Early Detection of Cancer
Open accessAbstract
Survival improves when cancer is detected early. However, ~50% of cancers are at an advanced stage when diagnosed. Early detection of cancer or precancerous change allows early intervention to try to slow or prevent cancer development and lethality. To achieve early detection of all cancers, numerous challenges must be overcome. It is vital to better understand who is at greatest risk of developing cancer. We also need to elucidate the biology and trajectory of precancer and early cancer to identify consequential disease that requires intervention. Insights must be translated into sensitive and specific early detection technologies and be appropriately evaluated to support practical clinical implementation. Interdisciplinary collaboration is key; advances in technology and biological understanding highlight that it is time to accelerate early detection research and transform cancer survival.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
Without definitive tests, healthcare providers often depend on clinical judgment and broader testing approaches, sometimes resulting in diagnostic errors.
“Without definitive tests, healthcare providers often depend on clinical judgment and broader testing approaches, sometimes resulting in diagnostic errors.”
From Toward Safer Diagnoses: A SEIPS-Based Narrative Review of Diagnostic Errors (2026) · cites this paper as [185]
There is a lack of appropriate or specific diagnostic tests for certain conditions or diseases, especially in their early stages.
“First, there is a lack of appropriate or specific diagnostic tests for certain conditions or diseases, especially in their early stages [185,186,187].”
From Toward Safer Diagnoses: A SEIPS-Based Narrative Review of Diagnostic Errors (2026) · cites this paper as [185]
Cites
Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., & Bray, F. (2021) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21660 · Cited by 108,699 · see citing works
Hallmarks of Cancer: The Next Generation
Hanahan, D., & Weinberg, R. A. (2011) · Cell · doi:10.1016/j.cell.2011.02.013 · Cited by 66,590 · see citing works
Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening
The National Lung Screening Trial Research Team (2011) · New England Journal of Medicine · doi:10.1056/nejmoa1102873 · Cited by 11,064 · see citing works
The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups
METABRIC Group, Curtis, C., Shah, S. P., Chin, S.-F., Turashvili, G., Rueda, O. M., Dunning, M. J., Speed, D., Lynch, A. G., Samarajiwa, S., et al. (2012) · Nature · doi:10.1038/nature10983 · Cited by 6,097 · see citing works
Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors
Routy, B., Le Chatelier, E., Derosa, L., Duong, C. P. M., Alou, M. T., Daillère, R., Fluckiger, A., Messaoudene, M., Rauber, C., Roberti, M. P., et al. (2018) · Science · doi:10.1126/science.aan3706 · Cited by 5,671 · see citing works
Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients
Gopalakrishnan, V., Spencer, C. N., Nezi, L., Reuben, A., Andrews, M. C., Karpinets, T. V., Prieto, P. A., Vicente, D., Hoffman, K., Wei, S. C., et al. (2018) · Science · doi:10.1126/science.aan4236 · Cited by 4,812 · see citing works
Self-citationAccessories to the Crime: Functions of Cells Recruited to the Tumor Microenvironment
Hanahan, D., & Coussens, L. M. (2012) · Cancer Cell · doi:10.1016/j.ccr.2012.02.022 · Cited by 4,632 · see citing works
Self-citationAccurate whole human genome sequencing using reversible terminator chemistry
Bentley, D. R., Balasubramanian, S., Swerdlow, H. P., Smith, G. P., Milton, J., Brown, C. G., Hall, K. P., Evers, D. J., Barnes, C. L., Bignell, H. R., et al. (2008) · Nature · doi:10.1038/nature07517 · Cited by 3,684 · see citing works
Self-citationReduced Lung-Cancer Mortality with Volume CT Screening in a Randomized Trial
de Koning, H. J., van der Aalst, C. M., de Jong, P. A., Scholten, E. T., Nackaerts, K., Heuvelmans, M. A., Lammers, J.-W. J., Weenink, C., Yousaf-Khan, U., Horeweg, N., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa1911793 · Cited by 3,624 · see citing works
The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients
Matson, V., Fessler, J., Bao, R., Chongsuwat, T., Zha, Y., Alegre, M.-L., Luke, J. J., & Gajewski, T. F. (2018) · Science · doi:10.1126/science.aao3290 · Cited by 3,107 · see citing works
Detection and localization of surgically resectable cancers with a multi-analyte blood test
Cohen, J. D., Li, L., Wang, Y., Thoburn, C., Afsari, B., Danilova, L., Douville, C., Javed, A. A., Wong, F., Mattox, A., et al. (2018) · Science · doi:10.1126/science.aar3247 · Cited by 2,783 · see citing works
Christodoulou, E., Ma, J., Collins, G. S., Steyerberg, E. W., Verbakel, J. Y., & Van Calster, B. (2019) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2019.02.004 · Cited by 2,119 · see citing works
Multitarget Stool DNA Testing for Colorectal-Cancer Screening
Imperiale, T. F., Ransohoff, D. F., Itzkowitz, S. H., Levin, T. R., Lavin, P., Lidgard, G. P., Ahlquist, D. A., & Berger, B. M. (2014) · New England Journal of Medicine · doi:10.1056/nejmoa1311194 · Cited by 1,900 · see citing works
Liu, M. C., Oxnard, G. R., Klein, E. A., Swanton, C., Seiden, M. V., Liu, M. C., Oxnard, G. R., Klein, E. A., Smith, D., Richards, D., et al. (2020) · Annals of Oncology · doi:10.1016/j.annonc.2020.02.011 · Cited by 1,495 · see citing works
Long-term colorectal-cancer incidence and mortality after lower endoscopy
Nishihara, R., Wu, K., Lochhead, P., Morikawa, T., Liao, X., Qian, Z. R., Inamura, K., Kim, S. A., Kuchiba, A., Yamauchi, M., et al. (2013) · New England Journal of Medicine · doi:10.1056/nejmoa1301969 · Cited by 1,483 · see citing works
Prevalence of monoclonal gammopathy of undetermined significance
Kyle, R. A., Therneau, T. M., Rajkumar, S. V., Larson, D. R., Plevak, M. F., Offord, J. R., Dispenzieri, A., Katzmann, J. A., & Melton, L. J. (2006) · New England Journal of Medicine · doi:10.1056/nejmoa054494 · Cited by 1,423 · cited 1× · see citing works
“myeloma (a lethal cancer) of only 1% per year (11), and the risk of Barrett’s esophagus developing into esophageal cancer is ~0.”
Digital pathology and artificial intelligence
Niazi, M. K. K., Parwani, A. V., & Gurcan, M. N. (2019) · The Lancet Oncology · doi:10.1016/s1470-2045(19)30154-8 · Cited by 1,232 · see citing works
Lenalidomide plus Dexamethasone for High-Risk Smoldering Multiple Myeloma
Mateos, M.-V., Hernández, M.-T., Giraldo, P., de la Rubia, J., de Arriba, F., Corral, L. L., Rosiñol, L., Paiva, B., Palomera, L., Bargay, J., et al. (2013) · New England Journal of Medicine · doi:10.1056/nejmoa1300439 · Cited by 1,171 · see citing works
How the ageing microenvironment influences tumour progression
Fane, M., & Weeraratna, A. T. (2020) · Nature Reviews Cancer · doi:10.1038/s41568-019-0222-9 · Cited by 849 · see citing works
Palucka, A. K., & Coussens, L. M. (2016) · Cell · doi:10.1016/j.cell.2016.01.049 · Cited by 843 · see citing works
Self-citationTransparency and reproducibility in artificial intelligence
Haibe-Kains, B., Adam, G. A., Hosny, A., Khodakarami, F., Massive Analysis Quality Control (MAQC) Society Board of Directors, Shraddha, T., Kusko, R., Sansone, S.-A., Tong, W., Wolfinger, R. D., et al. (2020) · Nature · doi:10.1038/s41586-020-2766-y · Cited by 540 · see citing works
TGF-β Promotes Heterogeneity and Drug Resistance in Squamous Cell Carcinoma
Oshimori, N., Oristian, D., & Fuchs, E. (2015) · Cell · doi:10.1016/j.cell.2015.01.043 · Cited by 471 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
The STARD-AI reporting guideline for diagnostic accuracy studies using artificial intelligence
Sounderajah, V., Guni, A., Liu, X., Collins, G. S., Karthikesalingam, A., Markar, S. R., Golub, R. M., Denniston, A. K., Shetty, S., Moher, D., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03953-8 · Cited by 352 · see citing works
Zhu, L., Song, G., Zhang, W., Wu, Y., Chen, Y., Song, J., Wang, D., Li, G., Tang, B. Z., & Li, Y. (2025) · Nature Communications · doi:10.1038/s41467-025-57533-y · Cited by 63 · see citing works
Aguiar-Ibáñez, R., Mbous, Y. P. V., Sharma, S., & Chawla, E. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1546447 · Cited by 17 · see citing works
Toward Safer Diagnoses: A SEIPS-Based Narrative Review of Diagnostic Errors
Yen, C., Epling, J. W., Rockwell, M., & Vaughn-Cooke, M. (2026) · Diagnostics · doi:10.3390/diagnostics16020347 · Cited by 6 · see citing works
- Authors
- Crosby, D., Bhatia, S., Brindle, K. M., Coussens, L. M., Dive, C., Emberton, M., Esener, S., Fitzgerald, R. C., Gambhir, S. S., Kuhn, P., Rebbeck, T. R., & Balasubramanian, S.
- Affiliations
- Cancer Research UK (GB)
- Howard Hughes Medical Institute (US)
- University of Cambridge (GB)
- Oregon Health & Science University (US)
- University College London (GB)
- Stanford University (US)
- USC Michelson Center Convergent Science Institute in Cancer, University of Southern California, Los Angeles, CA, USA.
- Harvard University (US)
- Corresponding author
- Crosby, David
- Balasubramanian, Shankar
- ORCID
- Venue
- Science
- Published
- 2022-03-18
- Volume
- 375
- Issue
- 6586
- Pages
- eaay9040-eaay9040
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W4220903131
- Cited by
- 1,379
- References
- 22
- Citations by year
- 2026: 310 · 2025: 478 · 2024: 351 · 2023: 196 · 2022: 44
- FWCI
- 32.77
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Apollo (University of Cambridge) (submittedVersion)
- Apollo
- Funders
- Cancer Research UK (CB4330, 28289)
- National Cancer Institute (P30CA069533)
- Topics
- Health Sciences › Medicine › Oncology › Global Cancer Incidence and Screening
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Health Sciences › Medicine › Oncology › Cancer Diagnosis and Treatment
- Keywords
- early cancer detection, precancerous lesions, early-stage cancer, cancer risk, cancer screening, cancer survival, clinical implementation, interdisciplinary collaboration
- MeSH terms
- Disease Susceptibility, Female, Humans, Male, Neoplasms, Sensitivity and Specificity, Biomarkers, Tumor, Risk Assessment, Diagnostic Techniques and Procedures, Early Detection of Cancer, Carcinogenesis
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