Machine learning model for predicting DIBH non-eligibility in left-sided breast cancer radiotherapy: Development, validation and clinical impact analysis.

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
PMID: 39894262

Abstract

OBJECTIVE: Multi-day assessments accurately identify patients with left-sided breast cancer who are ineligible for irradiation in Deep Inspiration Breath Hold (DIBH) and minimise on-couch treatment time in those who are eligible. The challenge of implementing multi-day assessments in resource-constrained settings motivated the development of a machine learning (ML) model using data only from the 1st day of assessment to predict DIBH ineligibility.

Authors

  • Kundan Singh Chufal
    Department of Radiation Oncology, Rajiv Gandhi Cancer Institute & Research Centre, New Delhi, India. Electronic address: kundan25@gmail.com.
  • Irfan Ahmad
    Department of Clinical Laboratory Science, College of Applied Medical Sciences, King Khalid University, Abha 61421, Saudi Arabia.
  • Alexis Andrew Miller
    Department of Radiation Oncology, Illawarra Cancer Care Centre, Wollongong, New South Wales, Australia.
  • Ram Bajpai
    School of Primary, Community and Social Care, Keele University, Keele, UK.
  • Avani Dwivedi
    Department of Computer Science, Royal Holloway University of London, United Kingdom.
  • Alok Dwivedi
    Discover Financial Services, Reading, United Kingdom.
  • Preetha Umesh
    Department of Radiation Oncology, Rajiv Gandhi Cancer Institute & Research Centre, New Delhi, India.
  • Kratika Bhatia
    Department of Radiation Oncology, Rajiv Gandhi Cancer Institute & Research Centre, New Delhi, India.
  • Munish Gairola
    Department of Radiation Oncology, Rajiv Gandhi Cancer Institute & Research Centre, New Delhi, India.

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