Radiomic features based on MRI for prediction of lymphovascular invasion in rectal cancer

Abstract

Purpose To investigate the value of radiomics in predicting lymphovascular invasion (LVI) status of rectal cancer based on MRI. Materials and methods The retrospective study included 188 patients based on MRI with histologically confirmed rectal cancer and evaluated LVI status. Clinical factors and image data were collected, and radiomics features were extracted from multi-region (tumor and mesorectum) and single region (tumor), respectively, on T2WI and DWI. Spearman correlation analysis and the LASSO algorithm were used for radiomic feature extraction and selection; preliminarily selection of an optimal classifier by the results of the fivefold cross-validation performance in the six preselected specific machine learning classifier. Multi-regional and single-regional predictive models were both built and evaluated by calculating the area under the ROC curve (AUC) and corresponding accuracy, specificity, sensitivity, etc.Results A Ridge Classification model was constructed with 21 features (2 clinical features, 10 radiomics features from mesorectum region, and 9 radiomics features from tumor region) selected by Spearman correlation and LASSO analysis. The multi-regional model shows a good performance in the differentiation of the status of LVI in training data sets (AUC = 0.87, accuracy = 0.79). The model was further validated in the testing data sets, giving an AUC and an accuracy of 0.74 and 0.68, respectively. Furthermore, the performance of single-regional model (AUC = 0.72, accuracy = 0.67) is lower compared to the values given by the multi-regional model. Conclusion The radiomics model which we developed demonstrates that multi-regional radiomics features based on multiparametric MRI are useful for preoperatively predicting lymphovascular invasion in patients with rectal cancer. KeywordsRectal cancer · Machine learning · Radiomics · Lymphovascular · MRI Abbreviations AUC Area under the ROC curve EMVI Extramural venous invasion LASSO Least absolute shrinkage and selection operator LVI Lymphovascular invasion MRF Mesorectal fascia ROC Receiver-operating characteristic

Correlation Analysis of Gene and Radiomic Features in Colorectal Cancer Liver Metastases

“…With the continuous advancement of modern technology, radiomics has been widely studied in medicine [ 39 ]. For example, Jia et al studied the correlation between the radiomic features of lung cancer and its genetic features [ 40 ].…”