Automation of Radiation Treatment Planning for Rectal Cancer

04/26/2022
by   Kai Huang, et al.
0

To develop an automated workflow for rectal cancer three-dimensional conformal radiotherapy treatment planning that combines deep-learning(DL) aperture predictions and forward-planning algorithms. We designed an algorithm to automate the clinical workflow for planning with field-in-field. DL models were trained, validated, and tested on 555 patients to automatically generate aperture shapes for primary and boost fields. Network inputs were digitally reconstructed radiography, gross tumor volume(GTV), and nodal GTV. A physician scored each aperture for 20 patients on a 5-point scale(>3 acceptable). A planning algorithm was then developed to create a homogeneous dose using a combination of wedges and subfields. The algorithm iteratively identifies a hotspot volume, creates a subfield, and optimizes beam weight all without user intervention. The algorithm was tested on 20 patients using clinical apertures with different settings, and the resulting plans(4 plans/patient) were scored by a physician. The end-to-end workflow was tested and scored by a physician on 39 patients using DL-generated apertures and planning algorithms. The predicted apertures had Dice scores of 0.95, 0.94, and 0.90 for posterior-anterior, laterals, and boost fields, respectively. 100 posterior-anterior, laterals, and boost apertures were scored as clinically acceptable, respectively. Wedged and non-wedged plans were clinically acceptable for 85 dose percentage was reduced from 121 prescription dose. The integrated end-to-end workflow of automatically generated apertures and optimized field-in-field planning gave clinically acceptable plans for 38/39(97 clinical workflow for generating radiotherapy plans for rectal cancer for our institution.

READ FULL TEXT

page 3

page 6

page 8

page 10

page 12

research
06/07/2021

Deep Learning 3D Dose Prediction for Conventional Lung IMRT Using Consistent/Unbiased Automated Plans

Deep learning (DL) 3D dose prediction has recently gained a lot of atten...
research
06/06/2022

Learning Treatment Plan Representations for Content Based Image Retrieval

Objective: Knowledge based planning (KBP) typically involves training an...
research
02/17/2020

Surrogate-free machine learning-based organ dose reconstruction for pediatric abdominal radiotherapy

To study radiotherapy-related adverse effects, detailed dose information...
research
03/18/2021

Rapid treatment planning for low-dose-rate prostate brachytherapy with TP-GAN

Treatment planning in low-dose-rate prostate brachytherapy (LDR-PB) aims...
research
08/19/2021

Reproducible radiomics through automated machine learning validated on twelve clinical applications

Radiomics uses quantitative medical imaging features to predict clinical...

Please sign up or login with your details

Forgot password? Click here to reset