CT-Based Thyroid Cancer Diagnosis using Deep Learning and Radiomics Fusion Method

PurposeThe aim of this study was to address the limitations observed in past research, particularly the limited accuracy of individual deep learning or radiomics methods in small datasets. By developing a fusion approach that integrates the two techniques, we hypothesized that the performance in CT-based thyroid cancer diagnosis could be improved. Materials and Methods Eighty-five patients with thyroid tumors (58 malignant, 27 benign) who underwent CT scans were included in this study. The dataset was divided into training (70%) and testing (30%). A shallow CNN model, including five convolutional layers and two fully connected layers, was developed for tumor classification. Radiomics features were extracted and selected using the pyradiomics package and statistical tests (T-test, etc.). These features were then utilized to develop a Multiple Logistic Regression (MLR) model for tumor classification. The CNN and MLR models were combined using a fusion method that calculates the weighted sum of each diagnostic output for classification. The accuracy of the diagnostic methods was evaluated for both the individual and combined fusion models. The statistical significance of the weighted combination model was examined using the Wilcoxon-Test. Results The CNN model achieved an accuracy of 82.713%, and the MLR model achieved an accuracy of 76.596%. The accuracy of the fusion model reached 85.372%, suggested the improvement of performance of the fusion approach over the individual models. The Wilcoxon-Test yielded a W-Statistic of 19410.0 and a p-value of 〖2.96×10〗^(-14), which is below the threshold of 0.05. Conclusion A fusion model combining deep learning and radiomics methods was developed and showed improved accuracy in thyroid tumor diagnosis in a small dataset. The results showed a statistically significant difference between the fusion model and the individual models.