Data Science to Improve Treatment Planning for Advanced Prostate Cancer Patients Treated with Radiotherapy

Abstract

Radiotherapy can cause short- and long-term bladder and bowel toxicities, with corresponding quality of life (QoL)detriments, in up to 60% of prostate cancer patients. This study focuses on improving radiation treatment planning with innovative combination of novel datasets and new advances in data science. We will use deep learning techniques developed by our research group to predict clinician-rated toxicity and patient-reported outcomes (PROs)using dosiomics (i.e., a recently-developed methodology to create very high-resolution three-dimensional maps of radiation dosage) and radiomics (i.e. imaging of novel tumor features such as shape and texture, that may affect radiation outcomes). We will develop and validate our prediction algorithms using detailed, existing retrospective datasets from 1,948 prostate cancer patients treated with radiation at Moffitt and 794 treated at the VA, respectively. To date, we have built and refined our retrospective data query and we are currently creating the Moffitt radiotherapy QoL database.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2023
Accession Number
AD1208695

Entities

People

  • Heather Jim
  • Issam El Naqa

Organizations

  • H. Lee Moffitt Cancer Center & Research Institute

Tags

Communities of Interest

  • Autonomy
  • Human Systems

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Biomedical Research
  • Data Analysis
  • Data Mining
  • Data Science
  • Deep Learning
  • Health
  • Health Care
  • Health Services
  • Lymphocytes
  • Machine Learning
  • Medical Personnel
  • Neoplasms
  • Network Architecture
  • Neural Networks
  • Prostate Cancer
  • Quality Of Life
  • Radiation
  • Radiation Dosage
  • Therapy

Fields of Study

  • Medicine
  • Physics

Readers

  • Distributed Systems and Data Platform Development
  • Oncology

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks