Artificial Intelligence-based Segmentation of Residual Pancreatic Cancer in Resection Specimens Following Neoadjuvant Treatment (ISGPP-2): International Improvement and Validation Study.

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.

High prevalence of autoimmune disease in the rare inflammatory bone disorder sternocostoclavicular hyperostosis: survey of a Dutch cohort.

BACKGROUND: Sternocostoclavicular hyperostosis (SCCH; ORPHA178311) is a rare inflammatory disorder of the axial skeleton, the precise pathophysiology of which remains to be established. We addressed the potential association of SCCH with autoimmune processes by evaluating the lifetime prevalence of autoimmune disease in 70 patients with adult-onset SCCH and 518 SCCH-unaffected first-degree relatives (parents, siblings and children). Danish hospital registry data for autoimmune diseases were used as reference data. RESULTS: The mean age of interviewed patients was 56.3 years (range 26-80 years) and 86% were female. Interviewed patients belonged to 63 families, with four families having clusters of 2-3 patients. A diagnosis of at least one autoimmune disease was reported in 20 SCCH patients (29%) and in 47 relatives (9.1%), compared to an estimated 3.9% prevalence of autoimmune disease in the Danish reference population. A diversity of autoimmune diseases was reported in SCCH patients and relatives, most frequently psoriasis vulgaris (14%). Palmoplantar pustulosis was reported by 28 patients (40%). In SCCH patients, inclusion of palmoplantar pustulosis as putative autoimmune disease increased the overall prevalence to 54%. CONCLUSIONS: The high prevalence of autoimmune disease in patients with sternocostoclavicular hyperostosis and their first-degree relatives suggests that autoimmunity may play a role in the still elusive pathophysiology of the intriguing osteogenic response to inflammation observed in this rare bone disorder.