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Drift compensation on electronic nose data for non-invasive diagnosis of prostate cancer by urine analysis.
Bax, Carmen; Prudenza, Stefano; Gaspari, Giulia; Capelli, Laura; Grizzi, Fabio; Taverna, Gianluigi.
Afiliación
  • Bax C; Politecnico di Milano, Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", piazza Leonardo da Vinci 32, Milan 20133, Italy.
  • Prudenza S; Politecnico di Milano, Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", piazza Leonardo da Vinci 32, Milan 20133, Italy.
  • Gaspari G; Politecnico di Milano, Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", piazza Leonardo da Vinci 32, Milan 20133, Italy.
  • Capelli L; Politecnico di Milano, Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", piazza Leonardo da Vinci 32, Milan 20133, Italy.
  • Grizzi F; Department of Immunology and Inflammation, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, Milan 20089, Italy.
  • Taverna G; Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20090 Milan, Italy.
iScience ; 25(1): 103622, 2022 Jan 21.
Article en En | MEDLINE | ID: mdl-35024578
Diagnostic protocol for prostate cancer (KP) is affected by poor accuracy and high false-positive rate. The most promising innovative approach is based on urine analysis by electronic noses (ENs), highlighting a specific correlation between urine alteration and KP presence. Although EN could be exploited to develop non-invasive KP diagnostic tools, no study has already introduced EN into clinical practice, most probably because of drift issues that hinder EN scaling up from research objects to large-scale diagnostic devices. This study, proposing an EN for non-invasive KP detection, describes the data processing protocol applied to a urine headspace dataset acquired over 9 months, comprising 81 patients with KP and 41 controls, for compensating the drift. It proved effective in mitigating drift on 1-year-old sensors by restoring accuracy from 55% up to 80%, achieved by new sensors not subjected to drift. The model achieved, on double-blind validation, a balanced accuracy of 76.2% (CI95% 51.9-92.3).
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Clinical_trials / Diagnostic_studies / Guideline Idioma: En Revista: IScience Año: 2022 Tipo del documento: Article País de afiliación: Italia Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Clinical_trials / Diagnostic_studies / Guideline Idioma: En Revista: IScience Año: 2022 Tipo del documento: Article País de afiliación: Italia Pais de publicación: Estados Unidos