Prediction of drug amount in Parkinson's disease using hybrid machine learning systems and radiomics features

Abstract

AbstractParkinson's disease (PD) is progressive and heterogeneous. Levodopa is widely prescribed to control PD, and its long‐term‐treatment leads to dyskinesia in a dose‐dependent manner. Interpretation of clinical trials comparing different drug treatments for PD is complicated by different dose intensities employed: higher doses of levodopa produce better symptomatic control but more late complications. Thus, the dose must be recalibrated and reduced gradually. Since recommendations for gradually reducing Levodopa are currently lacking and estimation of Levodopa amount can help doctor to correctly prescribe drug amount, this study aims to predict Levodopa amount and incremental doses using Hybrid Machine Learning Systems (HMLS) and a mixture of radiomics and clinical features. We selected 264 patients from PPMI and obtained 950 features including imaging and nominating features. We generated seven datasets constructed from the dataset in years 0 and 1, which linked with outcomes, (O1) patients being on/off drug in year 1, (O2) dose amount in year 1, and (O3‐8) incremental dose from 1st to 2nd, 2nd to 3rd, 3rd to 4th, 4th to 5th, 1st to 4th, 1st to 5th year. HMLSs included 10 feature extraction/9 feature selection algorithms followed by 10 prediction algorithms. To predict O1, timeless dataset + Random Forest + ReliefA had the highest accuracy~88.5% ± 2.2%, and external testing~91.6%. Furthermore, to predict O2, timeless dataset + Minimum Redundancy Maximum Relevance Algorithm (MRMR) + K Nearest Neighbor Regressor (KNN‐R) achieved a mean absolute error (MAE) ~ 47.5 ± 13.6 ([30.3:850 milligram]) and external testing~31.9. To predict dose increments (O3‐8), HMLSs: Unsupervised Feature Selection with Ordinal Locality + KNNR, ReliefA + KNNR, ReliefA + KNNR, Local Learning‐based Clustering Feature Selection + KNNR, MRMR + KNNR, and MRMR + KNNR applied to timeless datasets resulted in MAEs ~ 0.42 ± 0.18, 0.10 ± 0.09, 0.04 ± 0.01, 0.24 ± 0.15, 0.25 ± 0.05, and 0.33 ± 0.26 ([0.23:29.7]), respectively. Moreover, their external testing confirmed our findings. We demonstrated that timeless datasets including a mixture of clinical and imaging features, linked with appropriate HMLSs, significantly improve prediction performances.