Share：
Share this content in WeChat
X
[Chinese] [PDF] 27 1
• Clinical Article •
Value of intratumoral and peritumoral DCE-MRI combined with mDixon-Quant parameters in predicting lymphovascular invasion of rectal cancer
SONG Jian  REN Junjie  ZHAO Yadi  JI Yuxia  SHI Hongwei  XIE Zongyuan 

Cite this article as SONG J, REN J J, ZHAO Y D, et al. Value of intratumoral and peritumoral DCE-MRI combined with mDixon-Quant parameters in predicting lymphovascular invasion of rectal cancer[J]. Chin J Magn Reson Imaging, 2026, 17(6): 103-109, 124. DOI:10.12015/issn.1674-8034.2026.06.013.


[Abstract] Objective To evaluate the value of combining dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and mDixon-Quant sequences in intratumoral and peritumoral regions for assessing lymphovascular invasion (LVI) status in rectal cancer.Materials and Methods A retrospective analysis was conducted on the clinical and imaging data of 78 patients with pathologically confirmed rectal cancer. Based on postoperative pathological results, patients were categorized into lymphovascular invasion (LVI) positive (n = 33) and negative groups (n = 45). The dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters including the volume transfer constant (Ktrans), rate constant (Kep), and extravascular extracellular volume fraction (Ve), as well as the mDixon-Quant parameters R2* and fat fraction (FF) were measured within both the tumor and peritumoral regions. The differences in each parameter between the two groups were compared. Multivariate logistic regression analysis was performed to screen independent correlated factors for LVI in rectal cancer and construct a predictive model. Bootstrap resampling was adopted for internal validation; the area under the curve (AUC) of receiver operating characteristic (ROC) was used to evaluate the discrimination ability of the model; the Hosmer-Lemeshow test and calibration curves were applied to assess the calibration degree; decision curve analysis (DCA) was utilized to verify the clinical validity of the model.Results Intratumoral Ktrans, R2*, FF and peritumoral Ktrans, R2*were significantly higher in the LVI (+) group than in the LVI (-) group, while peritumoral FF was significantly lower (all P < 0.05). Multivariate logistic regression analysis demonstrated that intratumoral Ktrans, R2* FF and peritumoral R2*, FF were independent influencing factors for LVI in rectal cancer. The AUC of the model constructed by combined parameters was 0.854 (95% CI: 0.764 to 0.944), and the AUC reached 0.875 (95% CI: 0.780 to 0.953) after internal validation via the Bootstrap method, indicating excellent discrimination performance of the model. The calibration curve revealed a high consistency between the predicted probability and the actual risk. The Hosmer-Lemeshow test demonstrated a good model fitting effect (χ2 = 4.137, P = 0.845). The results of DCA verified that the model possessed favorable clinical practical value.Conclusion Intratumoral and peritumoral DCE-MRI and mDixon-Quant can effectively evaluate the LVI status of rectal cancer. The combination of multiple parameters can improve predictive performance, better reflect tissue perfusion, hypoxia and lipid metabolism, and provide a reliable non-invasive imaging approach for the preoperative assessment of LVI in rectal cancer.
[Keywords] rectal cancer；lymphovascular invasion；lipid metabolism；magnetic resonance imaging；dynamic contrast-enhanced magnetic resonance imaging；mDixon-Quant

SONG Jian1   REN Junjie1   ZHAO Yadi1   JI Yuxia2   SHI Hongwei2   XIE Zongyuan1*  

1 Department of MRI, Medical Imaging Center, North China University of Science and Technology Affiliated Hospital, Tangshan 063000, China

2 Department of Medical Imaging, Tangshan Central Hospital, Tangshan 063000, China

Corresponding author: XIE Z Y, E-mail: 93347795@qq.com

Conflicts of interest   None.

Received  2025-12-29
Accepted  2026-05-14
DOI: 10.12015/issn.1674-8034.2026.06.013
Cite this article as SONG J, REN J J, ZHAO Y D, et al. Value of intratumoral and peritumoral DCE-MRI combined with mDixon-Quant parameters in predicting lymphovascular invasion of rectal cancer[J]. Chin J Magn Reson Imaging, 2026, 17(6): 103-109, 124. DOI:10.12015/issn.1674-8034.2026.06.013.

[1]
BRAY F, LAVERSANNE M, SUNG H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2024, 74(3): 229-263. DOI: 10.3322/caac.21834.
[2]
WU Q, FAN B N, LI Y. Analysis and interpretation of the 2022 Global Cancer Statistics Report: cancer burden and epidemiological trends in China and the world[J]. J Diagn Concepts Pract, 2025, 24(2): 135-145. DOI: 10.16150/j.1671-2870.2025.02.004.
[3]
SU Y X, ZHAO H Y, LYU Z H, et al. Quantification of intratumoral heterogeneity based on habitat analysis for preoperative assessment of lymphovascular invasion in colorectal cancer[J]. Acad Radiol, 2025, 32(8): 4532-4543. DOI: 10.1016/j.acra.2025.03.014.
[4]
WU C Q, HU H J, BAO F E, et al. Development of a multimodal predictive model using PET/CT radiomics and clinical data for preoperative assessment of lymphovascular invasion in gastric cancer[J]. Am J Transl Res, 2025, 17(7): 5441-5452. DOI: 10.62347/PBNU9406.
[5]
YUAN J, LIU K, ZHANG Y, et al. Quantitative dynamic contrast-enhance MRI parameters for rectal carcinoma characterization: correlation with tumor tissue composition[J/OL]. World J Surg Oncol, 2023, 21(1): 306 [2025-09-30]. https://doi.org/10.1186/s12957-023-03193-5. DOI: 10.1186/s12957-023-03193-5.
[6]
PRAJAPATI P K, GUPTA R K, SINGH A. Deep learning approach for DCE-MRI parameter estimation: Evaluating signal intensity and concentration-time curve-based convolution-neural-networks[J/OL]. Magn Reson Imaging, 2026, 126: 110559 [2025-09-30]. https://pubmed.ncbi.nlm.nih.gov/41207450/. DOI: 10.1016/j.mri.2025.110559.
[7]
ZHAO R, LU H, LI Y B, et al. Nomogram for early prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using dynamic contrast-enhanced and diffusion-weighted MRI[J]. Acad Radiol, 2022, 29: S155-S163. DOI: 10.1016/j.acra.2021.01.023.
[8]
YANG C, WANG Z, ZHANG J L, et al. Mri assessment of renal lipid deposition and abnormal oxygen metabolism of type 2 diabetes mellitus based on mDixon-Quant[J]. J Magn Reson Imaging, 2023, 58(5): 1408-1417. DOI: 10.1002/jmri.28701.
[9]
MENG X, ZHANG X W, TIAN S F, et al. Evaluation of lymphovascular space invasion in endometrial carcinoma by APTw and mDixon-Quant[J]. Acta Radiol, 2024, 65(11): 1440-1446. DOI: 10.1177/02841851241277339.
[10]
MENG X, TIAN S F, MA C J, et al. APTw combined with mDixon-Quant imaging to distinguish the differentiation degree of cervical squamous carcinoma[J/OL]. Front Oncol, 2023, 13: 1105867 [2025-09-30]. https://doi.org/10.3389/fonc.2023.1105867. DOI: 10.3389/fonc.2023.1105867.
[11]
ZHAO Y D, LI W L, SONG J, et al. IVIM, mDixon-Quant multiparameter quantitative imaging combined with blood cell parameters to assess Ki-67 expression levels in rectal cancer[J]. Chin J Magn Reson Imaging, 2024, 15(8): 145-151. DOI: 10.12015/issn.1674-8034.2024.08.022.
[12]
FENG Y R, HU T D, GU W C, et al. Mesorectal Fascia Involvement by tumor deposits or extramural vascular invasion at MRI predicts prognosis in rectal cancer[J/OL]. Radiology, 2025, 317: e243627 [2025-09-30]. https://doi.org/10.1148/radiol.243627. DOI: 10.1148/radiol.243627.
[13]
LI Q G, XIAO Y W, HAN L Y, et al. Microbiome dysbiosis, neutrophil recruitment and mesenchymal transition of mesothelial cells promotes peritoneal metastasis of colorectal cancer[J]. Nat Cancer, 2025, 6(3): 493-510. DOI: 10.1038/s43018-025-00910-9.
[14]
LIANG Z Y, YU M L, YANG H, et al. Beyond the tumor region: Peritumoral radiomics enhances prognostic accuracy in locally advanced rectal cancer[J/OL]. World J Gastroenterol, 2025, 31(8): 99036 [2025-09-30]. https://doi.org/10.3748/wjg.v31.i8.99036. DOI: 10.3748/wjg.v31.i8.99036.
[15]
KUHN E, GAMBINI D, DESPINI L, et al. Updates on lymphovascular invasion in breast cancer[J/OL]. Biomedicines, 2023, 11(3): 968 [2025-09-30]. https://doi.org/10.3390/biomedicines11030968. DOI: 10.3390/biomedicines11030968.
[16]
PENG L P, NIU J J, HUANG G, et al. Noninvasive prediction of lymphovascular invasion in rectal cancer without lymph node metastasis using a SHAP-interpretable combined model integrating MRI radiomics features and clinical immune-inflammatory biomarkers: a bicenter study[J/OL]. Cancer Imaging, 2025, 25(1): 123 [2025-09-30]. https://doi.org/10.1186/s40644-025-00946-1. DOI: 10.1186/s40644-025-00946-1.
[17]
HU J Q, ZHANG M H, QI Y, et al. Integrating lymphovascular and perineural invasion into TNM staging: a novel ITNM system for enhanced prognostic stratification in colorectal cancer[J/OL]. Front Oncol, 2026, 16: 1745324 [2025-09-30]. https://doi.org/10.3389/fonc.2026.1745324. DOI: 10.3389/fonc.2026.1745324.
[18]
BHUTIANI N, YOUSEF M M, YOUSEF A M, et al. Does extramural vascular invasion predict response to neoadjuvant therapy in locally advanced rectal cancer [J]. Clin Colorectal Cancer, 2025, 24(2): 272-279. DOI: 10.1016/j.clcc.2025.02.002.
[19]
SUN Q Q, LIU T X, LIU P, et al. Perineural and lymphovascular invasion predicts for poor prognosis in locally advanced rectal cancer after neoadjuvant chemoradiotherapy and surgery[J]. J Cancer, 2019, 10(10): 2243-2249. DOI: 10.7150/jca.31473.
[20]
WANG Q, ZHANG X Y, YANG J F, et al. Comparison of dynamic contrast-enhanced-magnetic resonance imaging parameters and serum markers in preoperative rectal cancer evaluation: Combined diagnostic value[J/OL]. World J Gastrointest Oncol, 2025, 17(5): 103809 [2025-09-30]. https://doi.org/10.4251/wjgo.v17.i5.103809. DOI: 10.4251/wjgo.v17.i5.103809.
[21]
FAN Y Y, CHEN M N, HUANG H Y, et al. Predicting lymphovascular invasion in rectal cancer: evaluating the performance of golden-angle radial sparse parallel MRI for rectal perfusion assessment[J/OL]. Sci Rep, 2023, 13(1): 8453 [2025-09-30]. https://pubmed.ncbi.nlm.nih.gov/37231115/. DOI: 10.1038/s41598-023-35763-8.
[22]
TACCONI C, CORREALE C, GANDELLI A, et al. Vascular endothelial growth factor C disrupts the endothelial lymphatic barrier to promote colorectal cancer invasion[J]. Gastroenterology, 2015, 148(7): 1438-1451. DOI: 10.1053/j.gastro.2015.03.005.
[23]
ZHOU J, SONG Q, LI H Z, et al. Targeting circ-0034880-enriched tumor extracellular vesicles to impede SPP1highCD206+ pro-tumor macrophages mediated pre-metastatic niche formation in colorectal cancer liver metastasis[J/OL]. Mol Cancer, 2024, 23(1): 168 [2025-09-30]. https://pubmed.ncbi.nlm.nih.gov/39164758/. DOI: 10.1186/s12943-024-02086-9.
[24]
LI K Y, XUE W H, LU Z H, et al. Tumor-derived exosomal ADAM17 promotes pre-metastatic niche formation by enhancing vascular permeability in colorectal cancer[J/OL]. J Exp Clin Cancer Res, 2024, 43(1): 59 [2025-09-30]. https://pubmed.ncbi.nlm.nih.gov/38413999/. DOI: 10.1186/s13046-024-02991-3.
[25]
GUI Y Q, PENG M X, HUANG Y X, et al. New insights into extracellular vesicles in metastatic cancer: From mechanisms to diagnostics and targeted therapies[J]. Mol Ther, 2025, 33(10): 4731-4747. DOI: 10.1016/j.ymthe.2025.07.016.
[26]
WANG Y, JU Y, AN Q, et al. mDIXON-Quant for differentiation of renal damage degree in patients with chronic kidney disease[J/OL]. Front Endocrinol, 2023, 14: 1187042 [2025-09-30]. https://doi.org/10.3389/fendo.2023.1187042. DOI: 10.3389/fendo.2023.1187042.
[27]
LUO X R, HUANG L Z, YIN J, et al. FSTL3 promotes colorectal cancer by activating the HIF1 pathway[J/OL]. Gene, 2025, 954: 149435 [2025-09-30]. https://doi.org/10.1016/j.gene.2025.149435. DOI: 10.1016/j.gene.2025.149435.
[28]
MA C J, TIAN S F, CHEN L H, et al. Quantitative assessment of microsatellite instability in endometrial cancer by T2 mapping combined with mDixon-Quant multiparameter imaging[J]. Chin J Magn Reson Imaging, 2022, 13(8): 48-54. DOI: 10.12015/issn.1674-8034.2022.08.009.
[29]
ZHAO Y D, YANG J, XUE H, et al. Prediction of lymphovascular invasion and lymph node metastasis in rectal cancer based on mDixon-Quant combined with blood cell parameters[J]. Chin J Clin Oncol, 2024, 51(21): 1092-1097. DOI: 10.12354/j.issn.1000-8179.2024.20240857.
[30]
ZHOU M, HUANG H, FAN Y, et al. The application of quantitative perfusion analysis of golden-angle radial sparse parallel MRI and R2 value for predicting pathological prognostic factors in rectal cancer[J]. Clin Radiol, 2024, 79(2): 124-132. DOI: 10.1016/j.crad.2023.10.027.
[31]
MA Y X, NENKOV M, CHEN Y, et al. The role of adipocytes recruited as part of tumor microenvironment in promoting colorectal cancer metastases[J/OL]. Int J Mol Sci, 2024, 25(15): 8352 [2025-09-30]. https://doi.org/10.3390/ijms25158352. DOI: 10.3390/ijms25158352.
[32]
SHANG C L, QIAO J, GUO H Y. The dynamic behavior of lipid droplets in the pre-metastatic niche[J/OL]. Cell Death Dis, 2020, 11: 990 [2025-09-30]. https://doi.org/10.1038/s41419-020-03207-0. DOI: 10.1038/s41419-020-03207-0.
[33]
LEI M C, LI J S, REN X Q, et al. Tumor-adipose assembloids reveal cell-fate-transition-triggered multistage collective invasions[J/OL]. Cell Syst, 2025, 16(8): 101353 [2025-09-30]. https://doi.org/10.1016/j.cels.2025.101353. DOI: 10.1016/j.cels.2025.101353.
[34]
PIETRASZEK-GREMPLEWICZ K, OLSZAŃSKA J, DOMAGALSKI M, et al. Dedifferentiationand metabolic reprogramming of human adipocytes in the tumor niche triggered by colorectal cancer cells[J/OL]. Biol Res, 2025, 58(1): 75 [2025-09-30]. https://doi.org/10.1186/s40659-025-00660-z. DOI: 10.1186/s40659-025-00660-z.
[35]
CONTI G, CALDERAN L, QUINTERO SIERRA L A, et al. Tumor and peritumoral adipose tissue crosstalk: De-differentiated adipocytes influence spread of colon carcinoma cells[J/OL]. Tissue Cell, 2023, 80: 101990 [2025-09-30]. https://doi.org/10.1016/j.tice.2022.101990. DOI: 10.1016/j.tice.2022.101990.

PREV Pre-treatment MRI radiomics and deep learning predict early progression of operable rectal cancer
NEXT The value of combined model based on 3.0 T high resolution T2WI imaging features in preoperative prediction of lymphatic vessel space invasion in endometrial cancer
  


LINK


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