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The value of preoperative prediction of tumor budding grade in stage T3-T4 rectal cancer based on abdominal CT body composition analysis combined with diffusion kurtosis imaging technique
CHEN Anliang  ZHANG Qinhe  LI Wenhao  XIE Suling  WANG Xiulin  WANG Hongkai  SUN Yinan  WEI Qiang  LIU Ailian 

Cite this article as: CHEN A L, ZHANG Q H, LI W H, et al. The value of preoperative prediction of tumor budding grade in stage T3-T4 rectal cancer based on abdominal CT body composition analysis combined with diffusion kurtosis imaging technique[J]. Chin J Magn Reson Imaging, 2026, 17(9): 16-24. DOI:10.12015/issn.1674-8034.2026.09.003.


[Abstract] Objective This study aims to investigate the value of abdominal CT body composition analysis combined with diffusion kurtosis imaging (DKI) for the preoperative prediction of tumor budding (TB) grade in patients with T3 to T4 rectal cancer.Materials and Methods A retrospective analysis was conducted on data from 89 patients diagnosed with T3 to T4 rectal adenocarcinoma, confirmed by postoperative pathology. All patients underwent pelvic 3.0 T MRI and whole abdominal CT examinations prior to surgery. Based on the pathological results, the TB grade was categorized into two groups: a low-intermediate grade TB group (52 cases) and a high grade TB group (37 cases). Two observers measured the quantitative parameters of DKI in the lesions of both groups, including fractional anisotropy (FA) value, mean diffusivity (MD) value, and mean kurtosis (MK) value. The TotalSegmentator tool was utilized to automatically segment subcutaneous fat, visceral fat, and muscle at the L3 level of the abdominal CT scans. Python software facilitated the automatic calculation of subcutaneous fat area (SFA), visceral fat area (VFA), and muscle area (MA). The ratios of VFA to SFA (VFA/SFA), SFA to standard body weight (SBW) (SFA/SBW), VFA to SBW (VFA/SBW), MA to SBW (MA/SBW), and the difference between VFA and SFA (VFA-SFA) were calculated. The consistency of DKI parameters assessed by two observers was evaluated using the intra-class correlation coefficient (ICC). To examine the differences in DKI parameters and body composition indicators across the groups, either a Mann-Whitney U test or an independent sample t-test was utilized. A multivariate logistic regression analysis was performed to determine independent risk factors, which facilitated the creation of a combined prediction model based on these factors. The diagnostic performance of each individual risk factor, as well as the combined prediction model, was evaluated through the receiver operating characteristic (ROC) curve. To compare the diagnostic efficacy between the individual variables and the combined model, the DeLong test was employed, whereas the McNemar test was used to assess differences in sensitivity and specificity.Results The consistency of the DKI quantitative parameters measured by the two observers was good, with ICC values exceeding 0.75. In the high grade TB group, the MK, VFA, and VFA/SBW values were 0.938 (0.891, 1.019), [150.620 (107.685, 199.840)] cm2, and 2.490 (1.730, 3.210), respectively, significantly surpassing those in the low-intermediate grade TB group, which had values of 0.776 (0.685, 0.878), [117.205 (72.067, 169.762)] cm2, and 2.115 (1.242, 2.650). The MD value in the high grade TB group was (1.113 ± 0.105) μm2/ms, which was lower than that in the low-intermediate grade TB group (1.347 ± 0.257) μm²/ms. The differences between the two groups for all the aforementioned parameters were statistically significant (P < 0.05). The results of multivariate logistic regression analysis indicated that the MK value [odds ratio: 4.104, 95% confidence interval (CI): 2.223 to 7.578] and the VFA/SBW value [odds ratio: 2.335 (95% CI: 1.300 to 4.195)] were independent risk predictors for TB grade (P < 0.05). The area under the curve (AUC) for predicting TB grade based on the MK and VFA/SBW values, as well as their combined prediction model, were 0.838, 0.627, and 0.886, respectively. The sensitivity of these models was 91.89%, 72.97%, and 81.08%, respectively, while the specificity was 67.31%, 50.00%, and 80.77%, respectively. The diagnostic efficacy of the combined prediction model was superior to that of VFA/SBW alone, and its specificity exceeded that of both MK and VFA/SBW, with statistically significant differences (P < 0.05).Conclusions For patients with T3 to T4 rectal cancer, DKI has been shown to effectively predict TB grade. Additionally, body composition analysis serves to preliminarily stratify the risk of TB. The integration of these two modalities significantly enhances the accuracy of predicting TB grade, thereby providing more comprehensive insights into the interplay between host factors and local tumor characteristics for preoperative TB risk assessment.
[Keywords] rectal cancer；tumor budding；body composition analysis；quantitative computed tomography；magnetic resonance imaging；diffusion kurtosis imaging

CHEN Anliang1, 2, 3   ZHANG Qinhe1, 2, 3   LI Wenhao1   XIE Suling4   WANG Xiulin5   WANG Hongkai2, 6   SUN Yinan6   WEI Qiang1   LIU Ailian1, 2, 3*  

1 Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian 116011, China

2 Dalian Engineering Research Center for Artificial Intelligence in Medical Imaging, Dalian 116011, China

3 Technology Innovation Center of Hyperpolarized MRI, Liaoning Province, Dalian 116011, China

4 Department of Pathology, the First Affiliated Hospital of Dalian Medical University, Dalian 116011, China

5 Stem Cell Clinical Research Institute of the First Affiliated Hospital of Dalian Medical University, Dalian 116011, China

6 Faculty of Medicine, Dalian University of Technology, Dalian 116011, China

Corresponding author: LIU A L, E-mail: cjr.liuailian@vip.163.com

Conflicts of interest   None.

Received  2025-12-04
Accepted  2026-03-15
DOI: 10.12015/issn.1674-8034.2026.09.003
Cite this article as: CHEN A L, ZHANG Q H, LI W H, et al. The value of preoperative prediction of tumor budding grade in stage T3-T4 rectal cancer based on abdominal CT body composition analysis combined with diffusion kurtosis imaging technique[J]. Chin J Magn Reson Imaging, 2026, 17(9): 16-24. DOI:10.12015/issn.1674-8034.2026.09.003.

[1]
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.
[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]
LUGLI A, ZLOBEC I, BERGER M D, et al. Tumour budding in solid cancers[J]. Nat Rev Clin Oncol, 2021, 18(2): 101-115. DOI: 10.1038/s41571-020-0422-y.
[4]
National Health Commission of the People's Republic of China, Chinese Society of Oncology. Chinese protocol of diagnosis and treatment of colorectal cancer (2023 edition)[J]. Chin J Surg, 2023, 61(8): 617-644. DOI: 10.3760/cma.j.cn112139-20230603-00222.
[5]
RAN X, CHEN Y, LIU C X, et al. Differential effect of tumor budding on the benefit of adjuvant chemotherapy in stage II colorectal cancer: a retrospective observational study[J]. J Gastrointest Oncol, 2024, 15(4): 1545-1555. DOI: 10.21037/jgo-24-278.
[6]
RAFIEE A, NASRI P, MORADI A, et al. Tumor budding as an indicator of prognosis in locally advanced rectal cancer after neoadjuvant chemoradiotherapy: a systematic review and meta-analysis[J/OL]. Front Oncol, 2025, 15: 1429319 [2025-12-15]. https://pubmed.ncbi.nlm.nih.gov/40270611/. DOI: 10.3389/fonc.2025.1429319.
[7]
WEN X Y, ZEE S Y, SHROYER K R, et al. Intratumoral budding and tumor microenvironment in pretreatment rectal cancer biopsies predict the response to neoadjuvant chemoradiotherapy[J]. Appl Immunohistochem Mol Morphol, 2022, 30(1): 1-7. DOI: 10.1097/PAI.0000000000000966.
[8]
PAKVISAL N, PAPPAS L, CAUGHEY B A, et al. Personalized treatment modalities for rectal cancer: advances in neoadjuvant treatment[J/OL]. J Clin Med, 2025, 14(13): 4411 [2026-02-23]. https://pubmed.ncbi.nlm.nih.gov/40648785/. DOI: 10.3390/jcm14134411.
[9]
HUBER F A, DEL GRANDE F, RIZZO S, et al. MRI in the assessment of adipose tissues and muscle composition: how to use it[J]. Quant Imaging Med Surg, 2020, 10(8): 1636-1649. DOI: 10.21037/qims.2020.02.06.
[10]
SIMONI P, GUGLIELMI R, APARISI GÓMEZ M P. Imaging of body composition in children[J]. Quant Imaging Med Surg, 2020, 10(8): 1661-1671. DOI: 10.21037/qims.2020.04.06.
[11]
DIETZ M V, POPURI K, JANSSEN L, et al. Evaluation of a fully automated computed tomography image segmentation method for fast and accurate body composition measurements[J/OL]. Nutrition, 2025, 129: 112592 [2025-12-15]. https://pubmed.ncbi.nlm.nih.gov/39442384/. DOI: 10.1016/j.nut.2024.112592.
[12]
ZHANG Y F, SHI J, XU Y N. Current applications and prospects of visceral fat in obesity diagnosis and comorbidity prediction[J]. J Diagn Concepts Pract, 2025, 24(1): 7-13. DOI: 10.16150/j.1671-2870.2025.01.002.
[13]
ZHU H, LIU G, ZHANG Y, et al. Analysis of the predictive value of VFA-based conventional CT examination combined with MSCTA on the efficacy of patients undergoing radical colorectal cancer surgery[J]. Chin J Biomed Eng, 2023, 29(3): 319-323. DOI: 10.3760/cma.j.cn115668-20230324-00056.
[14]
WANG H T, ZHANG J Z, LI Y. Comparison of surgical results, complications and prognosis of colorectal cancer patients with different visceral obesity indexes[J]. J Mod Oncol, 2023, 31(2): 294-299. DOI: 10.3969/j.issn.1672-4992.2023.02.018.
[15]
ZHAO L X, WANG C X, CHEN Y F, et al. Effects of visceral obesity on early complications after laparoscopic radical resection for rectal cancer[J/OL]. Chin J Oper Proced Gen Surg Electron Ed, 2025, 19(1): 35-39 [2025-12-15]. https://rs.yiigle.com/cmaid/1570533. DOI: 10.3877/cma.j.issn.1674-3946.2025.01.011.
[16]
ZHOU Z, SHEN F, LU H D, et al. The value of dynamic contrast-enhanced magnetic resonance imaging for preoperative evaluation of the tumor budding of rectal cancer[J]. Chin Comput Med Imaging, 2022, 28(4): 379-384. DOI: 10.3969/j.issn.1006-5741.2022.04.010.
[17]
ZHU L P, ZHU Y M, DONG Y Y, et al. Application value of magnetic resonance IVIM in preoperative evaluation of tumor budding of rectal cancer[J]. Chin J CT MRI, 2025, 23(2): 175-177. DOI: 10.3969/j.issn.1672-5131.2025.02.054.
[18]
CHEN A L, XIE S L, WANG Y, et al. The value of quantitative parameters of diffusion kurtosis imaging in preoperative prediction of tumor budding grade of rectal cancer[J]. Chin J Magn Reson Imaging, 2025, 16(2): 59-64, 99. DOI: 10.12015/issn.1674-8034.2025.02.009.
[19]
DU Y X, XU W X, HE Y Q, et al. Amide proton transfer weighted and diffusion-weighted imaging in evaluating rectal cancer tumor budding grade value[J]. Chin J Magn Reson Imaging, 2025, 16(1): 42-47, 73. DOI: 10.12015/issn.1674-8034.2025.01.007.
[20]
XIE P Y, HUANG Q T, ZHENG L T, et al. Sub-region based histogram analysis of amide proton transfer-weighted MRI for predicting tumor budding grade in rectal adenocarcinoma: a prospective study[J]. Eur Radiol, 2025, 35(3): 1382-1393. DOI: 10.1007/s00330-024-11172-x.
[21]
LI Z H, CHEN F Y, ZHANG S T, et al. The feasibility of MRI-based radiomics model in presurgical evaluation of tumor budding in locally advanced rectal cancer[J]. Abdom Radiol (NY), 2022, 47(1): 56-65. DOI: 10.1007/s00261-021-03311-5.
[22]
QU X T, ZHANG L, JI W N, et al. Preoperative prediction of tumor budding in rectal cancer using multiple machine learning algorithms based on MRI T2WI radiomics[J/OL]. Front Oncol, 2023, 13: 1267838 [2025-12-15]. https://pubmed.ncbi.nlm.nih.gov/37941552/. DOI: 10.3389/fonc.2023.1267838.
[23]
PENG L, WANG D Q, ZHUANG Z J, et al. Preoperative noninvasive evaluation of tumor budding in rectal cancer using multiparameter MRI radiomics[J]. Acad Radiol, 2024, 31(6): 2334-2345. DOI: 10.1016/j.acra.2023.11.023.
[24]
LIU Z Y, JIA J Y, BAI F, et al. Predicting rectal cancer tumor budding grading based on MRI and CT with multimodal deep transfer learning: a dual-center study[J/OL]. Heliyon, 2024, 10(7): e28769 [2025-12-15]. https://pubmed.ncbi.nlm.nih.gov/38590908/. DOI: 10.1016/j.heliyon.2024.e28769.
[25]
JIA J Y, KANG Y, WANG J H, et al. Attention mechanism-based multi-parametric MRI ensemble model for predicting tumor budding grade in rectal cancer patients[J]. Abdom Radiol (NY), 2025, 50(10): 4483-4494. DOI: 10.1007/s00261-025-04886-z.
[26]
LIU Z H, YANG H, NIE L, et al. Prediction of tumor budding grading in rectal cancer using a multiparametric MRI radiomics combined with a 3D vision transformer deep learning approach[J]. Acad Radiol, 2025, 32(8): 4512-4523. DOI: 10.1016/j.acra.2025.03.046.
[27]
JENSEN J H, HELPERN J A, RAMANI A, et al. Diffusional kurtosis imaging: the quantification of non-Gaussian water diffusion by means of magnetic resonance imaging[J]. Magn Reson Med, 2005, 53(6): 1432-1440. DOI: 10.1002/mrm.20508.
[28]
JU Y, NIE J, TIAN S F, et al. The value of diffusional kurtosis imaging in differentiating uterine sarcoma from degenerative hysteromyoma[J]. Chin J Magn Reson Imaging, 2021, 12(10): 61-65. DOI: 10.12015/issn.1674-8034.2021.10.014.
[29]
LI X W, TIAN S F, MA C J, et al. Multimodal MRI for estimating her-2 gene expression in endometrial cancer[J/OL]. Bioengineering (Basel), 2023, 10(12): 1399 [2025-12-15]. https://pubmed.ncbi.nlm.nih.gov/38135990/. DOI: 10.3390/bioengineering10121399.
[30]
WANG L L, LEI J K, LI S H, et al. Microsatellite instability of rectal cancer based on magnetic resonance diffusion kurtosis imaging[J]. Chin J Magn Reson Imaging, 2023, 14(8): 73-78. DOI: 10.12015/issn.1674-8034.2023.08.012.
[31]
MA Q, LIU Z L, ZHANG J D, et al. Multi-task reconstruction network for synthetic diffusion kurtosis imaging: Predicting neoadjuvant chemoradiotherapy response in locally advanced rectal cancer[J/OL]. Eur J Radiol, 2024, 174: 111402 [2025-12-15]. https://pubmed.ncbi.nlm.nih.gov/38461737/. DOI: 10.1016/j.ejrad.2024.111402.
[32]
ZHOU M, CHEN M Y, LUO M F, et al. Pathological prognostic factors of rectal cancer based on diffusion-weighted imaging, intravoxel incoherent motion, and diffusion kurtosis imaging[J]. Eur Radiol, 2025, 35(2): 979-988. DOI: 10.1007/s00330-024-11025-7.
[33]
LUGLI A, KIRSCH R, AJIOKA Y, et al. Recommendations for reporting tumor budding in colorectal cancer based on the International Tumor Budding Consensus Conference (ITBCC) 2016[J]. Mod Pathol, 2017, 30(9): 1299-1311. DOI: 10.1038/modpathol.2017.46.
[34]
HASSAWI B AL. Pathological and clinical significance of tumor budding and its association with epithelial-mesenchymal transition in colorectal carcinoma: a retrospective observational study[J]. Iran J Med Sci, 2025, 50(11): 762-774. DOI: 10.30476/ijms.2025.104785.3833.
[35]
KARJULA T, KEMI N, NISKAKANGAS A, et al. The prognostic role of tumor budding and tumor-stroma ratio in pulmonary metastasis of colorectal carcinoma[J]. Eur J Surg Oncol, 2023, 49(7): 1298-1306. DOI: 10.1016/j.ejso.2023.02.009.
[36]
Chinese Society for Therapeutic Radiation Oncology, Radiation Oncology Treatment Physician Branch of Chinese Medical Doctor Association, Professional Committee of Radiotherapy for Cancer of Chinese Anti-cancer Association. Expert consensus on neoadjuvant therapy for locally advanced rectal cancer (2025 edition)[J]. Chin J Radiat Oncol, 2026, 35(2): 121-134. DOI: 10.3760/cma.j.cn113030-20250728-00281.
[37]
SHIN J K, PARK Y A, HUH J W, et al. Tumor budding as a prognostic marker in rectal cancer patients on propensity score analysis[J]. Ann Surg Oncol, 2021, 28(13): 8813-8822. DOI: 10.1245/s10434-021-10286-6.
[38]
LI J H, MA Y L, WEN L, et al. Prognostic impact of tumor budding in rectal cancer after neoadjuvant therapy: a systematic review and meta-analysis[J/OL]. Syst Rev, 2024, 13(1): 22 [2025-12-15]. https://pubmed.ncbi.nlm.nih.gov/38191437/. DOI: 10.1186/s13643-023-02441-9.
[39]
LIU S L, ZHANG Y J, JU Y H, et al. Establishment and clinical application of an artificial intelligence diagnostic platform for identifying rectal cancer tumor budding[J/OL]. Front Oncol, 2021, 11: 626626 [2025-12-15]. https://pubmed.ncbi.nlm.nih.gov/33763362/. DOI: 10.3389/fonc.2021.626626.
[40]
DONG W, LIU A L, CHEN A L, et al. Comparison of amide proton transfer-weighted and diffusion kurtosis imaging in quantifying rectal cancer with and without chemotherapy[J]. Chin J Med Imaging, 2022, 30(1): 54-59. DOI: 10.3969/j.issn.1005-5185.2022.01.012.
[41]
FERRAZZOLI V, MINOSSE S, PICCHI E, et al. Multiparametric MRI in primary cerebral lymphoma: Correlation between diffusion kurtosis imaging (DKI), dynamic contrast enhanced (DCE) and dynamic Susceptibility contrast (DSC) MRI techniques[J/OL]. Phys Med, 2025, 129: 104864 [2025-12-15]. https://pubmed.ncbi.nlm.nih.gov/39631134/. DOI: 10.1016/j.ejmp.2024.104864.
[42]
LIU X, ZHANG Y, CHEN F, et al. Preliminary research of the classification of the brain acute stroke lesions by the Diffusion Kurtosis Imaging (DKI) and Diffusion Weighted Imaging (DWI) parameters[J]. Technol Health Care, 2023, 31(S1): 525-532. DOI: 10.3233/THC-236046.
[43]
QIAN L, ZHANG J G, LU S M, et al. Potential key roles of tumour budding: a representative malignant pathological feature of non-small cell lung cancer and a sensitive indicator of prognosis[J/OL]. BMJ Open, 2022, 12(3): e054009 [2025-12-15]. https://pubmed.ncbi.nlm.nih.gov/35361643/. DOI: 10.1136/bmjopen-2021-054009.
[44]
XU Y, ZHU Q. Histone modifications represent a key epigenetic feature of epithelial-to-mesenchyme transition in pancreatic cancer[J/OL]. Int J Mol Sci, 2023, 24(5): 4820 [2025-12-15]. https://pubmed.ncbi.nlm.nih.gov/36902253/. DOI: 10.3390/ijms24054820.
[45]
XIE Y Q, WANG X, WANG W Q, et al. Epithelial-mesenchymal transition orchestrates tumor microenvironment: current perceptions and challenges[J/OL]. J Transl Med, 2025, 23(1): 386 [2025-12-15]. https://pubmed.ncbi.nlm.nih.gov/40176117/. DOI: 10.1186/s12967-025-06422-5.
[46]
LARSSON S C, SPYROU N, MANTZOROS C S. Body fatness associations with cancer: evidence from recent epidemiological studies and future directions[J/OL]. Metabolism, 2022, 137: 155326 [2025-12-15]. https://pubmed.ncbi.nlm.nih.gov/36191637/. DOI: 10.1016/j.metabol.2022.155326.
[47]
ULRICH C M, HIMBERT C, HOLOWATYJ A N, et al. Energy balance and gastrointestinal cancer: risk, interventions, outcomes and mechanisms[J]. Nat Rev Gastroenterol Hepatol, 2018, 15(11): 683-698. DOI: 10.1038/s41575-018-0053-2.

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