Related papers: Adaptive design for identifying maximum tolerated …
In a tie-breaker design (TBD), subjects with high values of a running variable are given some (usually desirable) treatment, subjects with low values are not, and subjects in the middle are randomized. TBDs are intermediate between…
Hit identification is a central challenge in early drug discovery, traditionally requiring substantial experimental resources. Recent advances in artificial intelligence, particularly large language models (LLMs), have enabled virtual…
This study investigates the applicability of 3D dose predictions from a model trained on one modality to a cross-modality automated planning workflow. Additionally, we explore the impact of integrating a multi-criteria optimizer on adapting…
Clinical trials are an integral component of medical research. Trials require careful design to, for example, maintain the safety of participants, use resources efficiently and allow clinically meaningful conclusions to be drawn. Adaptive…
Personalized intervention strategies, in particular those that modify treatment based on a participant's own response, are a core component of precision medicine approaches. Sequential Multiple Assignment Randomized Trials (SMARTs) are…
Dose-finding clinical trials in oncology aim to estimate the maximum tolerated dose (MTD), based on safety traditionally obtained from the clinician's perspective. While the collection of patient-reported outcomes (PROs) has been advocated…
Purpose: To demonstrate the feasibility of fast Monte Carlo (MC) based inverse biological planning for the treatment of head and neck tumors in spot-scanning proton therapy. Methods: Recently, a fast and accurate Graphics Processor Unit…
There is a growing interest in the implementation of platform trials, which provide the flexibility to incorporate new treatment arms during the trial and the ability to halt treatments early based on lack of benefit or observed…
Topology optimization (TO) has been widely adopted in engineering design; however, it is prone to being trapped in local optima, particularly in strongly nonlinear problems. Sensitivity-free data-driven topology design (DDTD) offers a…
Clinical trials with time-to-event endpoints, such as overall survival (OS) or progression-free survival (PFS), are fundamental for evaluating new treatments, particularly in immuno-oncology. However, modern therapies, such as…
This work considers the problem of personalized dose guidance using Bayesian optimization that learns the optimum drug dose tailored to each individual, thus improving therapeutic outcomes. Safe learning using interior point method ensures…
Stochastic gradient-boosted decision trees are widely employed for multivariate classification and regression tasks. This paper presents a speed-optimized and cache-friendly implementation for multivariate classification called FastBDT.…
Immunotherapies have revolutionized cancer treatment. Unlike chemotherapies, immune agents often take longer time to show benefit, and the complex and unique mechanism of action of these agents renders the use of multiple endpoints more…
Field experiments and computer simulations are effective but time-consuming methods of measuring the quality of engineered systems at different settings. To reduce the total time required, experimenters may employ Bayesian optimization,…
This work addresses the need for a balanced approach between performance and efficiency in scalable production environments for visually-rich document understanding (VDU) tasks. Currently, there is a reliance on large document foundation…
Most Bayesian response-adaptive designs unbalance randomization rates towards the most promising arms with the goal of increasing the number of positive treatment outcomes during the study, even though the primary aim of the trial is…
Botulinum toxin (Botox) injections are the gold standard for managing facial asymmetry and aesthetic rejuvenation, yet determining the optimal dosage remains largely intuitive, often leading to suboptimal outcomes. We propose a localized…
The Project Optimus initiative by the FDA's Oncology Center of Excellence is widely viewed as a groundbreaking effort to change the $\textit{status quo}$ of conventional dose-finding strategies in oncology. Unlike in other therapeutic areas…
2-in-1 design (Chen et al. 2018) is becoming popular in oncology drug development, with the flexibility of using different endpoints at different decision time. Based on the observed interim data, sponsors choose either to seamlessly…
Adaptive designs for clinical trials permit alterations to a study in response to accumulating data in order to make trials more flexible, ethical and efficient. These benefits are achieved while preserving the integrity and validity of the…