Training-Aware Risk Control for Intensity Modulated Radiation Therapies Quality Assurance with Conformal Prediction

Kevin He, David Adam, Sarah Han-Oh, Anqi Liu
Proceedings of the 4th Machine Learning for Health Symposium, PMLR 259:456-470, 2025.

Abstract

Measurement quality assurance (QA) prac- tices play a key role in the safe use of Inten- sity Modulated Radiation Therapies (IMRT) for cancer treatment. These practices have re- duced measurement-based IMRT QA failure be- low 1%. However, these practices are time and labor intensive which can lead to delays in pa- tient care. In this study, we examine how con- formal prediction methodologies can be used to robustly triage plans. We propose a new training-aware conformal risk control method by combining the benefit of conformal risk con- trol and conformal training. We incorporate the decision-making thresholds based on the GPR, along with the risk functions used in clinical evaluation, into the design of the risk control framework. Our method achieves high sensitiv- ity and specificity and significantly reduces the number of plans needing measurement without generating a huge confidence interval. Our re- sults demonstrate the validity and applicabil- ity of conformal prediction methods for improv- ing efficiency and reducing the workload of the IMRT QA process.

Cite this Paper

BibTeX
@InProceedings{pmlr-v259-he25a, title = {Training-Aware Risk Control for Intensity Modulated Radiation Therapies Quality Assurance with Conformal Prediction}, author = {He, Kevin and Adam, David and Han-Oh, Sarah and Liu, Anqi}, booktitle = {Proceedings of the 4th Machine Learning for Health Symposium}, pages = {456--470}, year = {2025}, editor = {Hegselmann, Stefan and Zhou, Helen and Healey, Elizabeth and Chang, Trenton and Ellington, Caleb and Mhasawade, Vishwali and Tonekaboni, Sana and Argaw, Peniel and Zhang, Haoran}, volume = {259}, series = {Proceedings of Machine Learning Research}, month = {15--16 Dec}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v259/main/assets/he25a/he25a.pdf}, url = {https://proceedings.mlr.press/v259/he25a.html}, abstract = {Measurement quality assurance (QA) prac- tices play a key role in the safe use of Inten- sity Modulated Radiation Therapies (IMRT) for cancer treatment. These practices have re- duced measurement-based IMRT QA failure be- low 1%. However, these practices are time and labor intensive which can lead to delays in pa- tient care. In this study, we examine how con- formal prediction methodologies can be used to robustly triage plans. We propose a new training-aware conformal risk control method by combining the benefit of conformal risk con- trol and conformal training. We incorporate the decision-making thresholds based on the GPR, along with the risk functions used in clinical evaluation, into the design of the risk control framework. Our method achieves high sensitiv- ity and specificity and significantly reduces the number of plans needing measurement without generating a huge confidence interval. Our re- sults demonstrate the validity and applicabil- ity of conformal prediction methods for improv- ing efficiency and reducing the workload of the IMRT QA process.} }
Endnote
%0 Conference Paper %T Training-Aware Risk Control for Intensity Modulated Radiation Therapies Quality Assurance with Conformal Prediction %A Kevin He %A David Adam %A Sarah Han-Oh %A Anqi Liu %B Proceedings of the 4th Machine Learning for Health Symposium %C Proceedings of Machine Learning Research %D 2025 %E Stefan Hegselmann %E Helen Zhou %E Elizabeth Healey %E Trenton Chang %E Caleb Ellington %E Vishwali Mhasawade %E Sana Tonekaboni %E Peniel Argaw %E Haoran Zhang %F pmlr-v259-he25a %I PMLR %P 456--470 %U https://proceedings.mlr.press/v259/he25a.html %V 259 %X Measurement quality assurance (QA) prac- tices play a key role in the safe use of Inten- sity Modulated Radiation Therapies (IMRT) for cancer treatment. These practices have re- duced measurement-based IMRT QA failure be- low 1%. However, these practices are time and labor intensive which can lead to delays in pa- tient care. In this study, we examine how con- formal prediction methodologies can be used to robustly triage plans. We propose a new training-aware conformal risk control method by combining the benefit of conformal risk con- trol and conformal training. We incorporate the decision-making thresholds based on the GPR, along with the risk functions used in clinical evaluation, into the design of the risk control framework. Our method achieves high sensitiv- ity and specificity and significantly reduces the number of plans needing measurement without generating a huge confidence interval. Our re- sults demonstrate the validity and applicabil- ity of conformal prediction methods for improv- ing efficiency and reducing the workload of the IMRT QA process.
APA
He, K., Adam, D., Han-Oh, S. & Liu, A.. (2025). Training-Aware Risk Control for Intensity Modulated Radiation Therapies Quality Assurance with Conformal Prediction. Proceedings of the 4th Machine Learning for Health Symposium, in Proceedings of Machine Learning Research 259:456-470 Available from https://proceedings.mlr.press/v259/he25a.html.

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