Using online search activity for earlier detection of gynaecological malignancy

Abstract

Background: Ovarian 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.

Methods: This 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.

Results: The 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.

Conclusions: Online 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.

Keywords: Cancer screening test; Early detection of cancer; Endometrial neoplasms; Health; Internet; Ovarian neoplasms.

Fig. 1

Summary of patient enrolment flowchart. The flowchart outlines the enrolment process for the study cohort (n = 235) and health-related search cohort (n = 153), from individuals referred to a London University Teaching Hospital with a suspected cancer between December 2020-June 2022. It outlines the reasons for incomplete enrolment and exclusion from the study

Fig. 2

The time series chart outlining the number of online search queries per discrete category within the study cohort. The time series chart outlines the number of online search queries made per patient, within each distinct symptom category: menopause, urinary, bleeding, bloating, gastrointestinal, vagina, pain etc. stratified by outcome (benign/malignant) up to 490 days in advance of GP referral. The time series are smoothed using a 4-week moving average. Online search activity can identify symptomatic individuals with gynaecological cancer at an earlier stage

Fig. 3

Model performance as a function of start and end times. The top figure shows the AUC for the terms model and the bottom figure for the categories model. The start and end times correspond to the duration of time in advance of GP referral date. Different lines correspond to different start (T₁) times and the dots on each line correspond to different end (T₂) times. Each dot represents the average of 10 runs. Standard deviation is equal, on average, to 0.01 (1.8% of the average AUC)

Fig. 4

A histogram (10 bins) of model classification scores, when applied to our sample population (n = 235) and Bing users (n = 1.8 million). The histogram demonstrates the classification score for individual users. A high classification score indicates an increased likelihood of a malignant diagnosis. The Bing user population is distributed towards lower classification scores, in line with benign sample population and a lower likelihood of malignancy