Description

Systemic treatments for thyroid cancer have emerged in the past decade, accompanied by a deeper understanding of its underlying molecular mechanisms. Among these, lenvatinib, a multi-targeted tyrosine kinase inhibitor, was approved as a monotherapy for treating locally advanced or metastatic radioactive iodine refractory differentiated thyroid cancer. Despite its efficacy, lenvatinib is associated with a spectrum of adverse events (AEs), including hypertension, fatigue, proteinuria, and gastrointestinal disturbances, which often necessitate dose reduction, interruption, or permanent discontinuation. To overcome these challenges, the investigators address to the Constrained Disorder Principle (CDP), an innovative approach that emphasizes the exploration of constrained variability in treatment regimens to optimize drug effectiveness and minimize AEs. In other disease contexts, such as congestive heart failure, multiple sclerosis, and chronic pain, the integration of CDP-based second-generation artificial intelligence (AI) systems into treatment regimens has shown promising results in enhancing therapeutic outcomes by dynamically adjusting treatment parameters. The investigators hypothesize that a personalized dynamic adjustment of lenvatinib dosages and administration timing, guided by an AI-driven approach via a mobile application, may reduce AEs, improve adherence, and enhance overall treatment efficacy. In this proof-of-concept study, the investigators aim to evaluate the feasibility and efficacy of utilizing a CDP-based second-generation AI system to optimize the therapeutic regimen of lenvatinib in patients with cancer.