Evidence map›Paper›PMID 38462622›Full record

ArticleBMC public health2024

Using online search activity for earlier detection of gynaecological malignancy.

Jennifer F Barcroft, Elad Yom-Tov, Vasileios Lampos, Laura Burney Ellis, David Guzman, Víctor Ponce-López, Tom Bourne, Ingemar J Cox, Srdjan Saso

Erratum issuedAbstract read
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Article in BMC public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Jennifer F BarcroftImperial College London, Hammersmith Hospital Campus, Du Cane Road, London, W12 0HS, UK.
Elad Yom-TovMicrosoft Research, Hoshaya, Israel.
Vasileios LamposDepartment of Computer Science, University College London, London, UK.
Laura Burney EllisImperial College London, Hammersmith Hospital Campus, Du Cane Road, London, W12 0HS, UK.
David GuzmanDepartment of Computer Science, University College London, London, UK.
Víctor Ponce-LópezDepartment of Computer Science, University College London, London, UK.
Tom BourneImperial College London, Hammersmith Hospital Campus, Du Cane Road, London, W12 0HS, UK.
Ingemar J CoxDepartment of Computer Science, University College London, London, UK.
Srdjan SasoImperial College London, Hammersmith Hospital Campus, Du Cane Road, London, W12 0HS, UK. srdjan.saso01@imperial.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundOvarian cancer is the most lethal and endometrial cancer the most common gynaecological cancer in the UK, yet neither have a screening program in place to facilitate early disease detection. The aim is to evaluate whether online search data can be used to differentiate between individuals with malignant and benign gynaecological diagnoses.

methodsThis is a prospective cohort study evaluating online search data in symptomatic individuals (Google user) referred from primary care (GP) with a suspected cancer to a London Hospital (UK) between December 2020 and June 2022. Informed written consent was obtained and online search data was extracted via Google takeout and anonymised. A health filter was applied to extract health-related terms for 24 months prior to GP referral. A predictive model (outcome: malignancy) was developed using (1) search queries (terms model) and (2) categorised search queries (categories model). Area under the ROC curve (AUC) was used to evaluate model performance. 844 women were approached, 652 were eligible to participate and 392 were recruited. Of those recruited, 108 did not complete enrollment, 12 withdrew and 37 were excluded as they did not track Google searches or had an empty search history, leaving a cohort of 235.

resultsThe cohort had a median age of 53 years old (range 20-81) and a malignancy rate of 26.0%. There was a difference in online search data between those with a benign and malignant diagnosis, noted as early as 360 days in advance of GP referral, when search queries were used directly, but only 60 days in advance, when queries were divided into health categories. A model using online search data from patients (n = 153) who performed health-related search and corrected for sample size, achieved its highest sample-corrected AUC of 0.82, 60 days prior to GP referral.

conclusionsOnline search data appears to be different between individuals with malignant and benign gynaecological conditions, with a signal observed in advance of GP referral date. Online search data needs to be evaluated in a larger dataset to determine its value as an early disease detection tool and whether its use leads to improved clinical outcomes.

Indexed as

Genital Neoplasms, FemaleOvarian NeoplasmsAdultAgedAged, 80 and overEarly Detection of CancerFemaleHumansLondonMiddle AgedProspective StudiesYoung AdultCancer screening testEarly detection of cancerEndometrial neoplasmsHealthInternetOvarian neoplasms

Identifiers

PMID38462622
PMCPMC10926628

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.