Machine learning for automatic construction of pediatric abdominal phantoms for radiation dose reconstruction

Marco Virgolin, Ziyuan Wang, Tanja Alderliesten, Peter A.N. Bosman

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Abstract

The advent of Machine Learning (ML) is proving extremely beneficial in many healthcare applications. In pediatric oncology, retrospective studies that investigate the relationship between treatment and late adverse effects still rely on simple heuristics. To capture the effects of radiation treatment, treatment plans are typically simulated on virtual surrogates of patient anatomy called phantoms. Currently, phantoms are built to represent categories of patients based on reasonable yet simple criteria. This often results in phantoms that are too generic to accurately represent individual anatomies. We present a novel approach that combines imaging data and ML to build individualized phantoms automatically. We design a pipeline that, given features of patients treated in the pre-3D planning era when only 2D radiographs were available, as well as a database of 3D Computed Tomography (CT) imaging with organ segmentations, uses ML to predict how to assemble a patient-specific phantom. Using 60 abdominal CTs of pediatric patients between 2 to 6 years of age, we find that our approach delivers significantly more representative phantoms compared to using current phantom building criteria, in terms of shape and location of two considered organs (liver and spleen), and shape of the abdomen. Furthermore, as interpretability is often central to trust ML models in medical contexts, among other ML algorithms we consider the Gene-pool Optimal Mixing Evolutionary Algorithm for Genetic Programming (GP-GOMEA), that learns readable mathematical expression models. We find that the readability of its output does not compromise prediction performance as GP-GOMEA delivered the best performing models.

Original languageEnglish
Title of host publicationMedical Imaging 2020
Subtitle of host publicationImaging Informatics for Healthcare, Research, and Applications
EditorsPo-Hao Chen, Thomas M. Deserno
PublisherSPIE
Number of pages7
Volume11318
ISBN (Electronic)9781510634039
DOIs
Publication statusPublished - 2020
EventMedical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications - Houston, United States
Duration: 16 Feb 202017 Feb 2020

Conference

ConferenceMedical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications
CountryUnited States
CityHouston
Period16/02/2017/02/20

Keywords

  • Dose reconstruction
  • Machine learning
  • Pediatric cancer
  • Phantom
  • Radiation treatment

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