Global Management of Sarcoma Data: Is Real-Time Predictive Analytics on the Horizon?
A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Informatics and Big Data".
Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 15246
Special Issue Editor
Interests: sarcoma surgery; sarcoma MDT; quality indicators; VBHC
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Sarcomas belong to the rare diseases, there are some 150 molecularly defined biological subtypes, and its treatment is highly complex and transdisciplinary. The development of novel therapies has been stagnating over years for numerous complex reasons. Although it is accepted that only the interplay of several disciplines may render successful treatment results in sarcoma patients, data are usually still mono-disciplinarily reported, and there is no global approach for longitudinal prospective acquisition of structured data on quality. The successful introduction of the “Sarculator” and “PerSarc” -though still imperfect tools- has demonstrated the urge how clinicians starve for outcome prediction tools to incorporate into their strategic treatment decisions. Whereas real-time outcome prediction for sarcoma patients is the defined goal, we have to define the approach how to get there on a global level considering all possible aspects.
A first step includes the establishment of an overview what is available to then possibly orchestrate on the global level, and to outline the opportunities.
Herewith, we aim for a current global representation of sarcoma registries available within the existing sarcoma networks with detailed overview how they work. Further, we want to consider the novel opportunities of digital transformation and challenges of international data exchange and analytics.
Prof. Dr. Bruno Fuchs
Guest Editor
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Keywords
- sarcoma
- registry/data bases
- data harmonization and exchange
- global collaboration
- digital transformation
- predictive outcome analytics
- prognosis & prognostic research
- prognostic and predictive models
- predictive analytics & machine learning
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