Deep learning features based on 18F-FDG PET/CT to predict preoperative colorectal cancer lymph node metastasis

Background The objective of this study was to create and authenticate a prognostic model for lymph node metastasis in Colorectal cancer (CRC) that integrates clinical, radiomics, and deep transfer learning features. Materials and methods In this study, we analyzed data from 119 colorectal cancer (CRC) patients who underwent 18F-FDG PET/CT scanning. The patient cohort was divided into training and validation subsets at an 8:2 ratio, with an additional 33 external data points for testing. Initially, we conducted univariate analysis to screen clinical parameters. Radiomics features were extracted from manually drawn images using pyradiomics, and deep learning features, radiomics features, and clinical features were selected using LASSO regression and Spearman correlation coefficient. We then constructed a model by training a support vector machine (SVM), and evaluated the performance of the prediction model by comparing the area under the curve (AUC), sensitivity, and specificity. Finally, we developed nomograms combining clinical and radiological features for interpretation and analysis. Results The DLR Nomogram model, which was developed by integrating deep learning, radiomics, and clinical features, exhibited excellent performance. The area under the curve was (AUC = 0.934, 95% CI: 0.884-0.983) in the training cohort, (AUC = 0.902, 95% CI: 0.769-1.000) in the validation cohort, and (AUC = 0.836, 95% CI: 0.673-0.998) in the test cohort. Conclusion We developed a pre-operative predictive machine learning model using deep transfer learning, radiomics, and clinical features to differentiate LNM status in Colorectal cancer (CRC), aiding in treatment decision-making for patients.