Importance In 2016, the American Joint Committee on Cancer (AJCC) established criteria to
evaluate prediction models for staging. No localized prostate cancer models were endorsed …

Dynamic prediction incorporates time‐dependent marker information accrued during follow‐up
to improve personalized survival prediction probabilities. At any follow‐up, or “landmark”, …

Importance Female resident physicians are disproportionately affected by burnout, which
can have serious consequences for their well-being and career trajectory. Growing evidence …

Importance Patients with preexisting liver dysfunction could benefit the most from personalized
therapy for liver tumors to balance maximal tumor control and minimal risk of liver failure. …

Background Prediction models for time-to-event outcomes are commonly used in biomedical
research to obtain subject-specific probabilities that aid in making important clinical care …

Background Risk prediction models for time-to-event outcomes play a vital role in personalized
decision-making. A patient’s biomarker values, such as medical lab results, are often …

Machine learning (ML) models have been shown to predict the presence of clinical factors
from medical imaging with remarkable accuracy. However, these complex models can be …

Purpose To use knowledge-based planning (KBP) as a method of producing high-quality,
consistent, protocol-compliant treatment plans in a complex setting of spine stereotactic body …

Epstein-Barr virus (EBV), a member of the herpes virus family, is a causative agent for
infectious mononucleosis in young adults. It has an asymptomatic and subclinical distribution in …

Background The increased incidence of human papillomavirus (HPV)-related cancers has
motivated efforts to optimise treatment for these patients with excellent prognosis. Validation …