The DOI matches its Crossref record; 63 of the 64 references it cites were found in a registry or an index.
ESMO Guidance for Reporting Oncology real-World evidence (GROW)
Open accessAbstract
•Real-world evidence in oncology is evolving rapidly with many particularities. •This guidance provides key recommendations for reporting real-world evidence studies in oncology. •Recommendations are based on a review of current evidence and the authors’ collective expert opinion. •Authors are a multidisciplinary group of experts from different institutions and countries. •Guidance is provided for full article development, including title, introduction, methods, results, discussion, conclusion.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“Finally, there is an increasing use of AI in analyzing real-world data (RWD), and the European Society for Medical Oncology Guidance for Reporting Oncology Real-World Evidence (ESMO-GROW) now provides publication guidelines regarding the use of RWD in oncology, along with specific AIfocused reporting guidelines (201).”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [201]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions
Sterne, J. A., Hernán, M. A., Reeves, B. C., Savović, J., Berkman, N. D., Viswanathan, M., Henry, D., Altman, D. G., Ansari, M. T., Boutron, I., et al. (2016) · BMJ · doi:10.1136/bmj.i4919 · Cited by 20,329
Elm, E. V., Altman, D. G., Egger, M., Pocock, S. J., Gøtzsche, P. C., & Vandenbroucke, J. P. (2007) · BMJ · doi:10.1136/bmj.39335.541782.ad · Cited by 10,766
Benchimol, E. I., Smeeth, L., Guttmann, A., Harron, K., Moher, D., Petersen, I., Sørensen, H. T., von Elm, E., Langan, S. M., & RECORD Working Committee (2015) · PLOS Medicine · doi:10.1371/journal.pmed.1001885 · Cited by 5,749
Galit Shmueli, A. (2010) · Statistical Science · doi:10.1214/10-sts330 · Cited by 3,392
Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available: Table 1.
Hernán, M. A., & Robins, J. M. (2016) · American Journal of Epidemiology · doi:10.1093/aje/kwv254 · Cited by 3,115
Statistics versus machine learning
Bzdok, D., Altman, N., & Krzywinski, M. (2018) · Nature Methods · doi:10.1038/nmeth.4642 · Cited by 1,508
Statistics in Medicine — Reporting of Subgroup Analyses in Clinical Trials
Wang, R., Lagakos, S. W., Ware, J. H., Hunter, D. J., & Drazen, J. M. (2007) · New England Journal of Medicine · doi:10.1056/nejmsr077003 · Cited by 1,445
Checklist for Artificial Intelligence in Medical Imaging (CLAIM): a guide for authors and reviewers
Mongan, J., Moy, L., & Kahn, C. E. (2020) · Radiology: Artificial Intelligence · doi:10.1148/ryai.2020200029 · Cited by 1,443
REporting recommendations for tumour MARKer prognostic studies (REMARK).
for the Statistics Subcommittee of the NCI-EORTC Working Group on Cancer Diagnostics, McShane, L. M., Altman, D. G., Sauerbrei, W., Taube, S. E., Gion, M., & Clark, G. M. (2005) · British Journal of Cancer · doi:10.1038/sj.bjc.6602678 · Cited by 1,394
Morgan, R. L., Whaley, P., Thayer, K. A., & Schünemann, H. J. (2018) · Environment International · doi:10.1016/j.envint.2018.07.015 · Cited by 1,282
Cited by
Sorted by citations
BCLC strategy for prognosis prediction and treatment recommendations: The 2026 update
Reig, M., Sanduzzi‐Zamparelli, M., Forner, A., Rimola, J., Ferrer, J., Burrel, M., García‐Criado, Á., Díaz, A., Llarch, N., Iserte, G., et al. (2025) · Journal of Hepatology · doi:10.1016/j.jhep.2025.10.020 · Cited by 253
A guide to artificial intelligence for cancer researchers
Pérez-López, R., Laleh, N. G., Mahmood, F., & Kather, J. N. (2024) · Nature reviews. Cancer · doi:10.1038/s41568-024-00694-7 · Cited by 227
Advances in molecular pathology and therapy of non-small cell lung cancer
Huang, Q., Li, Y., Huang, Y., Wu, J., Bao, W., Xue, C., Li, X., Dong, S., Dong, Z., & Hu, S. (2025) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-025-02243-6 · Cited by 197
Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions
Lotter, W., Hassett, M. J., Schultz, N., Kehl, K. L., Van Allen, E. M., & Cerami, E. (2024) · Cancer Discovery · doi:10.1158/2159-8290.cd-23-1199 · Cited by 196
Privacy-preserving large language models for structured medical information retrieval
Wiest, I. C., Ferber, D., Zhu, J., van Treeck, M., Meyer, S. K., Juglan, R., Carrero, Z. I., Paech, D., Kleesiek, J., Ebert, M., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01233-2 · Cited by 184
Metadata
- Authors
- Castelo-Branco, L., Pellat, A., Martins-Branco, D., Valachis, A., Derksen, J. W. G., Suijkerbuijk, K. P. M., Dafni, U., Dellaporta, T., Vogel, A., Prelaj, A., Groenwold, R. H. H., Martins, H., Stahel, R., Bliss, J., Kather, J., Ribelles, N., Perrone, F., Hall, P. S., Dienstmann, R., Booth, C. M., Pentheroudakis, G., Delaloge, S., & Koopman, M.
- Affiliations
- European Society for Medical Oncology (CH)
- Université Paris Cité (FR)
- Université Libre de Bruxelles (BE)
- Örebro University (SE)
- Utrecht University (NL)
- National and Kapodistrian University of Athens (GR)
- Frontier Science Foundation-Hellas (GR)
- University Health Network (CA)
- Fondazione Politecnico di Milano (IT)
- Leiden University Medical Center (NL)
- Iscte – Instituto Universitário de Lisboa (PT)
- International Breast Cancer Study Group (CH)
- Institute of Cancer Research (GB)
- Heidelberg University (DE)
- Instituto de Investigación Biomédica de Málaga (ES)
- Istituto Nazionale Tumori IRCCS "Fondazione G. Pascale" (IT)
- Institute of Genetics and Cancer (GB)
- Vall d'Hebron Institute of Oncology (ES)
- Queen's University (CA)
- Institut Gustave Roussy (FR)
- Corresponding author
- Castelo-Branco, L.
- ORCID
- Venue
- Annals of Oncology
- Publisher
- Elsevier BV
- Published
- 2023-12
- Volume
- 34
- Issue
- 12
- Pages
- 1097-1112
- ISSN
- 0923-7534 (ISSN-L)
- OpenAlex
- W4387652542
- Cited by
- 247
- References
- 64
- Citations by year
- 2026: 70 · 2025: 108 · 2024: 65 · 2023: 4
- FWCI
- 45.81
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Annals of Oncology (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Leiden Repository (Leiden University) (submittedVersion, cc-by-nc)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by-nc)
- Funders
- Amgen
- Eli Lilly and Company
- Pfizer
- AstraZeneca
- Sanofi
- Gilead Sciences
- Associazione Italiana di Oncologia Medica
- Les Laboratories Pierre Fabre
- Clovis Oncology
- Incyte
- Puma Biotechnology
- Seagen
- Cancer Research UK (23706, 25351)
- National Institute for Health and Care Research (NIHR202432)
- European Commission
- Eisai
- Astellas Pharma
- European Society for Medical Oncology
- Servier
- Ipsen
- Nordic Pharma Group
- Sirtex Medical
- Genentech
- Foundation Medicine
- Cilag
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Clinical practice guidelines implementation
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- real-world evidence, oncology, reporting guidelines, ESMO, article structure
- MeSH terms
- Humans, Medical Oncology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
