Segmentation of patients with small cell lung cancer into responders and non-responders using the optimal cross-validation technique.

ABSTRACT:

Background

The timing of treating cancer patients is an essential factor in the efficacy of treatment. So, patients who will not respond to current therapy should receive a different treatment as early as possible. Machine learning models can be built to classify responders and nonresponders. Such classification models predict the probability of a patient being a responder. Most methods use a probability threshold of 0.5 to convert the probabilities into binary group membership. However, the cutoff of 0.5 is not always the optimal choice.

Methods

In this study, we propose a novel data-driven approach to select a better cutoff value based on the optimal cross-validation technique. To illustrate our novel method, we applied it to three clinical trial datasets of small-cell lun

SUBMITTER: Majd E 

PROVIDER: S-EPMC11000309 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature