Conclusions
Our findings indicate the potential of a multi-modal approach to improve the accuracy of bladder cancer diagnosis in a non-invasive manner.
Methods
This study combined molecular biology methods for liquid biopsy, routine clinical data, and application of machine learning approach for the acquired data analysis. We evaluated urinary exosome miRNA expression data in combination with patient test
Results
Based solely on miRNA data, the SVM model achieved an ROC curve area of 0.75. Patient analysis' clinical and demographic data obtained ROC curve area of 0.80. Combining both data types enhanced performance, resulting in an F1 score of 0.79 and an ROC of 0.85. The feature importance analysis identified key predictors, including erythrocytes in urine, age, and several miRNAs. Conclusions: Our findings indicate the potential of a multi-modal approach to improve the accuracy of bladder cancer diagnosis in a non-invasive manner.