Share：
Share this content in WeChat
X
[Chinese][PDF]1843
• Special Focu •
The value of IVIM parameters in predicting synchronous liver metastasis of rectal cancer in tumor and mesorectal
CHEN Jie  CHEN Jianyou  LI Zhenhui  JIANG Jiezhi  LI Zhilin  AI Conghui  MA Yi  TAN Jing 

Cite this article as: CHEN J, CHEN J Y, LI Z H, et al. The value of IVIM parameters in predicting synchronous liver metastasis of rectal cancer in tumor and mesorectal[J]. Chin J Magn Reson Imaging, 2025, 16(1): 36-41, 67. DOI:10.12015/issn.1674-8034.2025.01.006.


[Abstract] Objective Through the study of intravoxel incoherent motion (IVIM), this research investigates the predictive value of tumor and mesorectum parameters for synchronous rectal liver metastasis (SRLM) in rectal cancer.Materials and Methods A retrospective analysis was conducted on data from 112 patients with pathologically confirmed rectal cancer, including 42 patients with SRLM. The patients were divided into the SRLM group (n = 42) and the non-SRLM group (n = 70). On the maximum cross-sectional image of the tumor, three regions of interest (ROI) were delineated: one in the tumor, one in the near-tumor area (ROI < 5 mm from the tumor), and one in the distant-tumor area (ROI > 10 mm from the tumor). IVIM parameters apparent diffusion coefficient (ADC), pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) were measured The Mann-Whitney U test and the Wilcoxon test were used to compare the statistical significance of parameter differences between and within the groups, respectively. The predictive performance of parameters showing statistically significant differences between groups was evaluated using receiver operating characteristic (ROC) curves.Results Compared with the non- SRLM group, the SRLM group showed significantly increased parameters ADC, D, and f in the distant tumor area (P < 0.001), and increased D and f in the near tumor area (P < 0.05). However, the differences in tumor parameters were not statistically significant (P > 0.05). After intragroup comparison, the ADC values in the distal tumor regions of both groups were significantly lower than those in the corresponding proximal tumor regions and tumor parameters (P < 0.05), and the D values were significantly lower than those in the corresponding proximal tumor regions and tumor parameters (P < 0.001). Yet, ADC and D in the near tumor area showed no statistically significant differences compared to the corresponding tumor parameters (P > 0.05). Although the parameter f in the distant tumor area was lower than in the near tumor area, this difference was not statistically significant in the SRLM group (P > 0.05). The parameters ADC, D, and f in the distant tumor area predicted the area under the curve (AUC) for predicting SRLM using the ADC, D, and f parameters of the mesorectum distal to the tumor were 0.769 (95% CI: 0.675 to 0.862), 0.745 (95% CI: 0.644 to 0.845), and 0.733 (95% CI: 0.635 to 0.831), respectively.Conclusions The IVIM parameters ADC, f, and D in the distant tumor area of the mesorectum can serve as imaging biomarkers to predict the likelihood of SRLM in rectal cancer. Their assessment is of significant clinical importance for the timely diagnosis of SRLM and for identifying patients with occult and high-risk Liver metastases.
[Keywords] rectal cancer；liver metastasis；mesorectum；magnetic resonance imaging；intravoxel incoherent motion；prediction

CHEN Jie   CHEN Jianyou   LI Zhenhui   JIANG Jiezhi   LI Zhilin   AI Conghui   MA Yi   TAN Jing*  

Department of Radiology, Yunnan Cancer Hospital (The Third Affiliated Hospital of Kunming Medical University), Kunming 650118, China

Corresponding author: TAN J, E-mail: 2323338133@qq.com

Conflicts of interest   None.

Received  2024-08-04
Accepted  2024-11-10
DOI: 10.12015/issn.1674-8034.2025.01.006
Cite this article as: CHEN J, CHEN J Y, LI Z H, et al. The value of IVIM parameters in predicting synchronous liver metastasis of rectal cancer in tumor and mesorectal[J]. Chin J Magn Reson Imaging, 2025, 16(1): 36-41, 67. DOI:10.12015/issn.1674-8034.2025.01.006.

[1]
QIN H, WU Y Q, LIN P, et al. Ultrasound image-based radiomics: an innovative method to identify primary tumorous sources of liver metastases[J]. J Ultrasound Med, 2021, 40(6): 1229-1244. DOI: 10.1002/jum.15506.
[2]
LIU M L, SHEN F, LU J P. The predictive value of radiomics nomogram based on high-resolution T2WI for synchronous liver metastases in patients with rectal cancer[J]. Chin Comput Med Imag, 2021, 27(2): 121-126. DOI: 10.3969/j.issn.1006-5741.2021.02.008.
[3]
LV Y, FENG Q Y, WEI Y, et al. Benefits of multi-disciplinary treatment strategy on survival of patients with colorectal cancer liver metastasis[J/OL]. Clin Transl Med, 2020, 10(3): e121 [2024-11-10]. https://doi.org/10.1002/ctm2.121. DOI: 10.1002/ctm2.121.
[4]
SHEN Y, GUAN Z, LIANG H, et al. MRI-based radiomics signature to predict the response of chemotherapy in synchronous liver metastasis of colorectal carcinoma[J]. J Pract Radiol, 2023, 39(3): 416-419, 432. DOI: 10.3969/j.issn.1002-1671.2023.03.018.
[5]
LI Y, GONG J, SHEN X G, et al. Assessment of primary colorectal cancer CT radiomics to predict metachronous liver metastasis[J/OL]. Front Oncol, 2022, 12: 861892 [2024-11-10]. https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.861892/full. DOI: 10.3389/fonc.2022.861892.
[6]
GRANATA V, FUSCO R, DE MUZIO F, et al. Radiomics and machine learning analysis based on magnetic resonance imaging in the assessment of colorectal liver metastases growth pattern[J/OL]. Diagnostics, 2022, 12(5): 1115 [2024-11-10]. https://www.ncbi.nlm.nih.gov/pubmed/35626271. DOI: 10.3390/diagnostics12051115.
[7]
OHYA H, WATANABE J, SUWA H, et al. Near-infrared imaging using indocyanine green for laparoscopic lateral pelvic lymph node dissection for clinical stage Ⅱ/Ⅲ middle-lower rectal cancer: a propensity score-matched cohort study[J]. Dis Colon Rectum, 2022, 65(7): 885-893. DOI: 10.1097/DCR.0000000000002156.
[8]
DUMONT F, BARRETEAU T, SIMON G, et al. Laparoscopic extraperitoneal approach to bilateral pelvic lymph node dissection in low rectal cancer: technique with video and 3D modeling[J]. Ann Surg Oncol, 2022, 29(1): 109-111. DOI: 10.1245/s10434-021-10728-1.
[9]
JIA Y P, LI P P, WU R, et al. Research status of mesenteric lymph node imaging and pathologic localization[J]. Chin J Magn Reson Imag, 2022, 13(8): 162-165. DOI: 10.12015/issn.1674-8034.2022.08.037.
[10]
ZHANG T Q, WANG Q T, LI M Y, et al. Preoperative prediction of T-stage of rectal cancer based on IVIM multiparameter MRI radiomics[J]. Radiol Pract, 2022, 37(9): 1085-1091. DOI: 10.13609/j.cnki.1000-0313.2022.09.006.
[11]
ZHOU B J, ZHOU Y M, TANG Y B, et al. Intravoxel incoherent motion MRI for rectal cancer: correlation of diffusion and perfusion characteristics with clinical-pathologic factors[J]. Acta Radiol, 2023, 64(3): 898-906. DOI: 10.1177/02841851221100081.
[12]
DING X, SUN D Q, GUO Q C, et al. The value of diffusion kurtosis imaging and intravoxel incoherent motion quantitative parameters in predicting synchronous distant metastasis of rectal cancer[J/OL]. BMC Cancer, 2022, 22(1): 920 [2024-11-10]. https://doi.org/10.1186/s12885-022-10022-7. DOI: 10.1186/s12885-022-10022-7.
[13]
LU B L, CHEN Y, WEN Z Q, et al. Quantitative assessment of the microstructure of the mesorectum with different prognostic statuses by intravoxel incoherent motion diffusion-weighed magnetic resonance imaging[J/OL]. BMC Gastroenterol, 2022, 22(1): 481 [2024-8-21]. https://doi.org/10.1186/s12876-022-02555-9. DOI: 10.1186/s12876-022-02555-9.
[14]
Chinese College of Surgeons; Chinese Society of Gastrointestinal Surgery; Chinese Society of Colorectal Surgery, et al. Chinese guidelines for diagnosis and comprehensive treatment of colorectal liver metastases (2023 edition)[J]. Chin J Dig Surg, 2023, 22(1): 1-28. DOI: 10.3760/cma.j.cn115610-20221228-00762.
[15]
COFFEY J C, BYRNES K G, WALSH D J, et al. Update on the mesentery: structure, function, and role in disease[J]. Lancet Gastroenterol Hepatol, 2022, 7(1): 96-106. DOI: 10.1016/S2468-1253(21)00179-5.
[16]
COFFEY J C, O'LEARY D P. The mesentery: structure, function, and role in disease[J]. Lancet Gastroenterol Hepatol, 2016, 1(3): 238-247. DOI: 10.1016/S2468-1253(16)30026-7.
[17]
BÄUERLE T, SEYLER L, MÜNTER M, et al. Diffusion-weighted imaging in rectal carcinoma patients without and after chemoradiotherapy: a comparative study with histology[J]. Eur J Radiol, 2013, 82(3): 444-452. DOI: 10.1016/j.ejrad.2012.10.012.
[18]
MASSUCCO P, FONTANA A P, BALBO MUSSETTO A, et al. MRI-detected extramural vascular invasion (mrEMVI) as the best predictive factor to identify candidates to total neoadjuvant therapy in locally advanced rectal cancer[J]. J Surg Oncol, 2022, 125(6): 1024-1031. DOI: 10.1002/jso.26818.
[19]
PAUL S, ARYA S, MOKUL S, et al. Extramural vascular invasion as an independent prognostic marker in locally advanced rectal cancer: propensity score match pair analysis[J]. Abdom Radiol (NY), 2022, 47(11): 3671-3678. DOI: 10.1007/s00261-022-03608-z.
[20]
WANG J, GAO Q K, WANG Y F, et al. A new insights of mesorectum[J]. Chin J Gastrointest Surg, 2022, 25(4): 321-326. DOI: 10.3760/cma.j.cn441530-20220228-00068.
[21]
YANG X Y, LIU Y Y, CHEN Y, et al. Evaluation of mesorectal microcirculation with quantitative dynamic contrast-enhanced MRI[J]. AJR Am J Roentgenol, 2020, 215(6): 1370-1376. DOI: 10.2214/AJR.19.22116.
[22]
SONG H C, WANG J Z. Advances in resection margin of colorectal liver metastases[J]. Chin J Pract Surg, 2023, 43(2): 216-220, 225. DOI: 10.19538/j.cjps.issn1005-2208.2023.02.18.
[23]
QIU B X, SU X H, QIN X X, et al. Application of machine learning techniques in real-world research to predict the risk of liver metastasis in rectal cancer[J/OL]. Front Oncol, 2022, 12: 1065468 [2024-8-22]. https://doi.org/10.3389/fonc.2022.1065468. DOI: 10.3389/fonc.2022.1065468.
[24]
LI Z F, KANG L Q, LIU F H, et al. Radiomics based on preoperative rectal cancer MRI to predict the metachronous liver metastasis[J]. Abdom Radiol, 2023, 48(3): 833-843. DOI: 10.1007/s00261-022-03773-1.
[25]
MEYER H J, HÖHN A K, WOIDACKI K, et al. Associations between IVIM histogram parameters and histopathology in rectal cancer[J]. Magn Reson Imaging, 2021, 77: 21-27. DOI: 10.1016/j.mri.2020.12.008.
[26]
YUAN J, GONG Z G, LIU K, et al. Correlation between diffusion kurtosis and intravoxel incoherent motion derived (IVIM) parameters and tumor tissue composition in rectal cancer: a pilot study[J]. Abdom Radiol, 2022, 47(4): 1223-1231. DOI: 10.1007/s00261-022-03426-3.
[27]
WENG X Y, JIANG H, JI L B, et al. Comparison between IVIM parameters and ADC value in rectal cancer for estimate histological grade[J]. Chin J Magn Reson Imag, 2020, 11(7): 535-539. DOI: 10.12015/issn.1674-8034.2020.07.012.
[28]
LI H, CHEN G W, LIU Y S, et al. Assessment of histologic prognostic factors of resectable rectal cancer: comparison of diagnostic performance using various apparent diffusion coefficient parameters[J/OL]. Sci Rep, 2020, 10(1): 11554 [2022-06-05]. https://doi.org/10.1038/s41598-020-68328-0. DOI: 10.1038/s41598-020-68328-0.
[29]
WESDORP N J, VAN GOOR V J, KEMNA R, et al. Advanced image analytics predicting clinical outcomes in patients with colorectal liver metastases: A systematic review of the literature[J/OL]. Surg Oncol, 2021, 38: 101578 [2024-07-30]. https://doi.org/10.1016/j.suronc.2021.101578. DOI: 10.1016/j.suronc.2021.101578.
[30]
TAGHAVI M, TREBESCHI S, SIMÕES R, et al. Machine learning-based analysis of CT radiomics model for prediction of colorectal metachronous liver metastases[J]. Abdom Radiol, 2021, 46(1): 249-256. DOI: 10.1007/s00261-020-02624-1.
[31]
LIANG M, CAI Z T, ZHANG H M, et al. Machine learning-based analysis of rectal cancer MRI radiomics for prediction of metachronous liver metastasis[J]. Acad Radiol, 2019, 26(11): 1495-1504. DOI: 10.1016/j.acra.2018.12.019.

PREV Comparative study on the application of CT grayscale inversion and MRI in the MRF of rectal cancer
NEXT Amide proton transfer weighted and diffusion-weighted imaging in evaluating rectal cancer tumor budding grade value
  


LINK


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