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1. Advancing oncology with federated learning: transcending boundaries in breast, lung, and prostate cancer. A systematic review

Author

Ankolekar, Anshu, Boie, Sebastian, Abdollahyan, Maryam, Gadaleta, Emanuela, Hasheminasab, Seyed Alireza, Yang, Guang, Beauville, Charles, Dikaios, Nikolaos, Kastis, George Anthony, Bussmann, Michael, Khalid, Sara, Kruger, Hagen, Lambin, Philippe, and Papanastasiou, Giorgos

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computational Engineering, Finance, and Science, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Federated Learning (FL) has emerged as a promising solution to address the limitations of centralised machine learning (ML) in oncology, particularly in overcoming privacy concerns and harnessing the power of diverse, multi-center data. This systematic review synthesises current knowledge on the state-of-the-art FL in oncology, focusing on breast, lung, and prostate cancer. Distinct from previous surveys, our comprehensive review critically evaluates the real-world implementation and impact of FL on cancer care, demonstrating its effectiveness in enhancing ML generalisability, performance and data privacy in clinical settings and data. We evaluated state-of-the-art advances in FL, demonstrating its growing adoption amid tightening data privacy regulations. FL outperformed centralised ML in 15 out of the 25 studies reviewed, spanning diverse ML models and clinical applications, and facilitating integration of multi-modal information for precision medicine. Despite the current challenges identified in reproducibility, standardisation and methodology across studies, the demonstrable benefits of FL in harnessing real-world data and addressing clinical needs highlight its significant potential for advancing cancer research. We propose that future research should focus on addressing these limitations and investigating further advanced FL methods, to fully harness data diversity and realise the transformative power of cutting-edge FL in cancer care., Comment: 5 Figures, 3 Tables, 1 Supplementary Table

Published
2024

3. Nine Recommendations for Decision Aid Implementation from the Clinician Perspective

Author

Ankolekar, Anshu, Vanneste, Ben G. L., Gurp, Esther Bloemen-van, van Roermund, Joep, Berlanga, Adriana, Roumen, Cheryl, van Limbergen, Evert, Lutgens, Ludy, Marcelissen, Tom, Lambin, Philippe, Dekker, Andre, and Fijten, Rianne

Subjects
Computer Science - Computers and Society, J.3
Abstract

Background: Shared decision-making (SDM) aims to empower patients to take an active role in their treatment choices, supported by clinicians and patient decision aids (PDAs). The purpose of this study is to explore barriers and possible facilitators to SDM and a PDA in the prostate cancer trajectory. In the process we identify possible actions that organizations and individuals can take to support implementation in practice. Methods: We use the Ottawa Model of Research Use as a framework to determine the barriers and facilitators to SDM and PDAs from the perspective of clinicians. Semi-structured interviews were conducted with urologists (n=4), radiation oncologists (n=3), and oncology nurses (n=2), focusing on the current decision-making process experienced by these stakeholders. Questions included their attitudes towards SDM and PDAs, barriers to implementation and possible strategies to overcome them. Results: Time pressure and patient characteristics were cited as major barriers by 55% of the clinicians we interviewed. Structural factors such as external quotas for certain treatment procedures were also considered as barriers by 44% of the clinicians. Facilitating factors involved organizational changes to em-bed PDAs in the treatment trajectory, training in using PDAs as a tool for SDM, and clinician motivation by disseminating positive clinical outcomes. Our findings also suggest a role for external stakeholders such as healthcare insurers in creating economic incentives to facilitate implementation. Conclusion: Our findings highlight the importance of a multi-faceted implementation strategy to support SDM. While clinician motivation and patient activation are essential, structural/economic barriers may hamper implementation. Action must also be taken at the administrative and policy levels to foster a collaborative environment for SDM and, in the process, for PDAs.

Published
2020

5. Shared decision-making in oncology: challenges and opportunities

Author

Ankolekar, Anshu, Ankolekar, Anshu, Ankolekar, Anshu, and Ankolekar, Anshu

Abstract

Newly diagnosed cancer patients often face multiple treatment options (such as chemotherapy, surgery, radiation therapy) each with their own advantages and disadvantages. Treatment choices are no longer driven purely by clinicians’ expertise but increasingly by patients’ individual values and preferences as well. This collaborative process between the two is known as shared decision-making (SDM) sometimes supported by information tools such as patient decision aids (PDAs). Implementation of SDM and PDAs often falters in practice due to a lack of attention to user perspectives and workflows. For this thesis, patients, clinicians, and nurses were interviewed to understand how they experience the decision-making process and how patient-clinician communication can be supported. Based on the results, a PDA for prostate cancer patients was developed. Also, SDM for lung cancer patients was implemented and evaluated. These findings shed light on the current clinical trajectory and the behavioural and organizational factors that influence SDM implementation success.

Published
2021

6. Clinician perspectives on clinical decision support systems in lung cancer:Implications for shared decision-making

Author

Ankolekar, Anshu, van der Heijden, Britt, Dekker, Andre, Roumen, Cheryl, De Ruysscher, Dirk, Reymen, Bart, Berlanga, Adriana, Oberije, Cary, Fijten, Rianne, Ankolekar, Anshu, van der Heijden, Britt, Dekker, Andre, Roumen, Cheryl, De Ruysscher, Dirk, Reymen, Bart, Berlanga, Adriana, Oberije, Cary, and Fijten, Rianne

Abstract

Background: Lung cancer treatment decisions are typically made among clinical experts in a multidisciplinary tumour board (MTB) based on clinical data and guidelines. The rise of artificial intelligence and cultural shifts towards patient autonomy are changing the nature of clinical decision-making towards personalized treatments. This can be supported by clinical decision support systems (CDSSs) that generate personalized treatment information as a basis for shared decision-making (SDM). Little is known about lung cancer patients' treatment decisions and the potential for SDM supported by CDSSs. The aim of this study is to understand to what extent SDM is done in current practice and what clinicians need to improve it. Objective: To explore (1) the extent to which patient preferences are taken into consideration in non-small-cell lung cancer (NSCLC) treatment decisions; (2) clinician perspectives on using CDSSs to support SDM. Design: Mixed methods study consisting of a retrospective cohort study on patient deviation from MTB advice and reasons for deviation, qualitative interviews with lung cancer specialists and observations of MTB discussions and patient consultations. Setting and Participants: NSCLC patients (N = 257) treated at a single radiotherapy clinic and nine lung cancer specialists from six Dutch clinics. Results: We found a 10.9% (n = 28) deviation rate from MTB advice; 50% (n = 14) were due to patient preference, of which 85.7% (n = 12) chose a less intensive treatment than MTB advice. Current MTB recommendations are based on clinician experience, guidelines and patients' performance status. Most specialists (n = 7) were receptive towards CDSSs but cited barriers, such as lack of trust, lack of validation studies and time. CDSSs were considered valuable during MTB discussions rather than in consultations. Conclusion: Lung cancer decisions are heavily influenced by clinical guidelines and experience, yet many patients prefer less intensive treatment

Published
2022

10. The Benefits and Challenges of Using Patient Decision Aids to Support Shared Decision Making in Health Care

Author

Ankolekar, Anshu, Ankolekar, Anshu, Dekker, Andre, Fijten, Rianne, Berlanga, Adriana, Ankolekar, Anshu, Ankolekar, Anshu, Dekker, Andre, Fijten, Rianne, and Berlanga, Adriana

Abstract

Shared decision making (SDM) and patient-centered care require patients to actively participate in the decision-making process. Yet with the increasing number and complexity of cancer treatment options, it can be a challenge for patients to evaluate clinical information and make risk-benefit trade-offs to choose the most appropriate treatment. Clinicians face time constraints and communication challenges, which can further hamper the SDM process. In this article, we review patient decision aids (PDAs) as a means of supporting SDM by presenting clinical information and risk data to patients in a format that is accessible and easy to understand. We outline the benefits and limitations of PDAs as well as the challenges in their development, such as a lengthy and complex development process and implementation obstacles. Lastly, we discuss future trends and how change on multiple levels-PDA developers, clinicians, hospital administrators, and health care insurers-can support the use of PDAs and consequently SDM. Through this multipronged approach, patients can be empowered to take an active role in their health and choose treatments that are in line with their values. (C) 2018 by American Society of Clinical Oncology

Published
2018

11. Implications of Clinicians' Attitudes Towards Clinical Decision Support Systems

Author

Ankolekar, Anshu, van der Heijden, Britt, Dekker, Andre, Roumen, Cheryl, de Ruysscher, Dirk, Reymen, Bart, Houben, Ruud, Fick, Peter, Puts, Sander, Veugen, Joeri, Berlanga, Adriana, Oberije, Cary, Fijten, Rianne, PapeHaugaard, LB, Lovis, C, Madsen, IC, Weber, P, Nielsen, PH, Scott, P, Radiotherapie, RS: GROW - R3 - Innovative Cancer Diagnostics & Therapy, and Precision Medicine

Subjects
clinical decision support, lung cancer, Attitude of Health Personnel, decision aids, Physicians, Decision Making, Humans, Patient Participation, Decision Support Systems, Clinical, Shared decision making
Published
2020

12. Improving shared decision making for lung cancer treatment by developing and validating an open-source web based patient decision aid for stage I-II non-small cell lung cancer.

Author

Halilaj I, Ankolekar A, Lenaers A, Chatterjee A, Oberije CJG, Eppings L, Smit HJM, Hendriks LEL, Jochems A, Lieverse RIY, van Timmeren JE, Wind A, and Lambin P

Abstract

The aim of this study was to develop and evaluate a proof-of-concept open-source individualized Patient Decision Aid (iPDA) with a group of patients, physicians, and computer scientists. The iPDA was developed based on the International Patient Decision Aid Standards (IPDAS). A previously published questionnaire was adapted and used to test the user-friendliness and content of the iPDA. The questionnaire contained 40 multiple-choice questions, and answers were given on a 5-point Likert Scale (1-5) ranging from "strongly disagree" to "strongly agree." In addition to the questionnaire, semi-structured interviews were conducted with patients. We performed a descriptive analysis of the responses. The iPDA was evaluated by 28 computer scientists, 21 physicians, and 13 patients. The results demonstrate that the iPDA was found valuable by 92% (patients), 96% (computer scientists), and 86% (physicians), while the treatment information was judged useful by 92%, 96%, and 95%, respectively. Additionally, the tool was thought to be motivating for patients to actively engage in their treatment by 92%, 93%, and 91% of the above respondents groups. More multimedia components and less text were suggested by the respondents as ways to improve the tool and user interface. In conclusion, we successfully developed and tested an iPDA for patients with stage I-II Non-Small Cell Lung Cancer (NSCLC)., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Halilaj, Ankolekar, Lenaers, Chatterjee, Oberije, Eppings, Smit, Hendriks, Jochems, Lieverse, van Timmeren, Wind and Lambin.)

Published
2024
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