Virtual clinical trial based on outcome modeling with iteratively redistributed extrapolation data

In this study, we developedExMixup, 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 withExMixup 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 andMixup models (P <  0.01). The proposed approach could enhance the ability of VCTs to predict treatment results in patients excluded from past clinical trials.
Source: Radiological Physics and Technology - Category: Physics Source Type: research