Virtual clinical trial based on outcome modeling with iteratively redistributed extrapolation data.
Extrapolation data
Outcome modeling
Outcome prediction
Radiotherapy
Virtual clinical trial
Journal
Radiological physics and technology
ISSN: 1865-0341
Titre abrégé: Radiol Phys Technol
Pays: Japan
ID NLM: 101467995
Informations de publication
Date de publication:
Jun 2023
Jun 2023
Historique:
received:
10
10
2022
accepted:
15
03
2023
revised:
14
03
2023
medline:
26
5
2023
pubmed:
23
3
2023
entrez:
22
3
2023
Statut:
ppublish
Résumé
Virtual clinical trials (VCTs) can potentially simulate clinical trials on a computer, but their application with a limited number of past clinical cases is challenging due to the biased estimation of the statistical population. In this study, we developed ExMixup, a novel training technique based on machine learning, using iteratively redistributed extrapolated data. Information obtained from 100 patients with prostate cancer and 385 patients with oropharyngeal cancer was used to predict the recurrence after radiotherapy. Model performance was evaluated by developing outcome prediction models based on three types of training methods: training with original data (baseline), interpolation data (Mixup), and interpolation + extrapolation data (ExMixup). Two types of VCTs were conducted to predict the treatment response of patients with distinct characteristics compared to the training data obtained from patient cohorts categorized under risk classification or cancer stage. The prediction models developed with ExMixup yielded concordance indices (95% confidence intervals) of 0.751 (0.719-0.818) and 0.752 (0.734-0.785) for VCTs on the prostate and oropharyngeal cancer datasets, respectively, which significantly outperformed the baseline and Mixup models (P < 0.01). The proposed approach could enhance the ability of VCTs to predict treatment results in patients excluded from past clinical trials.
Identifiants
pubmed: 36947353
doi: 10.1007/s12194-023-00715-4
pii: 10.1007/s12194-023-00715-4
doi:
Types de publication
Clinical Trial
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
262-271Subventions
Organisme : Japan Society for the Promotion of Science London
ID : 18K15604
Informations de copyright
© 2023. The Author(s), under exclusive licence to Japanese Society of Radiological Technology and Japan Society of Medical Physics.
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