RESUMO
Both the incidence and prevalence of well-differentiated neuroendocrine tumours from the small intestine (Si-NET) are gradually increasing. Most patients have non-functioning tumours with subtle GI symptoms and tumours are often discovered incidentally by endoscopy or at advanced disease stages by imaging depicting mesenteric lymph node and /or liver metastases while around 30% of the patients present with symptoms of the carcinoid syndrome. Adequate biochemical assessment and staging including functional imaging is crucial for treatment-related decision-making that should take place in an expert multidisciplinary team setting. Preferably, patients should be referred to specialised ENETS Centres of Excellence or centres of high expertise in the field. This guidance paper provides the current evidence and best knowledge for the management of Si-NET grade (G) 1-3 following 10 key questions of practical relevance for the diagnostic and therapeutic decision making.
RESUMO
Neoadjuvant therapy (NAT) has become routine in patients with borderline resectable pancreatic cancer. Pathologists examine pancreatic cancer resection specimens to evaluate the effect of NAT. However, an automated scoring system to objectively quantify residual pancreatic cancer (RPC) is currently lacking. Herein, we developed and validated the first automated segmentation model using artificial intelligence techniques to objectively quantify RPC. Digitized histopathological tissue slides were included from resected pancreatic cancer specimens from 14 centers in 7 countries in Europe, North America, Australia, and Asia. Four different scanner types were used: Philips (56%), Hamamatsu (27%), 3DHistech (10%), and Leica (7%). Regions of interest were annotated and classified as cancer, non-neoplastic pancreatic ducts, and others. A U-Net model was trained to detect RPC. Validation consisted of by-scanner internal-external cross-validation. Overall, 528 unique hematoxylin and eosin (H & E) slides from 528 patients were included. In the individual Philips, Hamamatsu, 3DHistech, and Leica scanner cross-validations, mean F1 scores of 0.81 (95% CI, 0.77-0.84), 0.80 (0.78-0.83), 0.76 (0.65-0.78), and 0.71 (0.65-0.78) were achieved, respectively. In the meta-analysis of the cross-validations, the mean F1 score was 0.78 (0.71-0.84). A final model was trained on the entire data set. This ISGPP model is the first segmentation model using artificial intelligence techniques to objectively quantify RPC following NAT. The internally-externally cross-validated model in this study demonstrated robust performance in detecting RPC in specimens. The ISGPP model, now made publically available, enables automated RPC segmentation and forms the basis for objective NAT response evaluation in pancreatic cancer.