LAUSR

To achieve personalized and optimized treatment for rectal cancer patients, accurate predictions of treatment response based on pretreatment medical images are essential. However, these images often have varying settings. Our study examined the impact of standardizing pixel spacing and slice thickness on predictive accuracy in medical image analysis. Using our custom-built evolutionary random subspace forest (ERSF) algorithm, we investigated how altering these spatial settings affected the accuracy of tumor regression grade (TRG) prediction. We examined 16 different adjustments to spacing settings in computed tomography (CT) images taken from 139 rectal cancer patients. Furthermore, we explored a Bayesian approach within our random forest (RF) algorithm. We utilized the modified data as prior information and employed radiomics data from the same CT images as posterior information. This study revealed that these alterations notably improved both training and validation accuracy, whereas the Bayesian approach enhanced model generalization, indicating a close alignment between training and validation results.