Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI Extension

The SPIRIT 2013 (The Standard Protocol Items: Recommendations for Interventional Trials) statement aims to improve the completeness of clinical trial protocol reporting, by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there is a growing recognition that interventions involving artificial intelligence need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes. The SPIRIT-AI extension is a new reporting guideline for clinical trials protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI. Both guidelines were developed using a staged consensus process, involving a literature review and expert consultation to generate 26 candidate items, which were consulted on by an international multi-stakeholder group in a 2-stage Delphi survey (103 stakeholders), agreed on in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The SPIRIT-AI extension includes 15 new items, which were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention will be integrated, considerations around the handling of input and output data, the human-AI interaction and analysis of error cases. SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer-reviewers, as well as the general readership, to understand, interpret and critically appraise the design and risk of bias for a planned clinical trial

Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension

The SPIRIT 2013 statement aims to improve the completeness of clinical trial protocol reporting by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes. The SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trial protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 26 candidate items, which were consulted upon by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The SPIRIT-AI extension includes 15 new items that were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention will be integrated, considerations for the handling of input and output data, the human–AI interaction and analysis of error cases. SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer reviewers, as well as the general readership, to understand, interpret, and critically appraise the design and risk of bias for a planned clinical trial

Connexin-43 upregulation in micrometastases and tumor vasculature and its role in tumor cell attachment to pulmonary endothelium

<p>Abstract</p> <p>Background</p> <p>The modulation of gap junctional communication between tumor cells and between tumor and vascular endothelial cells during tumorigenesis and metastasis is complex. The notion of a role for loss of gap junctional intercellular communication in tumorigenesis and metastasis has been controversial. While some of the stages of tumorigenesis and metastasis, such as uncontrolled cell division and cellular detachment, would necessitate the loss of intercellular junctions, other stages, such as intravasation, endothelial attachment, and vascularization, likely require increased cell-cell contact. We hypothesized that, in this multi-stage scheme, connexin-43 is centrally involved as a cell adhesion molecule mediating metastatic tumor attachment to the pulmonary endothelium.</p> <p>Methods</p> <p>Tumor cell attachment to pulmonary vasculature, tumor growth, and connexin-43 expression was studied in metastatic lung tumor sections obtained after tail-vein injection into nude mice of syngeneic breast cancer cell lines, overexpressing wild type connexin-43 or dominant-negatively mutated connexin-43 proteins. High-resolution immunofluorescence microscopy and Western blot analysis was performed using a connexin-43 monoclonal antibody. Calcein Orange Red AM dye transfer by fluorescence imaging was used to evaluate the gap junction function.</p> <p>Results</p> <p>Adhesion of breast cancer cells to the pulmonary endothelium increased with cancer cells overexpressing connexin-43 and markedly decreased with cells expressing dominant-negative connexin-43. Upregulation of connexin-43 was observed in tumor cell-endothelial cell contact areas <it>in vitro </it>and <it>in vivo</it>, and in areas of intratumor blood vessels and in micrometastatic foci.</p> <p>Conclusion</p> <p>Connexin-43 facilitates metastatic 'homing' by increasing adhesion of cancer cells to the lung endothelial cells. The marked upregulation of connexin-43 in tumor cell-endothelial cell contact areas, whether in preexisting 'homing' vessels or in newly formed tumor vessels, suggests that connexin-43 can serve as a potential marker of micrometastases and tumor vasculature and that it may play a role in the early incorporation of endothelial cells into small tumors as seeds for vasculogenesis.</p

Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI Extension

The SPIRIT 2013 (The Standard Protocol Items: Recommendations for Interventional Trials) statement aims to improve the completeness of clinical trial protocol reporting, by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there is a growing recognition that interventions involving artificial intelligence need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes. The SPIRIT-AI extension is a new reporting guideline for clinical trials protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI. Both guidelines were developed using a staged consensus process, involving a literature review and expert consultation to generate 26 candidate items, which were consulted on by an international multi-stakeholder group in a 2-stage Delphi survey (103 stakeholders), agreed on in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The SPIRIT-AI extension includes 15 new items, which were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention will be integrated, considerations around the handling of input and output data, the human-AI interaction and analysis of error cases. SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer-reviewers, as well as the general readership, to understand, interpret and critically appraise the design and risk of bias for a planned clinical trial

Recommendations for data monitoring committees from the Clinical Trials Transformation Initiative.

Background/aims Use of data monitoring committees to oversee clinical trials was first proposed nearly 50 years ago. Since then, data monitoring committee use in clinical trials has increased and evolved. Nonetheless, there are no well-defined criteria for determining the need for a data monitoring committee, and considerable variability exists in data monitoring committee composition and conduct. To understand and describe the role and function of data monitoring committees, and establish best practices for data monitoring committee trial oversight, the Clinical Trials Transformation Initiative-a public-private partnership to improve clinical trials-launched a multi-stakeholder project. Methods The data monitoring committee project team included 16 individuals charged with (1) clarifying the purpose of data monitoring committees, (2) identifying best practices for independent data monitoring committee conduct, (3) describing effective communication practices, and (4) developing strategies for training data monitoring committee members. Evidence gathering included a survey, a series of focus group discussions, and a 2-day expert meeting aimed at achieving consensus opinions that form the foundation of our data monitoring committee recommendations. Results We define the role of the data monitoring committee as an advisor to the research sponsor on whether to continue, modify, or terminate a trial based on periodic assessment of trial data. Data monitoring committees should remain independent from the sponsor and be composed of members with no relevant conflicts of interest. Representation on a data monitoring committee generally should include at least one clinician with expertise in the therapeutic area being studied, a biostatistician, and a designated chairperson who has experience with clinical trials and data monitoring. Data monitoring committee meetings are held periodically to evaluate the unmasked data from ongoing trials, but the content and conduct of meetings may vary depending on specific goals or topics for deliberation. To guide data monitoring committee conduct and communication plans, a charter consistent with the protocols research design and statistical analysis plan should be developed and agreed upon by the sponsor and the data monitoring committee prior to patient enrollment. We recommend concise and flexible charters that explain roles, responsibilities, operational issues, and how data monitoring committee recommendations are generated and communicated. The demand for data monitoring committee members appears to exceed the current pool of qualified individuals. To prepare a new generation of trained data monitoring committee members, we encourage a combination of didactic educational programs, practical experience, and skill development through apprenticeships and mentoring by experienced data monitoring committee members. Conclusion Our recommendations address data monitoring committee use, conduct, communication practices, and member preparation and training. Furthermore recommendations form the foundation for ongoing efforts to improve clinical trial oversight and enhance the safety and integrity of clinical research. These recommendations serve as a call to action for implementation of best practices that benefit study participants, study sponsors, and society