TY  - JOUR
AU  - Alves, Natalia
AU  - Schuurmans, Megan
AU  - Rutkowski, Dawid
AU  - Saha, Anindo
AU  - Vendittelli, Pierpaolo
AU  - Obuchowski, Nancy
AU  - Liedenbaum, Marjolein H
AU  - Haldorsen, Ingfrid S
AU  - Molven, Anders
AU  - Yakar, Derya
AU  - Geerdink, Jeroen
AU  - van Koeverden, Sebastiaan
AU  - Riviere, Deniece M
AU  - Venderink, Wulphert
AU  - de Haas, Robbert
AU  - Kim, Namkug
AU  - Löhr, J-Matthias
AU  - Suman, Garima
AU  - Maier-Hein, Klaus H
AU  - Hahn, Horst K
AU  - Wang, Weichung
AU  - Yuille, Alan L
AU  - Kambadakone, Avinash
AU  - Fishman, Elliot K
AU  - Verbeke, Caroline
AU  - Litjens, Geert
AU  - Hermans, John J
AU  - Huisman, Henkjan
TI  - Artificial intelligence and radiologists in pancreatic cancer detection using standard of care CT scans (PANORAMA): an international, paired, non-inferiority, confirmatory, observational study.
JO  - The lancet / Oncology
VL  - 27
IS  - 1
SN  - 1470-2045
CY  - London
PB  - The Lancet Publ. Group
M1  - DKFZ-2025-02600
SP  - 116-124
PY  - 2026
N1  - 2026 Jan;27(1):116-124. doi: 10.1016/S1470-2045(25)00567-4. Epub 2025 Nov 20
AB  - Pancreatic ductal adenocarcinoma (PDAC) has the worst prognosis among major cancer types, primarily due to late diagnosis on contrast-enhanced CT. Artificial intelligence (AI) can improve diagnostic performance, but robust benchmarks and reliable comparison to radiologists' performance are scarce. We established an open-source benchmark with the aim of investigating AI systems for PDAC detection on CT and compared them to radiologists' performance, at scale.In this international, paired, non-inferiority, confirmatory, observational study (PANORAMA), the AI system was trained and externally validated within an international benchmark, with a cohort of 2310 patients from four tertiary care centres in the Netherlands and the USA for training (n=2224) and tuning (n=86), and a sequestered cohort of 1130 patients from five tertiary care centres (the Netherlands, Sweden, and Norway) for testing. A multi-reader, multi-case observer study with 68 radiologists (40 centres, 12 countries; median 9·0 [IQR 6·0-14·5] years of experience) was conducted on a subset of 391 patients from the testing cohort. The reference standard was established with histopathology and at least 3 years of clinical follow-up. The primary endpoint was the mean area under the receiver operating characteristic curve (AUROC) of the AI system compared to that of radiologists at PDAC detection on CT. The study protocol and statistical plan were prespecified to test non-inferiority (considering a margin of 0·05), followed by superiority towards the AI system. This study is registered with Zenodo (https://doi.org/10.5281/zenodo.10599559) and is complete.Of the 3440 (1511 [44
LB  - PUB:(DE-HGF)16
C6  - pmid:41275871
DO  - DOI:10.1016/S1470-2045(25)00567-4
UR  - https://inrepo02.dkfz.de/record/306535
ER  -