cs.CVApr 3, 2026

PR3DICTR: A modular AI framework for medical 3D image-based detection and outcome prediction

Authors: Daniel C. MacRae, Luuk van der Hoek, Robert van der Wal, Suzanne P. M. de Vette, Hendrike Neh, Baoqiang Ma, Peter M. A. van Ooijen, Lisanne V. van Dijk

Organizations: Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands

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Abstract

Three-dimensional medical image data and computer-aided decision making, particularly using deep learning, are becoming increasingly important in the medical field. To aid in these developments we introduce PR3DICTR: Platform for Research in 3D Image Classification and sTandardised tRaining. Built using community-standard distributions (PyTorch and MONAI), PR3DICTR provides an open-access, flexible and convenient framework for prediction model development, with an explicit focus on classification using three-dimensional medical image data. By combining modular design principles and standardization, it aims to alleviate developmental burden whilst retaining adjustability. It provides users with a wealth of pre-established functionality, for instance in model architecture design options, hyper-parameter solutions and training methodologies, but still gives users the opportunity and freedom to ``plug in'' their own solutions or modules. PR3DICTR can be applied to any binary or event-based three-dimensional classification task and can work with as little as two lines of code.

Figures & tables

Figure 1: Example of a binary classification deep learning model workflow using 3D medical imaging.
Figure 2: Overall PR3DICTR workflow. An example notebook for steps 1–3 can be found in the repository, and another notebook demonstrates steps 4–6.
Figure 3: Schematic of a data pre-processing pipeline for preparing datasets to be used in the PR3DICTR framework.
Figure 4: Code flow diagram of the PR3DICTR framework.
Figure 5: Outline of the classification model framework in PR3DICTR, for a hypothetical 2-label classification task.
TypeOptionsNotes
Image modalitiesCTStandard planning or diagnostic CT used as anatomical input.
 PETFunctional metabolic imaging; often co-registered with CT.
 Radiation dose3D dose distribution map, used in radiotherapy settings.
 SegmentationsBinary or multi-class masks for organs or targets; used as additional spatial priors.
 MRIMulti-sequence (T1, T2, etc.) structural or functional MRI.
 OtherAny additional volumetric image (e.g. perfusion, ADC, or synthetic images) can be added to the PR3DICTR framework.
Table 1: Main options within the PR3DICTR framework modules.
Figure 6: Confusion matrix and calibration plot for the sex prediction model produced using the PR3DICTR framework on the NSCLC-Radiomics database.

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