Ricerc@Sapienza

Within the United Kingdom, cancer is estimated to contribute to over a quarter of deaths, and, over the last decade, incidence rates for all cancers combined have increased by more than 7%.
Almost half of cancers are diagnosed at a late stage and prognosis varies strongly by disease stage at diagnosis. The success of an early detection is dependent upon identifying populations at sufficient risk in order to maximize the benefit-to-harm ratio of the intervention, and this requires an accurate and powerful risk prediction model. Several models for many cancer sites already exist in literature. They are based traditional risk factors, related to sociodemographic, clinical and epidemiological characteristics. However, an increasing role in current cancer literature is being recognized to self-reported health-related quality of life (HRQoL) outcomes, which encompasses general health status, symptoms and functional domains. Although they have been extensively associated with survival endpoints, there is a gap in the research pertaining to what is known about their association with the risk of being diagnosed with cancer. Indeed, HRQoL specific domains such as fatigue, chest pain or persistent cough (which translates in an impaired overall health status) frequently exist before cancer diagnosis, and recognizing possible warning signs can have a great impact on the disease, allowing to take prompt action and facilitate a better cancer detection.
As little is known about the role of self-reported health status and specific HRQoL domains in relation to cancer diagnosis, with this proposal, we aim to address this gap, evaluating and possibly identifying novel predictive factors that might contribute to improved cancer risk prediction and a consequent earlier cancer detection.
Data analyses will be based on a major health resource, i.e., the UK Biobank, which currently comprises a cohort of 500.000 adult participants with data available openly for researchers.