Contouring: Interactive Training and Feedback in Radiation Oncology

Overview

Treating patients with safe and effective radiation therapy depends critically on the precise identification of tumor and nearby normal tissues. This process, known as contouring, refers to identifying and outlining cancer and normal tissues in medical images.

Poor radiation planning has detrimental consequences on patient well-being. Radiation plans that deviate from protocol specifications have substantially decreased survival compared to patients with compliant radiation plans. Given the impact of contouring on patient outcomes, many contouring resources exist. However, practice guidelines rarely translate into real-world clinical practices, primarily due to ineffective methods of development, delivery, and access.

In collaboration with the department of Radiation Medicine at UCSD, we work to replace the traditional one-to-one apprenticeship model with training that is personalized, timely, and available whenever a resident is ready to practice. Three systems carry that agenda.

iContour

iContour is a web-based contouring platform that presents anonymized DICOM cases and gives residents immediate, structured feedback as they delineate. It grew out of needfinding with residents and attendings about when and how contouring feedback actually reaches a trainee, and out of measurements showing that everyday touch devices are accurate enough for real delineation work.

A randomized international trial of iContour has now completed. Secondary analyses of that trial characterize the specific, site-dependent mistakes residents make before and after their clinical rotations, showing that contouring errors are predictable rather than idiosyncratic.

VRContour

VRContour asks what contouring becomes when the anatomy is no longer flat. Residents and attendings work slice by slice on 2D displays even though the structures they are outlining are volumetric. VRContour brings delineation into virtual reality, with sketching techniques designed for 3D exploration and annotation of medical images, and studies which parts of the task actually benefit from immersion.

iConTutor

iConTutor is the next stage. Where iContour delivered practice and feedback, iConTutor aims to turn that into personalized tutoring: using the errors a trainee actually makes, at the disease site they are actually rotating through, to decide what feedback they should see and when.

The completed trial and the mistake analyses are the evidence base. Because errors are predictable and site-dependent, targeted automated tutoring becomes plausible where generic feedback would not be.

Funding and External Collaborations

iContour is funded by Agency for Healthcare Research and Quality (AHRQ). It is a collaboration between the HXI Lab and a number of radiation oncology faculty and residents at UCSD school of medicine.

Publications