Abstract
Imaging data is becoming more ubiquitous in the medical domain, particularly in radiotherapy. Deep learning (DL) models have been shown to be effective in utilizing medical image data in prediction tasks, however they remain challenging to develop and are yet to be widely adopted in clinical practice. To aid standardized development of 3D image-based DL prediction models we introduce PR3DICTR: Platform for Research in 3D Image Classification and sTandardised tRaining. PR3DICTR uses a 6-step process for effective use: 1-3 prerequisites (image/tabular data preprocessing and organization); 4-5 model construction and user input (including training and hyperparameter tuning); and 6 monitoring and evaluation. To highlight its use in radiotherapy, PR3DICTR was used for two radiotherapy related prediction tasks: 1) dysphagia 6 months after head and neck cancer (HNC) treatment; and 2) overall survival after HNC treatment. For both tasks, the PR3DICTR model achieves comparable or slightly better performance than the published counterparts (task 1: AUC: 0.86 vs 0.84, task 2: C-index: 0.78 vs 0.71). PR3DICTR was conceived to be a continuously evolving platform, and we invite any researcher to see if it is applicable for their research efforts.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/MIART_008.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: Not Available
BibTex
@InProceedings{vanLuu_PR3DICTR_MICCAISAT2026,
author = { van der Hoek, Luuk AND MacRae, Daniel C. AND van der Wal, Robert AND de Vette, Suzanne P. M. AND Neh, Hendrike AND Ma, Baoqiang AND Sijtsema, Nanna M. AND van Ooijen, Peter M. A. AND van Dijk, Lisanne V.},
title = { { PR3DICTR: a 3D Image-Based Deep Learning Prediction Modelling Framework and its Use in Radiotherapy } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17274},
month = {pending},
page = {pending}
}