Optimal dose escalation methods using deep reinforcement learning in phase I oncology trials

In phase I trials of a novel anticancer drug, one of the most important objectives is to identify the maximum tolerated dose (MTD). To this end, a number of methods have been proposed and evaluated under various scenarios. However, the percentages of correct selection (PCS) of MTDs using previous methods are insufficient to determine the dose for late-phase trials. The purpose of this study is to construct an action rule for escalating or de-escalating the dose and continuing or stopping the trial to increase the PCS as much as possible. We show that deep reinforcement learning with an appropriately defined state, action, and reward can be used to construct such an action selection rule. The simulation study shows that the proposed method can improve the PCS compared with the 3 + 3 design, CRM, BLRM, BOIN, mTPI, and i3 + 3 methods.</p

Pregnancy outcomes for overweight women in reference to weight gain.

<p>Pregnancy outcomes for overweight women in reference to weight gain.</p

Pregnancy outcomes for normal weight women in reference to weight gain.

<p>Pregnancy outcomes for normal weight women in reference to weight gain.</p

Flow diagram of study inclusion.

<p>^aBMI, body mass index; ^bGWG, gestational weight gain.</p

Comparison of maternal characteristics between pre-pregnancy underweight, normal weight, overweight and obese women.

<p>Comparison of maternal characteristics between pre-pregnancy underweight, normal weight, overweight and obese women.</p

Pregnancy outcomes for obese women in reference to weight gain.

<p>Pregnancy outcomes for obese women in reference to weight gain.</p

Pregnancy outcomes for underweight women in reference to weight gain.

<p>Pregnancy outcomes for underweight women in reference to weight gain.</p

Comparison of patient and stone characteristics among the procedures with stone-free status, residual fragments less than 4mm and residual fragments more than 4mm.

<p>Comparison of patient and stone characteristics among the procedures with stone-free status, residual fragments less than 4mm and residual fragments more than 4mm.</p

Summary of linear regression model for variables predicting the operative time of flexible ureteroscopy with lithotripsy for the treatment of renal stone (N = 316).

<p>Summary of linear regression model for variables predicting the operative time of flexible ureteroscopy with lithotripsy for the treatment of renal stone (N = 316).</p

A new prediction model for operative time of flexible ureteroscopy with lithotripsy for the treatment of renal stones

<div><p>This study aimed to develop a prediction model for the operative time of flexible ureteroscopy (fURS) for renal stones. We retrospectively evaluated patients with renal stones who had been treated successfully and had stone-free status determined by non-contrast computed tomography (NCCT) 3 months after fURS and holmium laser lithotripsy between December 2009 and September 2014 at a single institute. Correlations between possible factors and the operative time were analyzed using Spearman’s correlation coefficients and a multivariate linear regression model. The P value < 0.1 was used for entry of variables into the model and for keeping the variables in the model. Internal validation was performed using 10,000 bootstrap resamples. Flexible URS was performed in 472 patients, and 316 patients were considered to have stone-free status and were enrolled in this study. Spearman’s correlation coefficients showed a significant positive relationship between the operation time and stone volume (ρ = 0.417, p < 0.001), and between the operation time and maximum Hounsfield units (ρ = 0.323, p < 0.001). A multivariate assessment with forced entry and stepwise selection revealed six factors to predict the operative time of fURS: preoperative stenting, stone volume, maximum Hounsfield unit, surgeon experience, sex, and sheath diameter. Based on this finding, we developed a model to predict operative time of fURS. The coefficient of determination (R²) in this model was 0.319; the mean R² value for the prediction model was 0.320 ± 0.049. To our knowledge, this is the first report of a model for predicting the operative time of fURS treatment of renal stones. The model may be used to reliably predict operative time preoperatively based on patient characteristics and the surgeons’ experience, plan staged URS, and avoid surgical complications.</p></div