A novel approach for the non-invasive diagnosis of prostate cancer based on urine odour analysis

Bax, Carmen; Capelli, Laura; Grizzi, Fabio; Prudenza, Stefano; Taverna, Gianluigi

doi:10.1109/isoen54820.2022.9789651

Cited by 1 publication

(2 citation statements)

References 12 publications

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“…It should be noted that this proposal is the first study where both sensory systems have been evaluated to analyze urine and breath samples to detect Prostate Cancer [54][55][56][57][58][59][60][61][62][63][64][65][66][67][68][69]. In this study, a dataset was generated from E-senses devices to create ML models using 113 measurements; however, despite this limited number of cases, this representative dataset can provide us with an excellent pathway to generate models to detect possible PCa cases in the future.…”

Section: Discussionmentioning

confidence: 99%

“…Filianoti, et al in 2022 conducted a study to determine the efficiency of the urinary volatiloma profile to distinguish patients with PCa from healthy controls, using a commercial nose called Cyranose C320, in which a sensitivity of 82.7% was obtained, and a specificity of 88.5% for the technique [58]. Likewise, other studies have been reported in different sources [59][60][61][62].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prostate Cancer Detection in Colombian Patients through E-Senses Devices in Exhaled Breath and Urine Samples

Durán Acevedo,

Carrillo Gómez,

Cuastumal Vasquez

et al. 2024

Chemosensors

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This work consists of a study to detect prostate cancer using E-senses devices based on electronic tongue and electronic nose systems. Therefore, two groups of confirmed prostate cancer and control patients were invited to participate through urine and exhaled breath samples, where the control patients group was categorized as Benign Prostatic Hyperplasia, Prostatitis, and Healthy patients. Afterward, the samples were subsequently classified using Pattern Recognition and machine learning methods, where the results were compared through clinical history, obtaining a 92.9% success rate in the PCa and control samples’ classification accuracy by using eTongue and a 100% success rate of classification using eNose.

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