Childhood Cancer Data Initiative Annual Symposium (Abstract Registration): Submission #77

serial: '77'
sid: '192594'
uuid: 7a4125cc-f57c-47aa-8d26-f1e490540392
uri: /nci/ccdisymposium/abstract
created: '1787854810'
completed: '1787855219'
changed: '1787855219'
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langcode: en
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data:
  authors_:
    - add_author_degree: PhD
      add_author_first_name: Jungwook
      add_author_last_name: Shin
      add_author_middle: ''
      add_author_organization: 'Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD'
    - add_author_degree: PhD
      add_author_first_name: Matthew
      add_author_last_name: Mille
      add_author_middle: M
      add_author_organization: 'Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD'
    - add_author_degree: BA
      add_author_first_name: Caroline
      add_author_last_name: Esposito
      add_author_middle: ''
      add_author_organization: 'Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD'
    - add_author_degree: PhD
      add_author_first_name: Todd
      add_author_last_name: Gibson
      add_author_middle: M
      add_author_organization: 'Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD'
    - add_author_degree: PhD
      add_author_first_name: Keith
      add_author_last_name: Griffin
      add_author_middle: T
      add_author_organization: 'Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD'
    - add_author_degree: PhD
      add_author_first_name: 'Jae Won'
      add_author_last_name: Jung
      add_author_middle: ''
      add_author_organization: 'Department of Radiation Oncology, East Carolina University, Greenville, NC'
    - add_author_degree: PhD
      add_author_first_name: Choonik
      add_author_last_name: Lee
      add_author_middle: ''
      add_author_organization: 'Department of Radiation Oncology, University of Michigan, Ann Arbor, MI'
    - add_author_degree: PhD
      add_author_first_name: Sergio
      add_author_last_name: Morató
      add_author_middle: ''
      add_author_organization: 'Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD'
    - add_author_degree: MD
      add_author_first_name: Torunn
      add_author_last_name: Yock
      add_author_middle: I
      add_author_organization: 'Massachusetts General Hospital and Harvard Medical School, Boston, MA'
    - add_author_degree: DPhil
      add_author_first_name: Amy
      add_author_last_name: 'Berrington de González'
      add_author_middle: ''
      add_author_organization: 'Division of Genetics and Epidemiology, Institute for Cancer Research, London, UK'
    - add_author_degree: PhD
      add_author_first_name: Choonsik
      add_author_last_name: Lee
      add_author_middle: ''
      add_author_organization: 'Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD'
    - add_author_degree: PhD
      add_author_first_name: Cari
      add_author_last_name: Kitahara
      add_author_middle: M
      add_author_organization: 'Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD'
  abstract: 'The Pediatric Proton/Photon Therapy Comparison (PPTC) cohort study is the first large-scale study comparing the risk of second cancers in children treated with proton versus photon radiotherapy. The study has collected treatment records for more than 10,000 pediatric cancer patients from 17 hospitals. Because the cohort data are both large and distributed across multiple databases, we have deployed a cloud-based system to streamline data collection, monitoring, validation, and transfer to a high-performance computing (HPC) cluster. Data for ~7,500 patients have been collected to date in an industry-standard medical imaging format (DICOM), including radiation field parameters, planning computed tomography (CT) images with clinician-delineated anatomical structures, and dose distributions from the treatment planning system (TPS). Another critical component is the development of scalable methods for estimating individualized, organ-level radiation dose for epidemiological dose-response analyses. Planning CT scans typically cover only the treatment region, often omitting organs of interest for late effects research. To address this, we developed a method to extend partial-body CT images using a library of surrogate anatomies. In addition, due to variability and inconsistency in organ delineation and naming, we utilize a deep learning–based automatic segmentation tool to standardize organ delineation. Finally, we address limitations of TPS dose estimates by performing advanced Monte Carlo radiation transport simulations of both modalities. These simulations integrate patient data and detailed physics modeling and are efficiently executed on the NIH HPC cluster. This poster presents our efforts to implement a state-of-the-art dosimetry platform—from data collection to individualized dose calculations.'
  abstract_title_: 'Radiation dosimetry for the first large-scale systemic comparison of the risk of second cancers in children treated with proton versus photon therapy'
  email_address_: matthew.mille@nih.gov
  institution_: 'Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD'
  presenting_author_: 'Matthew M. Mille'