Knowledge Models as Teaching Aid for Training Intensity Modulated Radiation Therapy Planning: A Lung Cancer Case Study.

Matt Mistro,Yang Sheng,Q Jackie Wu,Yaorong Ge,Qiuwen Wu,Jing Cai,Chris R Kelsey,Fang-Fang Yin,Jatinder R Palta

doi:10.3389/frai.2020.00066

Abstract

Purpose: Artificial intelligence (AI) employs knowledge models that often behave as a black-box to the majority of users and are not designed to improve the skill level of users. In this study, we aim to demonstrate the feasibility that AI can serve as an effective teaching aid to train individuals to develop optimal intensity modulated radiation therapy (IMRT) plans.Methods and Materials: The training program is composed of a host of training cases and a tutoring system that consists of a front-end visualization module powered by knowledge models and a scoring system. The current tutoring system includes a beam angle prediction model and a dose-volume histogram (DVH) prediction model. The scoring system consists of physician chosen criteria for clinical plan evaluation as well as specially designed criteria for learning guidance. The training program includes six lung/mediastinum IMRT patients: one benchmark case and five training cases. A plan for the benchmark case is completed by each trainee entirely independently pre- and post-training. Five training cases cover a wide spectrum of complexity from easy (2), intermediate (1) to hard (2). Five trainees completed the training program with the help of one trainer. Plans designed by the trainees were evaluated by both the scoring system and a radiation oncologist to quantify planning quality.Results: For the benchmark case, trainees scored an average of 21.6% of the total max points pre-training and improved to an average of 51.8% post-training. In comparison, the benchmark case's clinical plans score an average of 54.1% of the total max points. Two of the five trainees' post-training plans on the benchmark case were rated as comparable to the clinically delivered plans by the physician and all five were noticeably improved by the physician's standards. The total training time for each trainee ranged between 9 and 12 h.Conclusion: This first attempt at a knowledge model based training program brought unexperienced planners to a level close to experienced planners in fewer than 2 days. The proposed tutoring system can serve as an important component in an AI ecosystem that will enable clinical practitioners to effectively and confidently use KBP.

Highlights

Knowledge models collect and extract important patterns and knowledge from high quality clinical plans and utilize them to predict clinically optimal solutions for new cases
For the five cases used within the training program (Figure 5B), every trainee obtained a score in the final plan that was greater than that of the clinically delivered plan with the exception of case 3 for trainee 5 and there was an overall average of 12.6 raw planning score point improvement over the respective clinically delivered plans
One of the trainee’s plan was deemed appropriate for clinical use based on the physician’s discretion. This is the first attempt at developing an effective training program for IMRT planning that capitalizes on the implicit planning tactics that is built into knowledge models for lung IMRT

Summary

Introduction

Knowledge models collect and extract important patterns and knowledge from high quality clinical plans and utilize them to predict clinically optimal solutions for new cases For treatment planning, this comes in the form of selected beam angles, optimized collimator settings, predicted achievable dose-volume histogram (DVH) endpoints for inverse optimization, and combined multiple parameter predictions for a fully automated treatment planning process (Zhu et al, 2011; Breedveld et al, 2012; Zhang et al, 2012, 2018, 2019a,b; Good et al, 2013; Voet et al, 2013; Zarepisheh et al, 2014; Sheng et al, 2015, 2019; Yuan et al, 2015, 2018; Hazell et al, 2016). The goal is to make a human-centered artificial intelligence (AI) system to exploit the strengths from both ends and efficiently train competent planners

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