGSE value, and ADCOGSE40Hz/PGSE value. The correlation between td-dMRI parameters and pathological results was verified. Based on postoperative pathological data, patients were divided into LNM-negative group (Neg-LNM, n = 40) and LNM-positive group (Pos-LNM, n = 48), as well as TD-negative group (Neg-TD, n = 70) and TD-positive group (Pos-TD, n = 18). Differences in microstructural parameters and ADC ratios between the groups were compared. Receiver operating characteristic (ROC) curves and logistic regression analysis were used to assess the diagnostic efficacy of single and combined models in predicting TD and LNM.Results There was a good correlation between td-dMRI parameters and pathological measurements (n = 16; all r > 0.70; all P < 0.05). In the Pos-LNM group, the d value of cancer cells and ADCOGSE25Hz/PGSE values were significantly higher than those in the Neg-LNM group (both P < 0.05), while cellularity was lower than that in the Neg-LNM group (P < 0.05). In the Pos-TD group, the d value and ADCOGSE25Hz/PGSE value were significantly higher than those in the Neg-TD group (both P < 0.05), and cellularity was lower than that in the Neg-TD group (P < 0.05). Univariate logistic regression analysis showed that d value and ADCOGSE25Hz/PGSE value could predict LNM and TD (all P < 0.05). Multivariate logistic regression analysis combining d and ADCOGSE25Hz/PGSE indicated that the combined model could predict LNM and TD (P < 0.05). The AUC values of the d value, ADCOGSE25Hz/PGSE value, and the combined indicator for predicting LNM and TD were all greater than 0.70.Conclusions The microstructural parameter d and ADC ratio based on td-dMRI have favorable clinical application potential in predicting LNM and TD.
[Keywords] rectal cancer;tumor deposit;lymph nodes metastatic;time-dependent diffusion magnetic resonance imaging;cellular microstructural parameters;apparent diffusion coefficient

YI Siqi1   LI Yanwan1, 2   CHEN Meining3   ZHENG Ping4   LI Hang5   ZHOU Peng1   DONG Xiaolei1   CHEN Xiaoli1*  

1 Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Department of Radiology of University of Electronic Science and Technology of China, Chengdu 610041, China

2 School of Medicine University of Electronic Science and Technology of China, Chengdu 610051, China

3 Siemens Healthcare Systems Co., Ltd. Chengdu Branch, Chengdu 610041, China

4 Department of Pathology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu 610041, China

5 Department of Radiology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu 610072, China

Corresponding author: CHEN X L, E-mail: xiaolichen20@163.com

Conflicts of interest   None.

Received  2025-09-02
Accepted  2025-10-28
DOI: 10.12015/issn.1674-8034.2025.11.020
DOI:10.12015/issn.1674-8034.2025.11.020.

[1]
BENSON A B, VENOOK A P, ADAM M, et al. NCCN guidelines® insights: rectal cancer, version 3.2024[J]. J Natl Compr Cancer Netw, 2024, 22(6): 366-375. DOI: 10.6004/jnccn.2024.0041.
[2]
HU Z Y, LI Y, CHEN B, et al. Study of tumor deposits' application in postoperative pathologic staging definition and the prognostic value in colorectal cancer patients[J]. Chin J Colorectal Dis Electron Ed, 2020, 9(2): 137-143. DOI: 10.3877/cma.j.issn.2095-3224.2020.02.006.
[3]
NAGTEGAAL I D, KNIJN N, HUGEN N, et al. Tumor deposits in colorectal cancer: improving the value of modern staging-a systematic review and meta-analysis[J]. J Clin Oncol, 2017, 35(10): 1119-1127. DOI: 10.1200/JCO.2016.68.9091.
[4]
AL-SUKHNI E, MILOT L, FRUITMAN M, et al. Diagnostic accuracy of MRI for assessment of T category, lymph node metastases, and circumferential resection margin involvement in patients with rectal cancer: a systematic review and meta-analysis[J]. Ann Surg Oncol, 2012, 19(7): 2212-2223. DOI: 10.1245/s10434-011-2210-5.
[5]
XU J Z. Probing neural tissues at small scales: Recent progress of oscillating gradient spin echo (OGSE) neuroimaging in humans[J/OL]. J Neurosci Methods, 2021, 349: 109024 [2025-09-01]. https://pubmed.ncbi.nlm.nih.gov/33333089/. DOI: 10.1016/j.jneumeth.2020.109024.
[6]
CHEN J, WU Z R, ZHANG Z, et al. Apparent diffusion coefficient and tissue stiffness are associated with different tumor microenvironment features of hepatocellular carcinoma[J]. Eur Radiol, 2024, 34(11): 6980-6991. DOI: 10.1007/s00330-024-10743-2.
[7]
LI Y W, CHEN X L. Advances in time-dependent diffusion MRI for tumor diagnosis and treatment response evaluation[J]. Chin J Magn Reson Imag, 2025, 16(3): 228-234. DOI: 10.12015/issn.1674-8034.2025.03.039.
[8]
LEMBERSKIY G, FIEREMANS E, VERAART J, et al. Characterization of prostate microstructure using water diffusion and NMR relaxation[J/OL]. Front Phys, 2018, 6: 91 [2025-09-01]. https://pubmed.ncbi.nlm.nih.gov/30568939/. DOI: 10.3389/fphy.2018.00091.
[9]
JIANG X Y, LI H, XIE J P, et al. In vivo imaging of cancer cell size and cellularity using temporal diffusion spectroscopy[J]. Magn Reson Med, 2017, 78(1): 156-164. DOI: 10.1002/mrm.26356.
[10]
SOLOMON E, LEMBERSKIY G, BAETE S, et al. Time-dependent diffusivity and kurtosis in phantoms and patients with head and neck cancer[J]. Magn Reson Med, 2023, 89(2): 522-535. DOI: 10.1002/mrm.29457.
[11]
BA R C, WANG X X, ZHANG Z L, et al. Diffusion-time dependent diffusion MRI: effect of diffusion-time on microstructural mapping and prediction of prognostic features in breast cancer[J]. Eur Radiol, 2023, 33(9): 6226-6237. DOI: 10.1007/s00330-023-09623-y.
[12]
XU J, JIANG X, LI H, et al. Magnetic resonance imaging of mean cell size in human breast tumors[J]. Magn Reson Med, 2020, 83(6): 2002-2014. DOI: 10.1002/mrm.28056.
[13]
JOHNSTON E W, BONET-CARNE E, FERIZI U, et al. VERDICT MRI for prostate cancer: intracellular volume fraction versus apparent diffusion coefficient[J]. Radiology, 2019, 291(2): 391-397. DOI: 10.1148/radiol.2019181749.
[14]
WU D, JIANG K W, LI H, et al. Time-dependent diffusion MRI for quantitative microstructural mapping of prostate cancer[J]. Radiology, 2022, 303(3): 578-587. DOI: 10.1148/radiol.211180.
[15]
WEISER M R. AJCC 8th edition: colorectal cancer[J]. Ann Surg Oncol, 2018, 25(6): 1454-1455. DOI: 10.1245/s10434-018-6462-1.
[16]
LE BIHAN D, IIMA M. Correction: diffusion magnetic resonance imaging: what water tells us about biological tissues[J/OL]. PLoS Biol, 2015, 13(9): e1002246 [2025-09-01]. https://pubmed.ncbi.nlm.nih.gov/26334873/. DOI: 10.1371/journal.pbio.1002246.
[17]
JIANG X Y, XU J Z, GORE J C. Quantitative temporal diffusion spectroscopy as an early imaging biomarker of radiation therapeutic response in gliomas: a preclinical proof of concept[J]. Adv Radiat Oncol, 2019, 4(2): 367-376. DOI: 10.1016/j.adro.2018.11.003.
[18]
WANG X X, BA R C, HUANG Y, et al. Time-dependent diffusion MRI helps predict molecular subtypes and treatment response to neoadjuvant chemotherapy in breast cancer[J/OL]. Radiology, 2024, 313(1): e240288 [2025-09-01]. https://pubmed.ncbi.nlm.nih.gov/39436292/. DOI: 10.1148/radiol.240288.
[19]
LI Y, KANG X W, XI Y B, et al. Differentiation of cerebral glioblastoma and infectious lesions with ring-shape enhancement using diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging[J]. Radiol Pract, 2024, 39(2): 175-180. DOI: 10.13609/j.cnki.1000-0313.2024.02.006.
[20]
IIMA M, YAMAMOTO A, KATAOKA M, et al. Time-dependent diffusion MRI to distinguish malignant from benign head and neck tumors[J]. J Magn Reson Imaging, 2019, 50(1): 88-95. DOI: 10.1002/jmri.26578.
[21]
ZHANG H X, LIU K Y, BA R C, et al. Histological and molecular classifications of pediatric glioma with time-dependent diffusion MRI-based microstructural mapping[J]. Neuro Oncol, 2023, 25(6): 1146-1156. DOI: 10.1093/neuonc/noad003.
[22]
HONG Y, SONG G S, JIA Y P, et al. Predicting tumor deposits in patients with rectal cancer: Using the models of multiple mathematical parameters derived from diffusion-weighted imaging[J/OL]. Eur J Radiol, 2022, 157: 110573 [2025-09-01]. https://pubmed.ncbi.nlm.nih.gov/36347167/. DOI: 10.1016/j.ejrad.2022.110573.
[23]
KAMIMURA K, KAMIMURA Y, NAKANO T, et al. Differentiating brain metastasis from glioblastoma by time-dependent diffusion MRI[J/OL]. Cancer Imaging, 2023, 23(1): 75 [2025-09-01]. https://pubmed.ncbi.nlm.nih.gov/37553578/. DOI: 10.1186/s40644-023-00595-2.
[24]
JIANG X Y, LI H, DEVAN S P, et al. MR cell size imaging with temporal diffusion spectroscopy[J/OL]. Magn Reson Imaging, 2021, 77: 109-123 [2025-09-01]. https://pubmed.ncbi.nlm.nih.gov/33338562/. DOI: 10.1016/j.mri.2020.12.010.
[25]
XU H, YANG A, HE Y K, et al. Time-dependent diffusion MRI parameters for differentiating invasive breast cancer with ductal carcinoma in situ and simple invasive breast cancer[J]. Chin J Interv Imag Ther, 2025, 22(4): 255-259. DOI: 10.13929/j.issn.1672-8475.2025.04.006.
[26]
ZHANG L J, LONG X, CHEN L Q, et al. Detecting cellular microstructural changes of liver fibrosis with time-dependent diffusion MRI[J/OL]. Radiology, 2024, 313(1): e240343 [2025-09-01]. https://pubmed.ncbi.nlm.nih.gov/39352282/. DOI: 10.1148/radiol.240343.
[27]
CAO W X, CHEN Y L, WANG H J. Time-dependent diffusion MRI in prostate cancer: current status and future prospects[J]. Diagn Imag Interv Radiol, 2024, 33(4): 298-303. DOI: 10.3969/j.issn.1005-8001.2024.04.009.
[28]
DING J, YOU C, GU Y J. Progress of quantitative MRI technology based on time diffusion spectrum[J]. Chin J Radiol, 2023, 57(10): 1124-1127. DOI: 10.3760/cma.j.cn112149-20230311-00179.
[29]
SUROV A, EGER K I, POTRATZ J, et al. Apparent diffusion coefficient correlates with different histopathological features in several intrahepatic tumors[J]. Eur Radiol, 2023, 33(9): 5955-5964. DOI: 10.1007/s00330-023-09788-6.
[30]
XU T, LIU X W, PENG Y J, et al. Basic principle of time-dependent diffusion MRI and its application in prostate cancer[J]. Chin J Magn Reson Imag, 2023, 14(8): 171-175. DOI: 10.12015/issn.1674-8034.2023.08.030.
[31]
WANG X Y, ZHANG Y, CHENG J L, et al. Comparison of the efficacy of amide proton transfer weighted imaging and time-dependent diffusion MRI in the diagnosis of breast malignant lesions[J]. Chin J Radiol, 2024(6): 611-619. DOI: 10.3760/cma.j.cn112149-20230910-00176.
[32]
JIANG X Y, LI H, XIE J P, et al. Quantification of cell size using temporal diffusion spectroscopy[J]. Magn Reson Med, 2016, 75(3): 1076-1085. DOI: 10.1002/mrm.25684.

PREV Explainable machine learning model based on DKI, IVIM, and clinical features for preoperative prediction of lymphovascular invasion in rectal cancer
NEXT Prediction of zonal heterogeneity in prostate cancer using multi-parametric magnetic resonance habitat imaging
  



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