Virtual Dosimetrists: A Radiotherapy Training "Flight Simulator"

TL;DR

This paper introduces 'Virtual Dosimetrist' models that generate and improve radiotherapy treatment plans using dose prediction and natural language processing, enhancing training and education in radiotherapy planning.

Contribution

It presents the first integrated system combining dose distribution prediction with natural language prompts for radiotherapy plan training.

Findings

Accurate and rapid dose generation and modification

Supports training with suboptimal plan examples

Enables interactive plan review and improvement

Abstract

Effective education in radiotherapy plan quality review requires a robust, regularly updated set of examples and the flexibility to demonstrate multiple possible planning approaches and their consequences. However, the current clinic-based paradigm does not support these needs. To address this, we have developed 'Virtual Dosimetrist' models that can both generate training examples of suboptimal treatment plans and then allow trainees to improve the plan quality through simple natural language prompts, as if communicating with a dosimetrist. The dose generation and modification process is accurate, rapid, and requires only modest resources. This work is the first to combine dose distribution prediction with natural language processing; providing a robust pipeline for both generating suboptimal training plans and allowing trainees to practice their critical plan review and improvement skills that addresses the challenges of the current clinic-based paradigm.