Introduction Ovarian cancer is the most lethal gynaecological cancer in the UK, yet there is no screening program in place to facilitate early disease detection. The aim is to evaluate whether online search data (OSD) can be used to detect individuals with gynaecological malignancy.

Methods This prospective cohort study evaluates OSD in individuals referred with a suspected cancer to a London Hospital (UK) between December 2020 and June 2022. OSD was extracted via Google takeout and anonymised. Health-related terms were extracted (24 months prior to GP referral). A predictive model was developed using (1) search terms and (2) categorised search queries. Area under the ROC curve (AUC) was used to evaluate model performance. 844 women were approached, 652 were eligible to participate, 392 were recruited and 235 completed enrolment.

Results The cohort had a median age of 53 years old (range 20-81) and a 26.0% malignancy rate. OSD was different between individuals with a benign and malignant diagnosis, as early as 360 days before GP referral, using all search terms, but only 60 days before, using categorised search queries. A model using OSD from individuals (n=153) who performed health-related searches achieved its highest (sample-corrected) AUC of 0.82, 60 days before GP referral.

Conclusion/Implications OSD appears to be different between individuals with malignant and benign gynaecological conditions, with a signal observed in advance of GP referral date. OSD 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.