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Cited by 264

The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.

Lessons from Controversy: Ovarian Cancer Screening and Serum Proteomics

Ransohoff, D. F.·2005·JNCI Journal of the National Cancer Institute

Abstract

In 2002 a study reported that a blood test, based on pattern-recognition proteomics mass spectroscopy analysis of serum, was nearly 100% sensitive and specific to detect ovarian cancer. Plans to introduce a commercial screening test by early 2004 were delayed amid concerns about whether the approach was reproducible and reliable. In this issue of JNCI, two commentaries discuss whether the initial results are reproducible and whether bias may account for results. This essay describes how threats to validity from chance and bias may cause erroneous results and inflated expectations in the kind of observational research being conducted in several "-omics" fields to assess molecular markers for diagnosis and prognosis of cancer. To address such threats and to realize the potential of new -omics technology will require application of appropriate rules of evidence in the design, conduct, and interpretation of clinical research about molecular markers.

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Metadata

Authors
Ransohoff, D. F.
Venue
JNCI Journal of the National Cancer Institute
Published
2005-02-15
Volume
97
Issue
4
Pages
315-319
ISSN
  • 0027-8874 (ISSN-L)
  • 1460-2105
OpenAlex
W2131865610
Cited by
264
References
31
FWCI
47.27
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Lessons from Controversy: Ovarian Cancer Screening and Serum Proteomics. https://app.cochat.ai/papers/10.1093/jnci/dji054
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Ransohoff, D. F. (2005). Lessons from Controversy: Ovarian Cancer Screening and Serum Proteomics. JNCI Journal of the National Cancer Institute. https://doi.org/10.1093/jnci/dji054

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.