A systematic review of the barriers and facilitators impacting patient enrolment in clinical trials for lung cancer.

PURPOSE: Clinical research trials are needed to enhance the medical care and treatment for lung cancer, which remains the leading cause of cancer-related deaths worldwide. While clinical trials allow for the development of novel therapies to treat cancer, the recruitment of lung cancer patients to trials is low. This review aimed to identify and synthesise the available literature concerning barriers and facilitators affecting lung cancer patients' decisions to enrol in clinical trials to guide future cancer research efforts. METHODS: Four databases were systematically searched: Academic Search Complete, CINHAL, PubMed, and PsycINFO in August 2023. A supplemental grey literature search was also conducted alongside this. Articles were quality appraised using CASP and JMI checklists, and results were narratively synthesised. RESULTS: Eighteen articles of varied design met the inclusion criteria, and results were mapped onto the Capability, Opportunity, and Motivation Behaviour (COM-B) Model to help structure and conceptualise review findings. Evidence suggests that the decision to enrol in a trial is multifaceted and informed by: when and how study information is presented, travel and trial eligibility, and altruistic hopes and fears. CONCLUSIONS: There is need to address the many different concerns that lung cancer patients have about participating in a clinical trial through the supply of accessible and timely trial information, and via the reduction of travel, expansion of study eligibility criteria, and recognition of a person's altruistic wishes, hopes, fears, and family-oriented concerns. Future research should aim to work alongside lung cancer patients, clinicians, and other stakeholders to increase research accessibility.

Determining the impact of an artificial intelligence tool on the management of pulmonary nodules detected incidentally on CT (DOLCE) study protocol: a prospective, non-interventional multicentre UK study.

INTRODUCTION: In a small percentage of patients, pulmonary nodules found on CT scans are early lung cancers. Lung cancer detected at an early stage has a much better prognosis. The British Thoracic Society guideline on managing pulmonary nodules recommends using multivariable malignancy risk prediction models to assist in management. While these guidelines seem to be effective in clinical practice, recent data suggest that artificial intelligence (AI)-based malignant-nodule prediction solutions might outperform existing models. METHODS AND ANALYSIS: This study is a prospective, observational multicentre study to assess the clinical utility of an AI-assisted CT-based lung cancer prediction tool (LCP) for managing incidental solid and part solid pulmonary nodule patients vs standard care. Two thousand patients will be recruited from 12 different UK hospitals. The primary outcome is the difference between standard care and LCP-guided care in terms of the rate of benign nodules and patients with cancer discharged straight after the assessment of the baseline CT scan. Secondary outcomes investigate adherence to clinical guidelines, other measures of changes to clinical management, patient outcomes and cost-effectiveness. ETHICS AND DISSEMINATION: This study has been reviewed and given a favourable opinion by the South Central-Oxford C Research Ethics Committee in UK (REC reference number: 22/SC/0142).Study results will be available publicly following peer-reviewed publication in open-access journals. A patient and public involvement group workshop is planned before the study results are available to discuss best methods to disseminate the results. Study results will also be fed back to participating organisations to inform training and procurement activities. TRIAL REGISTRATION NUMBER: NCT05389774.