[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117389-en":3,"doc-seo-117389-105":30,"detail-sidebar-cat-0-en-105":95},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117389,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",7,"Healthcare","Liquid Biopsy Based Bladder Cancer Diagnostic by Machine Learning","Timely bladder cancer diagnostics remain challenging, as conventional tests show limited accuracy, sensitivity, and often rely on invasive procedures. This study applies machine learning to improve diagnosis by integrating urinary exosome miRNA expression, demographic and routine laboratory clinical data, and associated clinical features. Three classifiers are evaluated—Random Forest, SVM, and XGBoost—with ROC and F1 metrics. The SVM model reaches an ROC of 0.75 using miRNA alone, while clinical-demographic features reach 0.80; combining modalities yields an F1 score of 0.79 and ROC of 0.85. Feature importance highlights key predictors including urinary erythrocytes, age, and multiple miRNAs. Results support a non-invasive multi-modal approach for more accurate bladder cancer detection.","Article  \nLiquid Biopsy Based Bladder Cancer Diagnostic by Machine Learning  \n¯Erika Bitin, a-Barlote 1,2,*, Dmitrijs Bizn, uks 3, Sanda Silin, a 4, Mihails Šatcs 1, Egils Vjaters 1,2, Vilnis Lietuvietis 4, Miki Nakazawa-Miklaševiˇca 1,5, Juris Plonis 1,2, Edv¯ıns Miklaševiˇcs 1,5, Zanda Daneberga 1,5  \nand Jnis Gardovskis 6,7  \nAcademic Editor: TheoM. deReijke  \nReceived: 1 December 2024  \nRevised: 27 January 2025  \nAccepted: 8 February 2025  \nPublished: 18 February 2025  \nCitation: Bitin, a-Barlote, ¯E.; Bizn, uks, D.; Silin, a, S.; Šatcs, M.; Vjaters, E.; Lietuvietis, V.; Nakazawa-Miklaševiˇca, M.; Plonis, J.; Miklaševiˇcs, E.; Daneberga, Z.; et al. Liquid Biopsy Based Bladder Cancer Diagnostic by Machine Learning. Diagnostics 2025, 15, 492. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)diagnostics15040492  \nCopyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \n1 Institute of Oncology and Molecular Genetics, Riga Stradins University, LV-1002 Riga, Latvia  \n2 Department of Urology, Paul Stradins Clinical University Hospital, LV-1002 Riga, Latvia  \n3 Institute of Applied Computer Systems, Faculty of Computer Science, Information Technology and Energy, Riga Technical University, LV-1048 Riga, Latvia  \n4 Clinic of Urology and Oncological Urology, Riga East University Hospital, LV-1079 Riga, Latvia  \n5 Department of Biology and Microbiology, Riga Stradins University, LV-1007 Riga, Latvia  \n6 Department of Surgery, Riga Stradins University, LV-1002 Riga, Latvia  \n7 Department of Surgery, Paul Stradins Clinical University Hospital, LV-1002 Riga, Latvia  \n* Correspondence: [eribit@rsu.lv](eribit@rsu.lv)  \nAbstract: Background/Objectives: The timely diagnostics of bladder cancer is still a challenge in clinical settings. The reliability of conventional testing methods does not reach desirable accuracy and sensitivity, and it has an invasive nature. The present study examines the application of machine learning to improve bladder cancer diagnostics by integrating miRNA expression levels, demographic routine laboratory test results, and clinical data. We proposed that merging these datasets would enhance diagnostic accuracy. 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, as well as clinical and demographic data using three machine learning models: Random Forest, SVM, and XGBoost classifiers. 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.  \nKeywords: artificial intelligence; machine learning; miRNAs; bladder cancer; multi-modal data; biomarker; liquid biopsy; biofluids; urine exosomes  \n1. Introduction  \nThe current one-size-fits-all approach in cancer medicine is rapidly moving towards precision oncology, tailoring diagnostics and subsequent therapies to the unique characteristics of each individual patient. The ninth most common malignancy in the world is bladder cancer [1] . It is observed that bladder cancer occurs three times more often in men than in women. Approximately 70% of patients with bladder cancer are over","cbCaig1Cez9Kkiug","https://ap.wps.com/l/cbCaig1Cez9Kkiug","pdf",2043041,1,16,"English","en",105,"# Introduction\n# Materials and Methods\n## Liquid biopsy and data sources\n## Machine learning models\n# Results\n## Model performance and ROC/F1 metrics\n## Feature importance\n# Conclusions","[{\"question\":\"What data sources does the study integrate for bladder cancer diagnosis?\",\"answer\":\"It combines urinary exosome miRNA expression levels with demographic information and routine laboratory clinical test results, along with other clinical data.\"},{\"question\":\"Which machine learning models are evaluated in the study?\",\"answer\":\"The study evaluates Random Forest, SVM, and XGBoost classifiers.\"},{\"question\":\"How does combining miRNA data with clinical-demographic data affect diagnostic performance?\",\"answer\":\"The combined multi-modal approach improves performance, reaching an F1 score of 0.79 and ROC of 0.85, compared with 0.75 (miRNA-only SVM) and 0.80 (clinical-demographic features alone).\"},{\"question\":\"What predictors are identified as important for the diagnosis?\",\"answer\":\"Feature importance analysis highlights key predictors including urinary erythrocytes, patient age, and several miRNAs.\"}]","Liquid Biopsy Based Bladder Cancer Diagnostic by Machine Learning | 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