Litcius/Paper detail

Patients’ selection and trial matching in early-phase oncology clinical trials

Pauline Corbaux, A. Bayle, Sylvain Besle, Armelle Vinceneux, Hélène Vanacker, K. Ouali, B. Hanvic, Capucine Baldini, Philippe A. Cassier, C. Terret, Loïc Verlingue

2024Critical Reviews in Oncology/Hematology12 citationsDOIOpen Access PDF

Abstract

Early-phase clinical trials (EPCT) represent an important part of innovations in medical oncology and a valuable therapeutic option for patients with metastatic cancers, particularly in the era of precision medicine. Nevertheless, adult patients’ participation in oncology clinical trials is low, ranging from 2% to 8% worldwide, with unequal access, and up to 40% risk of early discontinuation in EPCT, mostly due to cancer-related complications. We review the tools and initiatives to increase patients’ orientation and access to early phase cancer clinical trials, and to limit early discontinuation. New approaches to optimize the early-phase clinical trial referring process in oncology include automatic trial matching, tools to facilitate the estimation of patients' prognostic and/or to better predict patients’ eligibility to clinical trials. Classical and innovative approaches should be associated to double patient recruitment, improve clinical trial enrollment experience and reduce early discontinuation rates. Whereas EPCT are essential for patients to access the latest medical innovations in oncology, offering the appropriate trial when it is relevant for patients should increase by organizational and technological innovations. The oncologic community will need to closely monitor their performance, portability and simplicity for implementation in daily clinical practice. • Prognostic scores based on clinical and biological variables help selecting patients for Early-phase clinical trials. • Natural Language Processing supports improved trial-matching and eligibility prediction tools. • Trial matching could increase patients’ recruitment to trials by up to 20%. • Eligibility prediction could bring early discontinuation to less than 20%. • Those new approaches should be associated to the classical ones for their implementation in clinical routine. .

Topics & Concepts

Matching (statistics)Clinical trialOncologySelection (genetic algorithm)MedicineInternal medicineMedical physicsComputer scienceArtificial intelligencePathologyEthics in Clinical ResearchStatistical Methods in Clinical TrialsCancer Genomics and Diagnostics