Evaluation of the Feasibility of Screening Patients for Early Signs of Lung Carcinoma in Web Search Logs.
OBJECTIVE
A statistical model that predicts the appearance of strong evidence of a lung carcinoma diagnosis via analysis of large-scale anonymized logs of web search queries from millions of people across the United States.


OBJECTIVE
To evaluate the feasibility of screening patients at risk of lung carcinoma via analysis of signals from online search activity.


METHODS
We identified people who issue special queries that provide strong evidence of a recent diagnosis of lung carcinoma. We then considered patterns of symptoms expressed as searches about concerning symptoms over several months prior to the appearance of the landmark web queries. We built statistical classifiers that predict the future appearance of landmark queries based on the search log signals. This was a retrospective log analysis of the online activity of millions of web searchers seeking health-related information online. Of web searchers who queried for symptoms related to lung carcinoma, some (n = 5443 of 4 813 985) later issued queries that provide strong evidence of recent clinical diagnosis of lung carcinoma and are regarded as positive cases in our analysis. Additional evidence on the reliability of these queries as representing clinical diagnoses is based on the significant increase in follow-on searches for treatments and medications for these searchers and on the correlation between lung carcinoma incidence rates and our log-based statistics. The remaining symptom searchers (n = 4 808 542) are regarded as negative cases.


METHODS
Performance of the statistical model for early detection from online search behavior, for different lead times, different sets of signals, and different cohorts of searchers stratified by potential risk.


RESULTS
The statistical classifier predicting the future appearance of landmark web queries based on search log signals identified searchers who later input queries consistent with a lung carcinoma diagnosis, with a true-positive rate ranging from 3% to 57% for false-positive rates ranging from 0.00001 to 0.001, respectively. The methods can be used to identify people at highest risk up to a year in advance of the inferred diagnosis time. The 5 factors associated with the highest relative risk (RR) were evidence of family history (RR = 7.548; 95% CI, 3.937-14.470), age (RR = 3.558; 95% CI, 3.357-3.772), radon (RR = 2.529; 95% CI, 1.137-5.624), primary location (RR = 2.463; 95% CI, 1.364-4.446), and occupation (RR = 1.969; 95% CI, 1.143-3.391). Evidence of smoking (RR = 1.646; 95% CI, 1.032-2.260) was important but not top-ranked, which was due to the difficulty of identifying smoking history from search terms.


CONCLUSIONS
Pattern recognition based on data drawn from large-scale web search queries holds opportunity for identifying risk factors and frames new directions with early detection of lung carcinoma.
Microsoft Research, Redmond, Washington.
Ryen W
RW
White
Microsoft Research, Redmond, Washington.
Eric
E
Horvitz
D002289:Carcinoma, Non-Small-Cell Lung / Q000175:diagnosis*; D055088:Early Detection of Cancer / Q000379:methods*; D005240:Feasibility Studies; D006801:Humans; D057226:Information Seeking Behavior; D020407:Internet / Q000706:statistics & numerical data*; D008175:Lung Neoplasms / Q000175:diagnosis*; D008403:Mass Screening / Q000379:methods; D015233:Models, Statistical; D012307:Risk Factors; D057386:Search Engine; D055752:Small Cell Lung Carcinoma / Q000175:diagnosis*