Abstract Machine learning models have been increasingly considered to model head and
neck cancer outcomes for improved screening, diagnosis, treatment, and prognostication of …

Background This systematic review aimed to evaluate the performance of machine learning
(ML) models in predicting post-treatment survival and disease progression outcomes …

Background Preclinical models of cancer can be of translational benefit when assessing
how different biomarkers are regulated in response to particular treatments. Detection of …

Simple Summary The assessment of tumor response is important in evaluating cancer
treatment and predicting clinical outcomes. The currently used response evaluation criteria …

Abstract Machine learning (ML) opens new perspectives in identifying predictive factors of
efficacy among a large number of patients' characteristics in oncology studies. The objective …

A joint modeling framework was developed using data from 75 patients of early
amcenestrant phase I–II AMEERA‐1‐2 dose escalation and expansion cohorts. A semi …

By using the population level data of heterogeneous populations, statistical and
mathematical approaches, such as Lasso and mixed effects modeling, to identify variables …