Investigating the potential for expediting diagnosis of ovarian cancer via prompt symptom recognition & "targeted screening"
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This thesis explores the potential for using symptoms as a tool to bring forward the diagnosis of ovarian cancer. Current evidence supports the existence of prediagnostic symptoms, however symptom lead time has yet to be adequately quantified. „Targeted screening‟ is one possible approach to expediting diagnosis. This would involve offering a blood test (e.g. CA125 or a future biomarker) to postmenopausal women presenting to primary care with symptoms possibly related to ovarian cancer. Key barriers include the non-specific nature of ovarian cancer symptoms and potential impact on GP workload. The main aims of this PhD research project were: To quantify the lead time of symptoms in ovarian cancer. To estimate the GP workload associated with offering a blood test to postmenopausal women with ovarian cancer symptoms.
Following a background to ovarian cancer symptoms research; a brief overview of the epidemiology of ovarian cancer, a case-control study to quantify symptom lead time, and a cross-sectional pilot study to estimate GP workload and symptom specificity in women aged 45-74 in the general population is presented. This is complemented by a systematic review focussed on the evidence for symptom lead time in ovarian cancer since 1980, with an update on ovarian cancer symptoms and a discussion of some of the methodological issues. The main findings showed that the diagnostic process could be initiated at least 3 months prior to the current date of diagnosis, in 45% to 74% of cases. However, pilot data suggest that 13%-35% of women in the general population aged ≥45 would be offered „targeted screening‟ in 1 year. Delays in ovarian cancer diagnosis of concern were identified but require further examination. Timing of symptoms is also an important consideration. The concluding chapter summarises the main findings of this thesis and discusses possibilities for future research.
Authors
Lim, Anita Wey WeyCollections
- Theses [3704]