A fusion-based deep-learning algorithm predicts PDAC metastasis based on primary tumour CT images: a multinational study.
Authors
Affiliations (11)
Affiliations (11)
- Spanish National Cancer Research Centre, Madrid, Spain.
- Sección de Imagen Abdominal, Hospital Vall d'Hebron, Barcelona, Catalunya, Spain.
- Radiology, Hospital Ramón y Cajal, Madrid, Spain.
- Universitair Medisch Centrum Sint Radboud, Nijmegen, Netherlands.
- Genetic and Molecular Epidemiology Group, Spanish National Cancer Research Center (CNIO), Madrid, Spain.
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain.
- Centro de Investigación Biomédica en Red-BBN de Bioingeniería, Biomateriales y Nanomedicina, Instituto Salud Carlos III, Madrid, Spain, Madrid, Spain.
- The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
- Radiology and Nucleas Medicine, Radboud University Medical Center Nijmegen, Nijmegen, Netherlands.
- ErasmusMC Rotterdam, NL, /, Netherlands.
- Spanish National Cancer Research Centre, Madrid, Spain [email protected].
Abstract
Diagnosing the presence of metastasis of pancreatic cancer is pivotal for patient management and treatment, with contrast-enhanced CT scans (CECT) as the cornerstone of diagnostic evaluation. However, this diagnostic modality requires a multifaceted approach. To develop a convolutional neural network (CNN)-based model (PMPD, Pancreatic cancer Metastasis Prediction Deep-learning algorithm) to predict the presence of metastases based on CECT images of the primary tumour. CECT images in the portal venous phase of 335 patients with pancreatic ductal adenocarcinoma (PDAC) from the PanGenEU study and The First Affiliated Hospital of Zhengzhou University (ZZU) were randomly divided into training and internal validation sets by applying fivefold cross-validation. Two independent external validation datasets of 143 patients from the Radboud University Medical Center (RUMC), included in the PANCAIM study (RUMC-PANCAIM) and 183 patients from the PREOPANC trial of the Dutch Pancreatic Cancer Group (PREOPANC-DPCG) were used to evaluate the results. The area under the receiver operating characteristic curve (AUROC) for the internally tested model was 0.895 (0.853-0.937) and 0.779 (0.741-0.817) in the PanGenEU and ZZU sets, respectively. In the external validation sets, the mean AUROC was 0.806 (0.787-0.826) for the RUMC-PANCAIM and 0.761 (0.717-0.804) for the PREOPANC-DPCG. When stratified by the different metastasis sites, the PMPD model achieved the average AUROC between 0.901-0.927 in PanGenEU, 0.782-0.807 in ZZU and 0.761-0.820 in PREOPANC-DPCG sets. A PMPD-derived Metastasis Risk Score (MRS) (HR: 2.77, 95% CI 1.99 to 3.86, p=1.59e-09) outperformed the Resectability status from the National Comprehensive Cancer Network guideline and the CA19-9 biomarker in predicting overall survival. Meanwhile, the MRS could potentially predict developed metastasis (AUROC: 0.716 for within 3 months, 0.645 for within 6 months). This study represents a pioneering utilisation of a high-performance deep-learning model to predict extrapancreatic organ metastasis in patients with PDAC.