This repository provides python scripts for head-and-neck photon and proton radiotherapy planning (in Raystation). It produces results for the paper - "Large-scale dose evaluation of deep learning organ contours in head-and-neck radiotherapy by leveraging existing plans" (under review).

Background and Purpose: Retrospective dose evaluation for organ-at-risk auto-contours has previously used small cohorts due to additional manual effort required for treatment planning on auto-contours. We aim to do this at large scale, by a) proposing and assessing an automated plan optimization workflow that uses existing clinical plan parameters and b) using it for head-and-neck auto-contour dose evaluation.

Materials and Methods: Our automated workflow emulates our clinics treatment planning protocol and reuses existing clinical plan optimization parameters. This workflow recreates the original clinical plan (P_OG) with manual contours (Pmc) and evaluates the dose effect (P_OG − P_MC) on 70 photon and 30 proton plans of head-and-neck patients. As a use-case, the same workflow (and parameters) creates a plan using auto-contours (P_AC) of eight head-and-neck organs-at-risk from a commercial tool and evaluates their dose effect (P_MC − P_AC).

Results: For plan recreation (P_OG − P_MC), our workflow, has a median impact of 0.99% and 1.45%, across dose metrics of auto-contours, for photon and proton, respectively. Computer time of automated planning is 25% (photon) and 42% (proton) of manual planning time. For auto-contour evaluation (P_MC − P_AC), we notice an impact of 2.01% and 2.58% for photon and proton radiotherapy. All evaluations have a median ∆NTCP less than 0.30%.

Conclusions: The plan replication capability of our automated program provides a blueprint for other clinics to perform auto-contour dose evaluation with large patient cohorts and minimal skill and resource requirements. Finally, in spite of geometric differences, auto-contours, have a minimal mediandose impact, hence inspiring confidence in their utility and facilitating their clinical adoption.

Within the Raystation python 3.6 environment, run the following command to install the required packages.

pip install pydicom

1. For photons

   • src/hnDosePhotons.py

2. For protons

   • src/hnDoseProtons.py

3. Other files

   • src/config.py
   • src/helpers.py