PlumX Metrics
Embed PlumX Metrics

Clinical integration of machine learning for curative-intent radiation treatment of patients with prostate cancer

Nature Medicine, ISSN: 1546-170X, Vol: 27, Issue: 6, Page: 999-1005
2021
  • 112
    Citations
  • 0
    Usage
  • 175
    Captures
  • 9
    Mentions
  • 142
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    112
  • Captures
    175
  • Mentions
    9
    • News Mentions
      9
      • 9
  • Social Media
    142
    • Shares, Likes & Comments
      142
      • Facebook
        142

Most Recent News

AI Use in Prostate Cancer: Potential Improvements in Treatments and Patient Care

Artificial intelligence use in prostate cancer encompasses 4 main areas including diagnostic imaging, prediction of outcomes, histopathology, and treatment planning. Artificial intelligence (AI) generally describes

Article Description

Machine learning (ML) holds great promise for impacting healthcare delivery; however, to date most methods are tested in ‘simulated’ environments that cannot recapitulate factors influencing real-world clinical practice. We prospectively deployed and evaluated a random forest algorithm for therapeutic curative-intent radiation therapy (RT) treatment planning for prostate cancer in a blinded, head-to-head study with full integration into the clinical workflow. ML- and human-generated RT treatment plans were directly compared in a retrospective simulation with retesting (n = 50) and a prospective clinical deployment (n = 50) phase. Consistently throughout the study phases, treating physicians assessed ML- and human-generated RT treatment plans in a blinded manner following a priori defined standardized criteria and peer review processes, with the selected RT plan in the prospective phase delivered for patient treatment. Overall, 89% of ML-generated RT plans were considered clinically acceptable and 72% were selected over human-generated RT plans in head-to-head comparisons. RT planning using ML reduced the median time required for the entire RT planning process by 60.1% (118 to 47 h). While ML RT plan acceptability remained stable between the simulation and deployment phases (92 versus 86%), the number of ML RT plans selected for treatment was significantly reduced (83 versus 61%, respectively). These findings highlight that retrospective or simulated evaluation of ML methods, even under expert blinded review, may not be representative of algorithm acceptance in a real-world clinical setting when patient care is at stake.

Bibliographic Details

McIntosh, Chris; Conroy, Leigh; Tjong, Michael C; Craig, Tim; Bayley, Andrew; Catton, Charles; Gospodarowicz, Mary; Helou, Joelle; Isfahanian, Naghmeh; Kong, Vickie; Lam, Tony; Raman, Srinivas; Warde, Padraig; Chung, Peter; Berlin, Alejandro; Purdie, Thomas G

Springer Science and Business Media LLC

Biochemistry, Genetics and Molecular Biology

Provide Feedback

Have ideas for a new metric? Would you like to see something else here?Let us know