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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: luzm@sj-hospital.org

Conflicts of interest   None.

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

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