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@ARTICLE{Alves:306535,
author = {N. Alves and M. Schuurmans and D. Rutkowski and A. Saha and
P. Vendittelli and N. Obuchowski and M. H. Liedenbaum and I.
S. Haldorsen and A. Molven and D. Yakar and J. Geerdink and
S. van Koeverden and D. M. Riviere and W. Venderink and R.
de Haas and N. Kim and J.-M. Löhr and G. Suman and K. H.
Maier-Hein$^*$ and H. K. Hahn and W. Wang and A. L. Yuille
and A. Kambadakone and E. K. Fishman and C. Verbeke and G.
Litjens and J. J. Hermans and H. Huisman},
collaboration = {P. consortium},
othercontributors = {N. Alves and M. Schuurmans and A. Saha and P. Vendittelli
and G. Litjens and J. Hermans and H. Huisman and D. M.
Riviere and W. Venderink and S. van Koeverden and D.
Rutkowski and M. H. Liedenbaum and I. S. Haldorsen and A.
Molven and D. Yakar and R. J. de Haas and J. Geerdink and J.
Veltman and A. Yuille and A. Kambadakone and C. Verbeke and
C. Matos and E. Fishman and G. Suman and H. K. Hahn and K.
Maier-Hein$^*$ and J.-M. Löhr and N. Kim and N. Obuchowski
and S. Gallinger and W. Wang and A. Stunt and H. Liu and R.
Gao and S. Grbic and Z. Deng and Y. He and Y. Shi and R.
Vétil and N. Debs and C. Abi-Nader and A. Bône and M.-M.
Rohé and C.-Y. Yu and J. Ma and T. Fu and B. Wang and A. F.
Bezuidenhout and A. T. Huber and A. Liguori and A. Korchi
and A. Ponsiglione and A. Schulz and A. Stanzione and A.
Minieri and B.-B. Chen and C. Maino and C. Triantopoulou and
D. Christodoulou and D. Geisel and D.-M. Koh and E. Boffa
and E. Boninsegna and E. Genco and E. Soloff and E. A.
Lettieri and F. Omboni and F. Castagnoli and F. Prato and F.
Wessels and G. Avesani and G. Porrello and G. Brembilla and
G. Morana and G. Zamboni and G. di Costanzo and G. Juliusson
and H. B. Jenssen and H. Zandvoort and J. Pijls and J.
Prince and K. De Paepe and K. Petrovic and L. van Valkenhoef
and L. Fortuna and L. Mannacio and M. Engelbrecht and M.
Chincarini and M. Dioguardi Burgio and M. Zerunian and M.
Imbriaco and M. Bariani and M. Bonatti and M. Ronot and N.
Norstedt and N. Kurt and N. Patel and P. M. Sbeghen and P.
Patel and P. A. Bonaffini and R. P. Mucelli and R. E.
Büyüktoka and R. Geenen and R. Cuocolo and R. Valletta and
R. Musella and R. Cannella and R. S. Dwarkasing and S.
Venturini and S. Gourtsoyianni and S. Malekzadeh and U.
Tupputi and V. Obmann and V. Liu},
title = {{A}rtificial intelligence and radiologists in pancreatic
cancer detection using standard of care {CT} scans
({PANORAMA}): an international, paired, non-inferiority,
confirmatory, observational study.},
journal = {The lancet / Oncology},
volume = {27},
number = {1},
issn = {1470-2045},
address = {London},
publisher = {The Lancet Publ. Group},
reportid = {DKFZ-2025-02600},
pages = {116-124},
year = {2026},
note = {2026 Jan;27(1):116-124. doi: 10.1016/S1470-2045(25)00567-4.
Epub 2025 Nov 20},
abstract = {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 $[44\%]$ female, 1929 $[56\%]$ male; median
age 67 [IQR 58-74] years) included patients (Jan 1, 2004 to
Dec 31, 2023), 1103 $(32\%)$ received a positive PDAC
diagnosis. In the sequestered testing cohort of 1130
patients (406 with histologically confirmed PDAC), AI
achieved an AUROC of 0·92 $(95\%$ CI 0·90-0·93). In the
subset of 391 patients (144 $[37\%]$ with histologically
confirmed PDAC) used for the reader study, AI achieved
statistically non-inferior (p<0·0001) and superior
(p=0·001) performance with an AUROC of 0·92 $(95\%$ CI
0·89-0·94), compared to the pool of 68 participating
radiologists, with an AUROC of 0·88 (0·85-0·91).AI
demonstrated substantially improved PDAC detection on
routine CT scans compared to radiologists on average,
showing potential to detect cancer earlier and improve
patient outcomes.European Union's Horizon 2020 research and
innovation programme.},
cin = {E230},
ddc = {610},
cid = {I:(DE-He78)E230-20160331},
pnm = {315 - Bildgebung und Radioonkologie (POF4-315)},
pid = {G:(DE-HGF)POF4-315},
typ = {PUB:(DE-HGF)16},
pubmed = {pmid:41275871},
doi = {10.1016/S1470-2045(25)00567-4},
url = {https://inrepo02.dkfz.de/record/306535},
}
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