# OpenKBP: The open-access knowledge-based planning grand challenge

arXiv: https://arxiv.org/abs/2011.14076 · HTML: https://mlsanity.com/paper/2011.14076/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2020.

## Built on by (most cited first, 30 of 30)

- [DoseDiff: Distance-aware Diffusion Model for Dose Prediction in Radiotherapy](https://mlsanity.com/paper/2306.16324/) (2023)
- [Multi-Agent Reinforcement Learning Meets Leaf Sequencing in Radiotherapy](https://mlsanity.com/paper/2406.01853/) (2024)
- [Domain Knowledge Driven 3D Dose Prediction Using Moment-Based Loss Function](https://mlsanity.com/paper/2207.03414/) (2022)
- [Diffusion-based Radiotherapy Dose Prediction Guided by Inter-slice Aware Structure Encoding](https://mlsanity.com/paper/2311.02991/) (2023)
- [OpenKBP-Opt: An international and reproducible evaluation of 76 knowledge-based planning pipelines](https://mlsanity.com/paper/2202.08303/) (2022)
- [An Efficient Multi-Scale Fusion Network for 3D Organ at Risk (OAR) Segmentation](https://mlsanity.com/paper/2208.07417/) (2022)
- [Cross-Task Attention Network: Improving Multi-Task Learning for Medical Imaging Applications](https://mlsanity.com/paper/2309.03837/) (2023)
- [LENAS: Learning-based Neural Architecture Search and Ensemble for 3D Radiotherapy Dose Prediction](https://mlsanity.com/paper/2106.06733/) (2021)
- [DeepDoseNet: A Deep Learning model for 3D Dose Prediction in Radiation Therapy](https://mlsanity.com/paper/2111.00077/) (2021)
- [Towards more precise automatic analysis: a comprehensive survey of deep learning-based multi-organ segmentation](https://mlsanity.com/paper/2303.00232/) (2023)
- [Automating RT Planning at Scale: High Quality Data For AI Training](https://mlsanity.com/paper/2501.11803/) (2025)
- [Enhancing Organ at Risk Segmentation with Improved Deep Neural Networks](https://mlsanity.com/paper/2202.01866/) (2022)
- [Learning Image Representations for Content Based Image Retrieval of Radiotherapy Treatment Plans](https://mlsanity.com/paper/2206.02912/) (2022)
- [Swin UNETR++: Advancing Transformer-Based Dense Dose Prediction Towards Fully Automated Radiation Oncology Treatments](https://mlsanity.com/paper/2311.06572/) (2023)
- [Deep Evidential Learning for Radiotherapy Dose Prediction](https://mlsanity.com/paper/2404.17126/) (2024)
- [Demo: Generative AI helps Radiotherapy Planning with User Preference](https://mlsanity.com/paper/2512.08996/) (2025)
- [Latent Spaces Enable Transformer-Based Dose Prediction in Complex Radiotherapy Plans](https://mlsanity.com/paper/2407.08650/) (2024)
- [Federated prediction for scalable and privacy-preserved knowledge-based planning in radiotherapy](https://mlsanity.com/paper/2505.14507/) (2025)
- [Robustness of Transformer-Based Fluence Map Prediction Under Clinically Realistic Perturbations](https://mlsanity.com/paper/2605.00904/) (2026)
- [Any2Any 3D Diffusion Models with Knowledge Transfer: A Radiotherapy Planning Study](https://mlsanity.com/paper/2605.09622/) (2026)
- [Domain Adaptation of Automated Treatment Planning from Computed Tomography to Magnetic Resonance](https://mlsanity.com/paper/2203.03576/) (2022)
- [The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private data](https://mlsanity.com/paper/2306.10484/) (2023)
- [FedKBP: Federated dose prediction framework for knowledge-based planning in radiation therapy](https://mlsanity.com/paper/2408.10275/) (2024)
- [Virtual Dosimetrists: A Radiotherapy Training "Flight Simulator"](https://mlsanity.com/paper/2505.09796/) (2025)
- [ALMo: Interactive Aim-Limit-Defined, Multi-Objective System for Personalized High-Dose-Rate Brachytherapy Treatment Planning and Visualization for Cervical Cancer](https://mlsanity.com/paper/2602.13666/) (2026)
- [Toward Actionable Digital Twins for Radiation-Based Imaging and Therapy: Mathematical Formulation, Modular Workflow, and an OpenKBP-Based Dose-Surrogate Prototype](https://mlsanity.com/paper/2603.26820/) (2026)
- [Clinical DVH metrics as a loss function for 3D dose prediction in head and neck radiotherapy](https://mlsanity.com/paper/2603.29670/) (2026)
- [A Machine-to-Machine Knowledge-Guided LLM Agent for Generalizable Radiotherapy Treatment Planning](https://mlsanity.com/paper/2606.00922/) (2026)
- [DoseBridge: Denoising Diffusion Bridge Model for Dose Prediction in Lung Intensity-Modulated Proton Therapy](https://mlsanity.com/paper/2608.10173/) (2026)
- [MedUAG: Unified Understanding and Generation for Medical Multimodal Models](https://mlsanity.com/paper/2608.18937/) (2026)

## Abstract (arXiv)

The purpose of this work is to advance fair and consistent comparisons of dose prediction methods for knowledge-based planning (KBP) in radiation therapy research. We hosted OpenKBP, a 2020 AAPM Grand Challenge, and challenged participants to develop the best method for predicting the dose of contoured CT images. The models were evaluated according to two separate scores: (1) dose score, which evaluates the full 3D dose distributions, and (2) dose-volume histogram (DVH) score, which evaluates a set DVH metrics. Participants were given the data of 340 patients who were treated for head-and-neck cancer with radiation therapy. The data was partitioned into training (n=200), validation (n=40), and testing (n=100) datasets. All participants performed training and validation with the corresponding datasets during the validation phase of the Challenge, and we ranked the models in the testing phase based on out-of-sample performance. The Challenge attracted 195 participants from 28 countries, and 73 of those participants formed 44 teams in the validation phase, which received a total of 1750 submissions. The testing phase garnered submissions from 28 teams. On average, over the course of the validation phase, participants improved the dose and DVH scores of their models by a factor of 2.7 and 5.7, respectively. In the testing phase one model achieved significantly better dose and DVH score than the runner-up models. Lastly, many of the top performing teams reported using generalizable techniques (e.g., ensembles) to achieve higher performance than their competition. This is the first competition for knowledge-based planning research, and it helped launch the first platform for comparing KBP prediction methods fairly and consistently. The OpenKBP datasets are available publicly to help benchmark future KBP research, which has also democratized KBP research by making it accessible to everyone.