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Improving Prognostic Performance in Resectable Pancreatic Ductal Adenocarcinoma using Radiomics and Deep Learning Features Fusion in CT Images
It was shown that the proposed feature fusion method significantly improves the prognosis performance for overall survival in resectable PDAC cohorts, elevating the area under ROC curve by 51% compared to predefined radiomics features alone, by 16% compared to deep learning features alone, and by 32% compared to existing feature fusion and reduction methods for a combination of deep learning and predefined radiomics features.
CNN-based Survival Model for Pancreatic Ductal Adenocarcinoma in Medical Imaging
The proposed CNN-based survival model outperformed the traditional CPH-based radiomics approach in terms of concordance index by 22%, providing a better fit for patients' survival patterns.
Prognostic Value of Transfer Learning Based Features in Resectable Pancreatic Ductal Adenocarcinoma
The proposed deep transfer learning model for prognostication of PDAC achieved the area under the receiver operating characteristic curve of 0. 74, which was significantly higher than that of the traditional radiomics model (0. 56) as well as a CNN model trained from scratch (0. 50).
