Core Patient-Centered Outcomes for Adolescents and Young Adults with Cancer: A Comprehensive Review of the Literature from the STRONG-AYA Project

Silvie H. M. Janssen, Winette T. A. van der Graaf, Anna Hurley-Wallace, Carla Vlooswijk, Catarina S. Padilla, Charlotte Cairns, Connor J. Tyler, Emily I. Holthuis, Gudrun E. Rohde, et al

Cancer, 17(3), 454 (2025). DOI

This review provides an overview of relevant outcomes of AYAs with cancer in order to develop a core outcome set (COS: an agreed standardized collection of outcomes) for this population. A literature search was conducted, after which articles were reviewed by two independent researchers using Rayyan to screen articles by their title/abstract and their full text. The data were extracted using a tailored extraction framework by a team of reviewers. A total of 1631 of the 17,301 screened articles were included. Of the five core areas, functioning (47.0%) and epidemiology (44.9%) were covered most often, followed by physiological/clinical (42.4%), resource use (6.1%), and adverse events (4.5%). The most represented outcome domains include mortality/survival, delivery of care, outcomes relating to neoplasms, and emotional functioning/well-being. This literature review provides a foundation for the development of an AYA-specific COS to improve the relevance and efficiency of measuring outcomes, the pooling of (international) data, and the value of care for AYAs with cancer.

From Federated Learning to Publicly Facing Platform; Proof-of-Concept of the STRONG AYA Information Portal

Joshi Hogenboom, Aiara Lobo Gomes, Andre Dekker, Winette van der Graaf, Olga Husson, Leonard Wee

IOS Press 327, 591-592 (2025). DOI

Information on health data is often shrouded in exclusivity and sharing of such information is normally difficult and time-locked for numerous reasons. Federated learning (FL) regularly accelerates knowledge sharing, yet it is not fully exploited to alleviate overall data exclusivity and time constraints. Here, we present a proof-of-concept for a public FL-based info portal of the STRONG AYA Initiative.

Getting real about synthetic data ethics: Are AI ethics principles a good starting point for synthetic data ethics?

Danielle Shanley, Joshi Hogenboom, Flora Lysen, Leonard Wee, Aiara Lobo Gomes, Andre Dekker, Darian Meacham

EMBO reports 25, 2152-2155 (2024). DOI

The creation of synthetic data (SD) — ‘artificial’ data generated specifically with the goal of resembling ‘real-world’ data in order to replace or reduce the need for ‘real’ data — is booming. SD promises to be a viable alternative when data collection and data sharing may not be feasible or cost-effective. However, the increasingly prevalent use of SD for training and benchmarking in AI development is generating novel and pertinent ethical concerns that should be considered by developers and users of synthetic datasets and AI applications. This article provides an overview of issues arising from the rapid growth in SD generation and use to encourage further reflection and investigation within the research community.

Adolescent and young adult (AYA) patient involvement and engagement in European health care and research projects: expanding the scope of patient advocacy

Urska Košir, Flora Lysen, Nicola Unterecker, Tomaž Deželak, Erik Sturesson, Iryna Shakhnenko, Daniel Stark, Katie Rizvi, Anne-Sophie Darlington, Leonard Wee, Winette T. A. van der Graaf, Olga Husson

ESMO Open 10(3), 104478 (2025). DOI

Patient involvement and engagement (PI&E) in health care and research has gained prominence, shifting towards person-centred approaches and shared decision making. Patients actively participating in health care design and research lead to better quality and efficiency of care. However, implementing meaningful PI&E is challenging and requires adequate resources and evaluation frameworks so that it does not result in tokenism. This is particularly important when considering niche areas like adolescents and young adults (AYAs) with cancer. As AYAs’ unique needs continue to gain recognition, it is becoming increasingly important to incorporate their expertise and diverse perspectives in navigating care. Large-scale European consortia that focus specifically on AYAs offer opportunities to establish successful partnerships with AYAs in the design and creation of the next generation of equitable, diverse, and inclusive cancer care. Concrete actions for meaningful AYA PI&E are discussed.

Protocol for the development of a Core Outcome Set (COS) for Adolescents and Young Adults (AYAs) with cancer

Olga Husson, Silvie H. M. Janssen, Bryce B. Reeve, Samantha C. Sodergren, Christabel K. Cheung, Martin G. McCabe, John M. Salsman, Winette T. A. van der Graaf, Anne-Sophie Darlington

BMC Cancer, 24, 126 (2024). DOI

Background: Adolescents and young adults (AYAs) with cancer, defined as individuals aged 15–39 years at initial cancer diagnosis, form a unique population; they face age-specific issues as they transition to adulthood. This paper presents the protocol for the development of a core outcome set (COS) for AYAs with cancer. Methods: The methodological standards from the Core Outcome Measures in Effectiveness Trials (COMET) and the International Consortium for Health Outcomes Measurement (ICHOM) for COS development will guide the development of the COS for AYAs with cancer. The project will consist of the following phases: (1) define the scope of the COS; (2) establish the need for a COS in this field (3) assemble an international, multi-stakeholder working group; (4) develop a detailed protocol; (5) determine “what to measure” (i.e., outcomes); (6) determine “how to measure” (i.e., measures); and (7) determine “case-mix” variables. Conclusions: The development of a COS for AYAs with cancer will facilitate the implementation of efficient and relevant standards for data collection, both for clinical trials and in routine healthcare, thereby increasing the usefulness of these data to improve the value of the care given to these underserved young cancer patients.

Actionability of Synthetic Data in a Heterogeneous and Rare Health Care Demographic: Adolescents and Young Adults With Cancer

Joshi Hogenboom, Aiara Lobo Gomes, Andre Dekker, Winette Van Der Graaf, Olga Husson, Leonard Wee

JCO Clin Cancer Inform 8, e2400056 (2024). DOI

Research on rare diseases and atypical health care demographics is often slowed by high interparticipant heterogeneity and overall scarcity of data. Synthetic data (SD) have been proposed as means for data sharing, enlargement, and diversification, by artificially generating real phenomena while obscuring the real patient data. The utility of SD is actively scrutinized in health care research, but the role of sample size for actionability of SD is insufficiently explored. We aim to understand the interplay of actionability and sample size by generating SD sets of varying sizes from gradually diminishing amounts of real individuals’ data. We evaluate the actionability of SD in a highly heterogeneous and rare demographic: adolescents and young adults (AYAs) with cancer. Methods: A population-based cross-sectional cohort study of 3,735 AYAs was subsampled at random to produce 13 training data sets of varying sample sizes. We studied four distinct generator architectures built on the open-source Synthetic Data Vault library. Each architecture was used to generate SD of varying sizes on the basis of each aforementioned training subsets. SD actionability was assessed by comparing the resulting SD with their respective real data against three metrics—veracity, utility, and privacy concealment. Results: All examined generator architectures yielded actionable data when generating SD with sizes similar to the real data. Large SD sample size increased veracity but generally increased privacy risks. Using fewer training participants led to faster convergence in veracity, but partially exacerbated privacy concealment issues. Conclusion: SD is a potentially promising option for data sharing and data augmentation, yet sample size plays a significant role in its actionability. SD generation should go hand-in-hand with consistent scrutiny, and sample size should be carefully considered in this process.