Preoperative prediction of lymphatic matastasis of mesolow colorectal cancer by endorectal ultrasound and elastography-based radiomics model / 中华超声影像学杂志

ABSTRACT

Objective:To investigate whether radiomics based on ultrasound images can predict lym-phatic metastasis of rectal cancer before surgery.Methods:A total of 80 patients with rectal cancer who underwent endorectal ultrasound (TRUS) and endorectal elastography were confirmed by postoperative pathology in Zhejiang Cancer Hospital from January 2016 to December 2019 were retrospectively analyzed. The general characteristics (gender, age, tumor size, depth of tumor infiltration, tumor location, carcinoembryonic antigen, glycoantigen 199) of the lymph node metastasis group ( n=27) and the non-metastasis group ( n=53) were compared, and the clinical risk factors with statistically significant differences were screened out. The tumor maximum sagittal 2D TRUS images and endorectal elastography were manually outlined, and the radiomics features were extracted using the open source software pyradiomics 3.0.1, and the filtering and embedding methods were used to reduce the dimensionality of the data to select the important features and obtain the best parameters of the model. Then all samples were randomly divided into training and validation sets in the ratio of 8∶2, the models were trained using the best model parameters, which were tested and validated in the validation set, and the predictive efficacy of different models was evaluated according to the ROC curve. Results:The depth of tumor infiltration was statistically significant in predicting whether the lymph nodes metastasized or not (χ 2=11.555, P<0.05), and its area under ROC curve(AUC) value was 0.699. A total of 1 710 features were extracted from sagittal 2D TRUS images and endorectal elastography. After pre-processing and screening, 10 features were strongly correlated with lymph node metastasis status. The 10 features were used to construct the prediction models with AUC values of 0.703, 0.726 and 0.742 for the Logistic Regression Model, Random Forest Model and Support Vector Machine Model, respectively. And the AUC value of the ensemble averaging model in the validation set was 0.734. The imaging-omics prediction model outperformed the prediction model based on statistical analysis of clinical data (AUC 0.734 vs 0.699, Z=1.984), with a statistically significant difference ( P<0.05). Conclusions:The endorectal ultrasound and endorectal elastography-based radiomics model constructed in this study is better than the model constructed based on statistical analysis of clinical data only, and it is valuable for preoperative lymph node metastasis prediction in rectal cancer.