Prediction of Future Parkinson Disease Using Plasma Proteins Combined With Clinical-Demographic Measures

An interpretable machine learning model based on computed tomography radiomics for predicting programmed death ligand 1 expression status in gastric cancer

BACKGROUND

Programmed death ligand 1 (PD-L1) expression status, closely related to immunotherapy outcomes, is a reliable biomarker for screening patients who may benefit from immunotherapy. Here, we developed and validated an interpretable machine learning (ML) model based on contrast-enhanced computed tomography (CECT) radiomics for preoperatively predicting PD-L1 expression status in patients with gastric cancer (GC).

METHODS

We retrospectively recruited 285 GC patients who underwent CECT and PD-L1 detection from two medical centers. A PD-L1 combined positive score (CPS) of ≥ 5 was considered to indicate a high PD-L1 expression status. Patients from center 1 were divided into training (n = 143) and validation sets (n = 62), and patients from center 2 were considered a test set (n = 80). Radiomics features were extracted from venous-phase CT images. After feature reduction and selection, 11 ML algorithms were employed to develop predictive models, and their performance in predicting PD-L1 expression status was evaluated using areas under receiver operating characteristic curves (AUCs). SHapley Additive exPlanations (SHAP) were used to interpret the optimal model and visualize the decision-making process for a single individual.

RESULTS

Nine features significantly associated with PD-L1 expression status were ultimately selected to construct the predictive model. The light gradient-boosting machine (LGBM) model demonstrated the best performance for PD-L1 high expression status prediction in the training, validation, and test sets, with AUCs of 0.841(95% CI: 0.773, 0.908), 0.834 (95% CI:0.729, 0.939), and 0.822 (95% CI: 0.718, 0.926), respectively. The SHAP summary and bar plots illustrated that a feature's value affected the feature's impact attributed to the model. The SHAP waterfall plots were used to visualize the decision-making process for a single individual.

CONCLUSION

Our CT radiomics-based LGBM model may aid in preoperatively predicting PD-L1 expression status in GC patients, and the SHAP method may improve the interpretability of this model.

Proximity Elongation Assay and ELISA for the Identification of Serum Diagnostic Biomarkers in Parkinson's Disease and Progressive Supranuclear Palsy

Clinical differentiation of progressive supranuclear palsy (PSP) from Parkinson's disease (PD) is challenging due to overlapping phenotypes and late onset of PSP specific symptoms, highlighting the need for easily assessable biomarkers. We used proximity elongation assay (PEA) to analyze 460 proteins in serum samples from 46 PD, 30 PSP patients, and 24 healthy controls. ANCOVA was used to identify the most promising proteins and machine learning (ML) XGBoost and random forest algorithms to assess their classification performance. Promising proteins were also quantified by ELISA. Moreover, correlations between serum biomarkers and biological and clinical features were investigated. We identified five proteins (TFF3, CPB1, OPG, CNTN1, TIMP4) showing different levels between PSP and PD, which achieved good performance (AUC: 0.892) when combined by ML. On the other hand, when the three most significant biomarkers (TFF3, CPB1 and OPG) were analyzed by ELISA, there was no difference between groups. Serum levels of TFF3 positively correlated with age in all subjects' groups, while for OPG and CPB1 such a correlation occurred in PSP patients only. Moreover, CPB1 positively correlated with disease severity in PD, while no correlations were observed in the PSP group. Overall, we identified CPB1 correlating with PD severity, which may support clinical staging of PD. In addition, our results showing discrepancy between PEA and ELISA technology suggest that caution should be used when translating proteomic findings into clinical practice.