A multidomain fusion model of radiomics and deep learning to discriminate between PDAC and AIP based on 18F-FDG PET/CT images

Abstract

Purpose To explore a multidomain fusion model of radiomics and deep learning features based on 18 F-fluorodeoxyglucose positron emission tomography/computed tomography ( 18 F-FDG PET/CT) images to distinguish pancreatic ductal adenocarcinoma (PDAC) and autoimmune pancreatitis (AIP), which could effectively improve the accuracy of diseases diagnosis. Materials and methods This retrospective study included 48 patients with AIP (mean age, 65 ± 12.0 years; range, 37–90 years) and 64 patients with PDAC patients (mean age, 66 ± 11.3 years; range, 32–88 years). Three different methods were discussed to identify PDAC and AIP based on 18 F-FDG PET/CT images, including the radiomics model (RAD_model), the deep learning model (DL_model), and the multidomain fusion model (MF_model). We also compared the classification results of PET/CT, PET, and CT images in these three models. In addition, we explored the attributes of deep learning abstract features by analyzing the correlation between radiomics and deep learning features. Five-fold cross-validation was used to calculate receiver operating characteristic (ROC), area under the roc curve (AUC), accuracy (Acc), sensitivity (Sen), and specificity (Spe) to quantitatively evaluate the performance of different classification models. Results The experimental results showed that the multidomain fusion model had the best comprehensive performance compared with radiomics and deep learning models, and the AUC, accuracy, sensitivity, specificity were 96.4% (95% CI 95.4–97.3%), 90.1% (95% CI 88.7–91.5%), 87.5% (95% CI 84.3–90.6%), and 93.0% (95% CI 90.3–95.6%), respectively. And our study proved that the multimodal features of PET/CT were superior to using either PET or CT features alone. First-order features of radiomics provided valuable complementary information for the deep learning model. Conclusion The preliminary results of this paper demonstrated that our proposed multidomain fusion model fully exploits the value of radiomics and deep learning features based on 18 F-FDG PET/CT images, which provided competitive accuracy for the discrimination of PDAC and AIP.

18 F-FDG-PET/CT based deep learning model for fully automated prediction of pathological grading for pancreatic ductal adenocarcinoma before surgery

“…Currently, research on deep learning models for PDAC mainly focuses on the disease's differential diagnosis, preoperative staging, and prognostic analysis. Wei et al 20 used a combination of machine learning and deep learning algorithms to extract features from PET/CT images to predict the difference between PDAC and autoimmune pancreatitis, developing a multi-domain fusion model with an overall performance of AUC, accuracy, sensitivity, and speci city of 0.96, 0.90, 0.88, and 0.93, respectively. Bian et al 21 developed and validated an automated preoperative AI algorithm for tumor and lymph node segmentation in CT imaging to predict LN metastasis in PDAC patients.…”

A review of deep learning and radiomics approaches for pancreatic cancer diagnosis from medical imaging

“…Integrating DL and radiomics approaches could potentially lead to more powerful diagnostic tools for pancreatic cancer. Combining these methods may result in enhanced model performance, benefiting from the strengths of both techniques, as suggested by several studies [38][39][40]. Future research should investigate the development and application of fusion models in pancreatic cancer diagnosis using medical imaging.…”

Deep Learning Applications in Pancreatic Cancer

“…To aid in the differentiation of autoimmune pancreatitis (AIP) from PC, Ziegelmayer et al developed a DL and radiomic-based model showing an AUC of 0.9 and 0.8, respectively [20]. Interestingly, fusion models incorporating DL and radiomic features consistently outperform pure radiomic models [14]. Radiomic feature extraction relies on pixel-level annotation and computational analysis.…”