Pancreatic NEuroendocrine Tumors (pNETs) are very rare endocrine neoplasms that account for less than 5% of all pancreatic malignancies, with an incidence of only 1-1.5 cases per 100,000. Early detection of pNETs is critical for improving patient survival, but the rarity of pNETs makes segmenting them from CT a very challenging problem. So far, there has not been a dataset specifically for pNETs available to researchers. To address this issue, we propose a pNETs dataset, a well-annotated Contrast-Enhanced Computed Tomography (CECT) dataset focused exclusively on Pancreatic Neuroendocrine Tumors, containing data from 469 patients. This is the first dataset solely dedicated to pNETs, distinguishing it from previous collections. Additionally, we provide the baseline detection networks with a new slice-wise weight loss function designed for the UNet-based model, improving the overall pNET segmentation performance. We hope that our dataset can enhance the understanding and diagnosis of pNET Tumors within the medical community, facilitate the development of more accurate diagnostic tools, and ultimately improve patient outcomes and advance the field of oncology.

An artificial intelligence (AI) tool developed by Cedars-Sinai investigators accurately predicted who would develop pancreatic cancer based on what their CT scan images looked like years prior to being diagnosed with the disease. The findings, which may help prevent death through early detection of one of the most challenging cancers to treat, are published in the journal Cancer Biomarkers. "This AI tool was able to capture and quantify very subtle, early signs of pancreatic ductal adenocarcinoma in CT scans years before occurrence of the disease. These are signs that the human eye would never be able to discern," said Debiao Li, Ph.D., director of the Biomedical Imaging Research Institute, professor of Biomedical Sciences and Imaging at Cedars-Sinai, and senior and corresponding author of the study. Li is also the Karl Storz Chair in Minimally Invasive Surgery in Honor of George Berci, MD.

An artificial intelligence (AI) tool developed by Cedars-Sinai investigators accurately predicted who would develop pancreatic cancer based on what their CT scan images looked like years prior to being diagnosed with the disease. The findings, which may help prevent death through early detection of one of the most challenging cancers to treat, are published in the journal Cancer Biomarkers. "This AI tool was able to capture and quantify very subtle, early signs of pancreatic ductal adenocarcinoma in CT scans years before occurrence of the disease. These are signs that the human eye would never be able to discern," said Debiao Li, PhD, director of the Biomedical Imaging Research Institute, professor of Biomedical Sciences and Imaging at Cedars-Sinai, and senior and corresponding author of the study. Li is also the Karl Storz Chair in Minimally Invasive Surgery in Honor of George Berci, MD.

BERG, a clinical-stage biotech that employs artificial intelligence (AI) to investigate diseases and develop innovative treatments, today announced two major medical/clinical research developments on pancreatic ductal adenocarcinoma (PDAC) to be presented virtually at the European Society for Medical Oncology (ESMO) 2020 Congress taking place from September 19-21, 2020. The first study entitled "Project Survival: High Fidelity Longitudinal Phenotypic and Multi-omic Characterization of Pancreatic Ductal Adenocarcinoma (PDAC) for Biomarker Discovery", is the culmination of the largest existing high-fidelity characterization of pancreatic cancer from a phenotypic/adaptive multi-omic perspective. BERG's Interrogative Biology platform was employed to identify causal relationships between existing pancreatic cancer therapies and changes in proteomic, metabolic and lipidomic responses to 253 treatment interventions and 211 progression events. The research cohort included PDAC patients across different stages including early, locally advanced and metastatic to yield the most accurate characterization of the evolution of the disease. Throughout the course of the study, 470,000 clinical data points were gathered.