Abstract 1218P
Background
Cancer is considered a leading cause of mortality and morbidity worldwide. This study constitutes one part of the user requirement definition of INCISIVE EU project. The project has been designed to explore the full potential of artificial intelligence (AI)-based technologies in cancer imaging. The study aimed to map cancer care pathways (breast, prostate, colorectal and lung cancers) across INCISIVE partner countries, and identify obstacles within these pathways.
Methods
A qualitative research approach employing email interviews was used. A purposive sampling strategy was employed to recruit ten oncology specialised healthcare professionals from INCISIVE partner countries: Greece, Cyprus, Spain, Italy, Finland, United Kingdom (UK) and Serbia. Data was collected between December 2020 and April 2021. Data was entered into Microsoft Excel spreadsheet to allow content and comparative analysis. Appropriate ethical approval was obtained for this study.
Results
Delays in the diagnosis and treatment of cancer was evident from all the pathways studied. With the exception of the UK, none of the countries studied had official national data regarding delays in cancer diagnosis and treatment. There was a considerable variation in the availability of imaging and diagnostic services across the seven countries that were analysed. Several concerns were also noted for national screening for the four investigated cancer types.
Conclusions
Delays in the diagnosis and treatment of cancer remain challenging issues that need to be addressed. To effectively address these challenges, it is crucial to have a systematic reporting of diagnostic and therapeutic delays in all countries. Proper estimation of the magnitude of the problem is essential, as no problem can be effectively tackled without an accurate understanding of its magnitude. Our findings also support the orientation of the current policies towards early detection and wide scale adoption and implementation of cancer screening, through research, innovation, and technology. Technologies involving AI can have a great potential to revolutionise cancer care delivery.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
INCISIVE Consortium.
Funding
EU Horizion 2020.
Disclosure
All authors have declared no conflicts of interest.
Resources from the same session
1230P - hPG80 (circulating progastrin) is a new blood-based biomarker for diagnosis of early-stage non-small cell lung cancers
Presenter: Paul Hofman
Session: Poster session 14
1231P - Machine learning prediction of the case-fatality of COVID-19 and risk factors for adverse outcomes in patients with non-small cell lung cancer
Presenter: Yeji Jung
Session: Poster session 14
1232P - Analytic analysis of PanSeer7, a targeted bisulfite sequencing assay for blood-based multi-cancer detection for cancer early detection and tissue-of-origin identification
Presenter: Xinrong Yang
Session: Poster session 14
1233P - Accurate prediction of gastrointestinal cancer tissue of origin using comprehensive plasma cell-free DNA fragmentomics features
Presenter: Xinrong Yang
Session: Poster session 14
1234P - HistoMate: Automated preprocessing software for digital histopathology image to enhance deep learning
Presenter: Jinok Lee
Session: Poster session 14
1235P - Enrichment of rare cancers in pragmatic precision cancer medicine trial: Experience from IMPRESS-Norway
Presenter: Aaslaug Helland
Session: Poster session 14
1236P - Feasibility of online symptom monitoring to detect lung cancer relapse in Poland
Presenter: Ewa Pawlowska
Session: Poster session 14
1237P - Design and validation of a custom next-generation sequencing panel in melanoma, glioma and gastrointestinal stromal tumor
Presenter: Xiaoyan Zhou
Session: Poster session 14
1238P - Detecting driver mutations by AmoyDx 11-gene PCR with high concordance with next-generation sequencing in Chinese non-small cell lung cancer patients
Presenter: Dongmei Lin
Session: Poster session 14
1239P - NHS-Galleri trial enrolment approaches and participant sociodemographic diversity
Presenter: Charles Swanton
Session: Poster session 14