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- Witjes, J. Alfred, et al.
(författare)
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EAU-ESMO Consensus Statements on the Management of Advanced and Variant Bladder Cancer – An International Collaborative Multistakeholder Effort : Under the Auspices of the EAU-ESMO Guidelines Committees
- 2020
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Ingår i: European Urology. - : Elsevier. - 0302-2838 .- 1873-7560. ; 77:2, s. 223-250
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Although guidelines exist for advanced and variant bladder cancer management, evidence is limited/conflicting in some areas and the optimal approach remains controversial.OBJECTIVE: To bring together a large multidisciplinary group of experts to develop consensus statements on controversial topics in bladder cancer management.DESIGN: A steering committee compiled proposed statements regarding advanced and variant bladder cancer management which were assessed by 113 experts in a Delphi survey. Statements not reaching consensus were reviewed; those prioritised were revised by a panel of 45 experts prior to voting during a consensus conference.SETTING: Online Delphi survey and consensus conference.PARTICIPANTS: The European Association of Urology (EAU), the European Society for Medical Oncology (ESMO), experts in bladder cancer management.OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Statements were ranked by experts according to their level of agreement: 1-3 (disagree), 4-6 (equivocal), and 7-9 (agree). A priori (level 1) consensus was defined as ≥70% agreement and ≤15% disagreement, or vice versa. In the Delphi survey, a second analysis was restricted to stakeholder group(s) considered to have adequate expertise relating to each statement (to achieve level 2 consensus).RESULTS AND LIMITATIONS: Overall, 116 statements were included in the Delphi survey. Of these statements, 33 (28%) achieved level 1 consensus and 49 (42%) achieved level 1 or 2 consensus. At the consensus conference, 22 of 27 (81%) statements achieved consensus. These consensus statements provide further guidance across a broad range of topics, including the management of variant histologies, the role/limitations of prognostic biomarkers in clinical decision making, bladder preservation strategies, modern radiotherapy techniques, the management of oligometastatic disease, and the evolving role of checkpoint inhibitor therapy in metastatic disease.CONCLUSIONS: These consensus statements provide further guidance on controversial topics in advanced and variant bladder cancer management until a time when further evidence is available to guide our approach.PATIENT SUMMARY: This report summarises findings from an international, multistakeholder project organised by the EAU and ESMO. In this project, a steering committee identified areas of bladder cancer management where there is currently no good-quality evidence to guide treatment decisions. From this, they developed a series of proposed statements, 71 of which achieved consensus by a large group of experts in the field of bladder cancer. It is anticipated that these statements will provide further guidance to health care professionals and could help improve patient outcomes until a time when good-quality evidence is available.
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| 2. |
- Marti-Bonmati, Luis, et al.
(författare)
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Considerations for artificial intelligence clinical impact in oncologic imaging : an AI4HI position paper
- 2022
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Ingår i: Insights into Imaging. - : Springer. - 1869-4101. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- To achieve clinical impact in daily oncological practice, emerging AI-based cancer imaging research needs to have clearly defined medical focus, AI methods, and outcomes to be estimated. AI-supported cancer imaging should predict major relevant clinical endpoints, aiming to extract associations and draw inferences in a fair, robust, and trustworthy way. AI-assisted solutions as medical devices, developed using multicenter heterogeneous datasets, should be targeted to have an impact on the clinical care pathway. When designing an AI-based research study in oncologic imaging, ensuring clinical impact in AI solutions requires careful consideration of key aspects, including target population selection, sample size definition, standards, and common data elements utilization, balanced dataset splitting, appropriate validation methodology, adequate ground truth, and careful selection of clinical endpoints. Endpoints may be pathology hallmarks, disease behavior, treatment response, or patient prognosis. Ensuring ethical, safety, and privacy considerations are also mandatory before clinical validation is performed. The Artificial Intelligence for Health Imaging (AI4HI) Clinical Working Group has discussed and present in this paper some indicative Machine Learning (ML) enabled decision-support solutions currently under research in the AI4HI projects, as well as the main considerations and requirements that AI solutions should have from a clinical perspective, which can be adopted into clinical practice. If effectively designed, implemented, and validated, cancer imaging AI-supported tools will have the potential to revolutionize the field of precision medicine in oncology.
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