"We must wound in order to treat. But will that wound heal?" A conversation with Robert Timmerman reframes what his OAR constraint tables were actually built to answer — and what it really means for physicists to weigh tumor coverage against the harm we cause getting there.

Every physicist learns to spare the urethra — but why? A 1 Gy increase in urethral dose measurably raises a patient's risk of long-term urinary toxicity, and the difference between a disciplined plan and a loose one can mean the difference between a smooth recovery and a urethral stricture. Here's what the data actually says about the number on your screen.

Incident learning systems are essential for maintaining safety and quality in radiation oncology, yet their design varies widely. Some rely on a physicist to log and review events, while others use multidisciplinary committees or hybrid models. This post outlines the core elements of an effective ILS—clear documentation, blame‑free reporting, and solution‑focused review—to help clinics refine their approach to continuous improvement.

This project applies a 3D convolutional neural network to predict grade ≥ 2 xerostomia six months after head‑and‑neck radiotherapy. By integrating CT, dose, and organ‑at‑risk data into a unified image, the model demonstrates how neural networks can advance predictive modeling and personalized care in radiation oncology.