Deciding to Enrol in a Cancer Trial: A Systematic Review of Qualitative Studies.

Background:Clinical trials are essential for the advancement of cancer treatments; however, participation by patients is suboptimal. Currently, there is a lack of synthesized qualitative review evidence on the patient experience of trial entry from which to further develop decision support. The aim of this review is to synthesise literature reporting experiences of participants when deciding to enrol in a cancer clinical trial in order to inform practice. Methods:A systematic review and meta-synthesis of qualitative studies were conducted to describe the experiences of adult cancer patients who decided to enrol in a clinical trial of an anti-cancer treatment. Results:Forty studies met eligibility criteria for inclusion. Three themes were identified representing the overarching domains of experience when deciding to enrol in a cancer trial: 1) need for trial information; (2) trepidation towards participation; and (3) justifying the decision. The process of deciding to enrol in a clinical trial is one marked by uncertainty, emotional distress and driven by the search for a cure. Conclusion:Findings from this review show that decision support modelled by shared decision-making and the quality of a shared decision needs to be accompanied by tailored or personalised psychosocial and supportive care. Although the decision process bears similarities to theoretical processes outlined in decision-making frameworks, there are a lack of supportive interventions for cancer patients that are adapted to the clinical trial context. Theory-based interventions are urgently required to support the specific needs of patients deciding whether to participate in cancer trials

Deciding to enrol in a cancer trial: A systematic review of qualitative studies

© 2020 Viljoen et al. Background: Clinical trials are essential for the advancement of cancer treatments; how-ever, participation by patients is suboptimal. Currently, there is a lack of synthesized qualitative review evidence on the patient experience of trial entry from which to further develop decision support. The aim of this review is to synthesise literature reporting experiences of participants when deciding to enrol in a cancer clinical trial in order to inform practice. Methods: A systematic review and meta-synthesis of qualitative studies were conducted to describe the experiences of adult cancer patients who decided to enrol in a clinical trial of an anti-cancer treatment. Results: Forty studies met eligibility criteria for inclusion. Three themes were identified representing the overarching domains of experience when deciding to enrol in a cancer trial: 1) need for trial information; (2) trepidation towards participation; and (3) justifying the decision. The process of deciding to enrol in a clinical trial is one marked by uncertainty, emotional distress and driven by the search for a cure. Conclusion: Findings from this review show that decision support modelled by shared decision-making and the quality of a shared decision needs to be accompanied by tailored or personalised psychosocial and supportive care. Although the decision process bears simila-rities to theoretical processes outlined in decision-making frameworks, there are a lack of supportive interventions for cancer patients that are adapted to the clinical trial context. Theory-based interventions are urgently required to support the specific needs of patients deciding whether to participate in cancer trials

Machine learning application in personalised lung cancer recurrence and survivability prediction

Machine learning is an important artificial intelligence technique that is widely applied in cancer diagnosis and detection. More recently, with the rise of personalised and precision medicine, there is a growing trend towards machine learning applications for prognosis prediction. However, to date, building reliable prediction models of cancer outcomes in everyday clinical practice is still a hurdle. In this work, we integrate genomic, clinical and demographic data of lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) patients from The Cancer Genome Atlas (TCGA) and introduce copy number variation (CNV) and mutation information of 15 selected genes to generate predictive models for recurrence and survivability. We compare the accuracy and benefits of three well-established machine learning algorithms: decision tree methods, neural networks and support vector machines. Although the accuracy of predictive models using the decision tree method has no significant advantage, the tree models reveal the most important predictors among genomic information (e.g. KRAS, EGFR, TP53), clinical status (e.g. TNM stage and radiotherapy) and demographics (e.g. age and gender) and how they influence the prediction of recurrence and survivability for both early stage LUAD and LUSC. The machine learning models have the potential to help clinicians to make personalised decisions on aspects such as follow-up timeline and to assist with personalised planning of future social care needs

The Role of Personalised Choice in Decision Support: A Randomized Controlled Trial of an Online Decision Aid for Prostate Cancer Screening.

IMPORTANCE: Decision support tools can assist people to apply population-based evidence on benefits and harms to individual health decisions. A key question is whether "personalising" choice within decisions aids leads to better decision quality. OBJECTIVE: To assess the effect of personalising the content of a decision aid for prostate cancer screening using the Prostate Specific Antigen (PSA) test. DESIGN: Randomized controlled trial. SETTING: Australia. PARTICIPANTS: 1,970 men aged 40-69 years were approached to participate in the trial. INTERVENTION: 1,447 men were randomly allocated to either a standard decision aid with a fixed set of five attributes or a personalised decision aid with choice over the inclusion of up to 10 attributes. OUTCOME MEASURES: To determine whether there was a difference between the two groups in terms of: 1) the emergent opinion (generated by the decision aid) to have a PSA test or not; 2) self-rated decision quality after completing the online decision aid; 3) their intention to undergo screening in the next 12 months. We also wanted to determine whether men in the personalised choice group made use of the extra decision attributes. RESULTS: 5% of men in the fixed attribute group scored 'Have a PSA test' as the opinion generated by the aid, as compared to 62% of men in the personalised choice group (χ2 = 569.38, 2df, p< 0001). Those men who used the personalised decision aid had slightly higher decision quality (t = 2.157, df = 1444, p = 0.031). The men in the personalised choice group made extensive use of the additional decision attributes. There was no difference between the two groups in terms of their stated intention to undergo screening in the next 12 months. CONCLUSIONS: Together, these findings suggest that personalised decision support systems could be an important development in shared decision-making and patient-centered care. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12612000723886

iManageMyHealth and iSupportMyPatients: mobile decision support and health management apps for cancer patients and their doctors

Clinical decision support systems can play a crucial role in healthcare delivery as they promise to improve health outcomes and patient safety, reduce medical errors and costs and contribute to patient satisfaction. Used in an optimal way, they increase the quality of healthcare by proposing the right information and intervention to the right person at the right time in the healthcare delivery process. This paper reports on a specific approach to integrated clinical decision support and patient guidance in the cancer domain as proposed by the H2020 iManageCancer project. This project aims at facilitating efficient self-management and management of cancer according to the latest available clinical knowledge and the local healthcare delivery model, supporting patients and their healthcare providers in making informed decisions on treatment choices and in managing the side effects of their therapy. The iManageCancer platform is a comprehensive platform of interconnected mobile tools to empower cancer patients and to support them in the management of their disease in collaboration with their doctors. The backbone of the iManageCancer platform comprises a personal health record and the central decision support unit (CDSU). The latter offers dedicated services to the end users in combination with the apps iManageMyHealth and iSupportMyPatients. The CDSU itself is composed of the so-called Care Flow Engine (CFE) and the model repository framework (MRF). The CFE executes personalised and workflow oriented formal disease management diagrams (Care Flows). In decision points of such a Care Flow, rules that operate on actual health information of the patient decide on the treatment path that the system follows. Alternatively, the system can also invoke a predictive model of the MRF to proceed with the best treatment path in the diagram. Care Flow diagrams are designed by clinical experts with a specific graphical tool that also deploys these diagrams as executable workflows in the CFE following the Business Process Model and Notation (BPMN) standard. They are exposed as services that patients or their doctors can use in their apps in order to manage certain aspects of the cancer disease like pain, fatigue or the monitoring of chemotherapies at home. The mHealth platform for cancer patients is currently being assessed in clinical pilots in Italy and Germany and in several end-user workshops

Machine learning application in personalised lung cancer recurrence and survivability prediction

Copyright © 2022 The Authors. Machine learning is an important artificial intelligence technique that is widely applied in cancer diagnosis and detection. More recently, with the rise of personalised and precision medicine, there is a growing trend towards machine learning applications for prognosis prediction. However, to date, building reliable prediction models of cancer outcomes in everyday clinical practice is still a hurdle. In this work, we integrate genomic, clinical and demographic data of lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) patients from The Cancer Genome Atlas (TCGA) and introduce copy number variation (CNV) and mutation information of 15 selected genes to generate predictive models for recurrence and survivability. We compare the accuracy and benefits of three well-established machine learning algorithms: decision tree methods, neural networks and support vector machines. Although the accuracy of predictive models using the decision tree method has no significant advantage, the tree models reveal the most important predictors among genomic information (e.g. KRAS, EGFR, TP53), clinical status (e.g. TNM stage and radiotherapy) and demographics (e.g. age and gender) and how they influence the prediction of recurrence and survivability for both early stage LUAD and LUSC. The machine learning models have the potential to help clinicians to make personalised decisions on aspects such as follow-up timeline and to assist with personalised planning of future social care needs.Funding from the UK Engineering & Physical Sciences Research Council (EPSRC) for the Future Targeted Healthcare Manufacturing Hub hosted at University College London with UK university partners is gratefully acknowledged (Grant Reference: EP/P006485/1). Financial and in-kind support from the consortium of industrial users and sector organisations is also acknowledged

Patient-centric trials for therapeutic development in precision oncology

An enhanced understanding of the molecular pathology of disease gained from genomic studies is facilitating the development of treatments that target discrete molecular subclasses of tumours. Considerable associated challenges include how to advance and implement targeted drug-development strategies. Precision medicine centres on delivering the most appropriate therapy to a patient on the basis of clinical and molecular features of their disease. The development of therapeutic agents that target molecular mechanisms is driving innovation in clinical-trial strategies. Although progress has been made, modifications to existing core paradigms in oncology drug development will be required to realize fully the promise of precision medicine