Share this content in WeChat
Clinical Article
Predictive value of MRI T2WI texture analysis for lymph node metastasis in rectal cancer
LI Guoqiang  KE Weiwei  SUN Xianglin  WEI Yuze  LU Zaiming 

Cite this article as: Li GQ, Ke WW, Sun XL, et al. Predictive value of MRI T2WI texture analysis for lymph node metastasis in rectal cancer[J]. Chin J Magn Reson Imaging, 2022, 13(7): 42-47. DOI:10.12015/issn.1674-8034.2022.07.008.

[Abstract] Objective To construct a prediction model based on T2WI texture features and clinical indicators to predict preoperative lymph node metastasis before rectal cancer.Materials and Methods This study retrospectively analyzed T2WI images, serum tumor markers and basic clinical data of 112 patients who underwent radical resection and lymph node dissection of rectal cancer because of pathological diagnosis of rectal cancer. All patients were randomly divided into training group and validation group with a ratio of 7∶3 to train and validate prediction models, respectively. Region of interest (ROI) of rectal cancer lesions and target lymph nodes were manually delineated on T2WI images. The texture parameters used to identify lymph node metastasis were automatically extracted using artificial intelligence software logistic regression analyses were used to construct two prediction models based on tumor tissue texture parameters and target lymph node texture parameters, a clinical prediction model based on patient clinical indicators, and a combined prediction model combining texture parameters and clinical indicators, respectively. The area under the receiver operating characteristic (AUCs) curves were used to evaluate the diagnostic performances of different models. The DeLong tests were used to compare the AUC differences between prediction models. The net clinical benefit of each prediction model was evaluated by decision curve analysis (DCA). Statistical significance was set at P<0.05.Results Four hundred and one texture features were extracted from the T2WI images of each ROI. After screening, 7 texture parameters of tumor tissue and 6 texture parameters of the target lymph node were selected for model building. The AUC of the target lymph node texture analysis prediction model in the training group was 0.881, with a sensitivity of 86.67% and a specificity of 81.25%; the AUC of the validation group was 0.795, with a sensitivity of 92.31% and specificity of 66.67%. The AUC of the tumor tissue texture analysis prediction model in the training group was 0.844, with a sensitivity of 80.00% and a specificity of 79.17%; the AUC of the validation group was 0.897, with a sensitivity of 84.62% and a specificity of 90.48%. The combined prediction model constructed by combining texture parameters, the ratio of short to long diameter of the target lymph nodes and the serum CA19-9 level of the patients gets the best performance among the models (AUC of the training group was 0.978 with the sensitivity and specificity were 93.33% and 91.67%, respectively, and the AUC of the validation group was 0.897 with the sensitivity was 84.62%, the specificity was 90.48%, P<0.05).Conclusions The texture features of rectal T2WI images combined with clinical indexes can be used to construct an effective model for predicting lymph node metastasis and provide help for clinical individualized treatment.
[Keywords] rectal cancer;magnetic resonance imaging;texture analysis;lymph node metastasis;prediction

LI Guoqiang   KE Weiwei   SUN Xianglin   WEI Yuze   LU Zaiming*  

Department of Radiology, Shengjing Hospital of China Medical University, Shenyang 110000, China

Lu ZM, E-mail:

Conflicts of interest   None.

ACKNOWLEDGMENTS National Natural Science Foundation of China (No. 81771947).
Received  2022-02-08
Accepted  2022-06-24
DOI: 10.12015/issn.1674-8034.2022.07.008
Cite this article as: Li GQ, Ke WW, Sun XL, et al. Predictive value of MRI T2WI texture analysis for lymph node metastasis in rectal cancer[J]. Chin J Magn Reson Imaging, 2022, 13(7): 42-47. DOI:10.12015/issn.1674-8034.2022.07.008.

Stoffel EM, Murphy CC. Epidemiology and mechanisms of the increasing incidence of colon and rectal cancers in young adults[J]. Gastroenterology, 2020, 158(2): 341-353. DOI: 10.1053/j.gastro.2019.07.055.
Horvat N, Carlos Tavares Rocha C, Clemente Oliveira B, et al. MRI of rectal cancer: tumor staging, imaging techniques, and management[J]. Radiographics, 2019, 39(2): 367-387. DOI: 10.1148/rg.2019180114.
Nakanishi R, Akiyoshi T, Toda S, et al. Radiomics approach outperforms diameter criteria for predicting pathological lateral lymph node metastasis after neoadjuvant (chemo)radiotherapy in advanced low rectal cancer[J]. Ann Surg Oncol, 2020, 27(11): 4273-4283. DOI: 10.1245/s10434-020-08974-w.
Benson AB, Venook AP, Al-Hawary MM, et al. NCCN guidelines insights: rectal cancer, version 6.2020[J]. J Natl Compr Canc Netw, 2020, 18(7): 806-815. DOI: 10.6004/jnccn.2020.0032.
Glynne-Jones R, Wyrwicz L, Tiret E, et al. Rectal cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up[J]. Ann Oncol, 2017, 28(suppl_4): iv22-iv40. DOI: 10.1093/annonc/mdx224.
National Health Commission of the People's Republic of China. Chinese protocol of diagnosis and treatment of colorectal cancer (2020 edition)[J]. Zhonghua Wai Ke Za Zhi, 2020, 58(8): 561-585. DOI: 10.3760/cma.j.cn112139-20200518-00390.
Peacock O, Chang GJ. The landmark series: management of lateral lymph nodes in locally advanced rectal cancer[J]. Ann Surg Oncol, 2020, 27(8): 2723-2731. DOI: 10.1245/s10434-020-08639-8.
Xu HS, Zhao WY, Guo WB, et al. Prediction model combining clinical and MR data for diagnosis of lymph node metastasis in patients with rectal cancer[J]. J Magn Reson Imaging, 2021, 53(3): 874-883. DOI: 10.1002/jmri.27369.
Wilson R, Devaraj A. Radiomics of pulmonary nodules and lung cancer[J]. Transl Lung Cancer Res, 2017, 6(1): 86-91. DOI: 10.21037/tlcr.2017.01.04.
Liu YJ, Fan HJ, Dong D, et al. Computed tomography-based radiomic model at node level for the prediction of normal-sized lymph node metastasis in cervical cancer[J]. Transl Oncol, 2021, 14(8): 101113. DOI: 10.1016/j.tranon.2021.101113.
Li ML, Zhang J, Dan YB, et al. Development and validation of a clinical-radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer[J]. China Oncol, 2020, 30(1): 49-56. DOI: 10.19401/j.cnki.1007-3639.2020.01.006.
Yu DD, Wang M, Xu LP, et al. Clinical value of 3T MRI for the preoperative staging of rectal cancer[J]. Chin J Surg Oncol, 2021, 13(5): 454-457. DOI: 10.3969/j.issn.1674-4136.2021.05.008.
Gu HW, Zheng XB, Lu MJ. Research of the regional lymph node metastasis in rectal cancer DWI combined with DCE-MRI[J]. J Med Theory Pract, 2021, 34(20): 3503-3505, 3531. DOI: 10.19381/j.issn.1001-7585.2021.20.004.
Yang LQ, Liu D, Fang X, et al. Rectal cancer: can T2WI histogram of the primary tumor help predict the existence of lymph node metastasis?[J]. Eur Radiol, 2019, 29(12): 6469-6476. DOI: 10.1007/s00330-019-06328-z.
Shayesteh SP, Alikhassi A, Farhan F, et al. Prediction of response to neoadjuvant chemoradiotherapy by MRI-based machine learning texture analysis in rectal cancer patients[J]. J Gastrointest Cancer, 2020, 51(2): 601-609. DOI: 10.1007/s12029-019-00291-0.
Lubner MG, Smith AD, Sandrasegaran K, et al. CT texture analysis: definitions, applications, biologic correlates, and challenges[J]. Radiographics, 2017, 37(5): 1483-1503. DOI: 10.1148/rg.2017170056.
Sun XL, You YN, Zhao XX, et al. Prediction of the early recurrence of HCC patients after TACE surgery based on T2 weighted image texture analysis[J]. Chin J Magn Reson Imaging, 2021, 12(8): 22-26, 32. DOI: 10.12015/issn.1674-8034.2021.08.005.
Stojkovic Lalosevic M, Stankovic S, Stojkovic M, et al. Can preoperative CEA and CA19-9 serum concentrations suggest metastatic disease in colorectal cancer patients?[J]. Hell J Nucl Med, 2017, 20(1): 41-45. DOI: 10.1967/s002449910505.
Han WB, Jia SH. Levels of serum CA19-9, PIVKA-Ⅱand VCAM-1 and clinical value of prognosis in patients with radical surgery of pancreatic cancer[J]. Pract J Cancer, 2020, 35(11): 1821-1825. DOI: 10.3969/j.issn.1001-5930.2020.11.021.
Xu WN, Wang T. Evaluation value of serum CEA, CA724 and CA199 levels in patients with pancreatic cancer[J]. Pract J Cancer, 2019, 34(5): 747-749. DOI: 10.3969/j.issn.1001-5930.2019.05.015.
Chen DY, Wu XB, Xia M, et al. Upregulated exosomic miR-23b-3p plays regulatory roles in the progression of pancreatic cancer[J]. Oncol Rep, 2017, 38(4): 2182-2188. DOI: 10.3892/or.2017.5919.
Li Y, Zhang K, Wang XP, et al. Texture analysis of preoperative CT images for prediction of lymph node metastasis: a preliminary study in patients with rectal cancer[J]. J Clin Radiol, 2021, 40(5): 930-934. DOI: 10.13437/j.cnki.jcr.2021.05.021.
Macherla S, Laks S, Naqash AR, et al. Emerging role of immune checkpoint blockade in pancreatic cancer[J]. Int J Mol Sci, 2018, 19(11): E3505. DOI: 10.3390/ijms19113505.

PREV The reproducibility of liver stiffness from magnetic resonance elastography under confounding factors in patients with chronic liver disease
NEXT Clinical application of whole-volume apparent diffusion coefficient histogram parameters of histological grading rectal adenocarcinoma

Tel & Fax: +8610-67113815    E-mail: