Biomarkers to assess the risk of bladder cancer in patients presenting with haematuria are gender-specific

Duggan, Brian; O’Rourke, Declan; Anderson, Neil; Reid, Cherith N.; Watt, Joanne; O'Kane, Hugh F.; Boyd, Ruth; Curry, David J.; Evans, Mark I.; Stevenson, Michael; Kurth, Mary Jo; Lamont, John; Fitzgerald, Peter; Ruddock, Mark W.

doi:10.3389/fonc.2022.1009014

Cited by 4 publications

(6 citation statements)

References 33 publications

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“…Males, older patients, and smokers have significantly higher malignancy risk ( 36 – 39 ). On the other hand, women do not receive the same diagnostic attention, which leads to delays in urological consultation and poorer oncological outcomes in bladder cancer ( 22 , 40 , 41 ). It is therefore crucial to provide gender-specific blood or urine biomarkers which could reduce the time and harm associated with the current methods, while being affordable and addressing gender inequalities in the diagnostic process.…”

Section: Discussionmentioning

confidence: 99%

“…HaBio was funded by Invest Northern Ireland and Randox Laboratories Ltd. Ethical approval was obtained from the Office for Research Ethics Committee Northern Ireland (11/NI/0164) to recruit patients who satisfied the HaBio study inclusion criteria ( 22 ). The protocol for HaBio was also reviewed by hospital review boards and was conducted according to the Standards for Reporting of Diagnostic Accuracy (STARD) ( 31 ).…”

Section: Methodsmentioning

confidence: 99%

“…Detailed description of analytical procedures is provided in Supplementary Materials . When data was below the Limit of Detection (LOD) or the Mean Detectable Dose (MDD) for any given test, 90% of the LOD or the MDD was used in lieu of the actual value for analysis ( 22 ).…”

Section: Methodsmentioning

confidence: 99%

“…Numerous studies have proved that by using advanced analytical methods, it is possible to create algorithms that can improve patient diagnosis with multiple biomarker analysis ( 15 , 21 , 22 ). Machine Learning (ML) ( 23 , 24 ), especially, has been able to produce unique insights using different data sources ( 25 – 27 ).…”

Section: Introductionmentioning

confidence: 99%

“…The Haematuria Biomarker (HaBio) dataset ( 22 ) is a unique collection of data illustrative of a patient population presenting with haematuria and includes an extensive range of biomarkers preselected based on literature searches and clinical experience. At the same time, HaBio presents all the challenges for ML described above.…”

Section: Introductionmentioning

confidence: 99%

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Stratifying risk of disease in haematuria patients using machine learning techniques to improve diagnostics

Drożdż,

Duggan,

Ruddock

et al. 2024

Front. Oncol.

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BackgroundDetailed and invasive clinical investigations are required to identify the causes of haematuria. Highly unbalanced patient population (predominantly male) and a wide range of potential causes make the ability to correctly classify patients and identify patient-specific biomarkers a major challenge. Studies have shown that it is possible to improve the diagnosis using multi-marker analysis, even in unbalanced datasets, by applying advanced analytical methods. Here, we applied several machine learning algorithms to classify patients from the haematuria patient cohort (HaBio) by analysing multiple biomarkers and to identify the most relevant ones.Materials and methodsWe applied several classification and feature selection methods (k-means clustering, decision trees, random forest with LIME explainer and CACTUS algorithm) to stratify patients into two groups: healthy (with no clear cause of haematuria) or sick (with an identified cause of haematuria e.g., bladder cancer, or infection). The classification performance of the models was compared. Biomarkers identified as important by the algorithms were also analysed in relation to their involvement in the pathological processes.ResultsResults showed that a high unbalance in the datasets significantly affected the classification by random forest and decision trees, leading to the overestimation of the sick class and low model performance. CACTUS algorithm was more robust to the unbalance in the dataset. CACTUS obtained a balanced accuracy of 0.747 for both genders, 0.718 for females and 0.803 for males. The analysis showed that in the classification process for the whole dataset: microalbumin, male gender, and tPSA emerged as the most informative biomarkers. For males: age, microalbumin, tPSA, cystatin C, BTA, HAD and S100A4 were the most significant biomarkers while for females microalbumin, IL-8, pERK, and CXCL16.ConclusionsCACTUS algorithm demonstrated improved performance compared with other methods such as decision trees and random forest. Additionally, we identified the most relevant biomarkers for the specific patient group, which could be considered in the future as novel biomarkers for diagnosis. Our results have the potential to inform future research and provide new personalised diagnostic approaches tailored directly to the needs of the individuals.

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