Potentiometric multisensor system as a possible simple tool for non-invasive prostate cancer diagnostics through urine analysis

Research output

4 Citations (Scopus)

Abstract

We report a simple potentiometric multisensor system for distinguishing the urine samples from the patients with diagnosed prostate cancer and from healthy control group. The sensors of the system are sensitive towards variety of cationic and anionic species in urine, as well as to the presence of RedOx pairs. The response of the system represents a complex chemical fingerprint of urine sample that can be related with patient status through multivariate modelling. 89 urine samples were studied (43 from cancer patients confirmed by prostatic puncture biopsy and 46 from healthy control group) and variety of multivariate classification techniques was applied to the potentiometric data. The best results were obtained with logistic regression model yielding 100% sensitivity and 93% specificity in the independent test set of samples.

Original languageEnglish
Pages (from-to)42-47
Number of pages6
JournalSensors and Actuators, B: Chemical
Volume289
DOIs
Publication statusPublished - 15 Jun 2019

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Sensor data fusion
urine
cancer
Biopsy
Logistics
Sensors
logistics
regression analysis
sensitivity
sensors
Oxidation-Reduction

Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Instrumentation
  • Condensed Matter Physics
  • Surfaces, Coatings and Films
  • Metals and Alloys
  • Electrical and Electronic Engineering
  • Materials Chemistry

Cite this

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title = "Potentiometric multisensor system as a possible simple tool for non-invasive prostate cancer diagnostics through urine analysis",
abstract = "We report a simple potentiometric multisensor system for distinguishing the urine samples from the patients with diagnosed prostate cancer and from healthy control group. The sensors of the system are sensitive towards variety of cationic and anionic species in urine, as well as to the presence of RedOx pairs. The response of the system represents a complex chemical fingerprint of urine sample that can be related with patient status through multivariate modelling. 89 urine samples were studied (43 from cancer patients confirmed by prostatic puncture biopsy and 46 from healthy control group) and variety of multivariate classification techniques was applied to the potentiometric data. The best results were obtained with logistic regression model yielding 100{\%} sensitivity and 93{\%} specificity in the independent test set of samples.",
keywords = "Classification, Diagnostics, Logistic regression, Potentiometric multisensor system, Prostate cancer",
author = "Svetlana Solovieva and Mikhail Karnaukh and Vitaly Panchuk and Evgeny Andreev and Liudmila Kartsova and Elena Bessonova and Andrey Legin and Ping Wang and Hao Wan and Igor Jahatspanian and Dmitry Kirsanov",
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T1 - Potentiometric multisensor system as a possible simple tool for non-invasive prostate cancer diagnostics through urine analysis

AU - Solovieva, Svetlana

AU - Karnaukh, Mikhail

AU - Panchuk, Vitaly

AU - Andreev, Evgeny

AU - Kartsova, Liudmila

AU - Bessonova, Elena

AU - Legin, Andrey

AU - Wang, Ping

AU - Wan, Hao

AU - Jahatspanian, Igor

AU - Kirsanov, Dmitry

PY - 2019/6/15

Y1 - 2019/6/15

N2 - We report a simple potentiometric multisensor system for distinguishing the urine samples from the patients with diagnosed prostate cancer and from healthy control group. The sensors of the system are sensitive towards variety of cationic and anionic species in urine, as well as to the presence of RedOx pairs. The response of the system represents a complex chemical fingerprint of urine sample that can be related with patient status through multivariate modelling. 89 urine samples were studied (43 from cancer patients confirmed by prostatic puncture biopsy and 46 from healthy control group) and variety of multivariate classification techniques was applied to the potentiometric data. The best results were obtained with logistic regression model yielding 100% sensitivity and 93% specificity in the independent test set of samples.

AB - We report a simple potentiometric multisensor system for distinguishing the urine samples from the patients with diagnosed prostate cancer and from healthy control group. The sensors of the system are sensitive towards variety of cationic and anionic species in urine, as well as to the presence of RedOx pairs. The response of the system represents a complex chemical fingerprint of urine sample that can be related with patient status through multivariate modelling. 89 urine samples were studied (43 from cancer patients confirmed by prostatic puncture biopsy and 46 from healthy control group) and variety of multivariate classification techniques was applied to the potentiometric data. The best results were obtained with logistic regression model yielding 100% sensitivity and 93% specificity in the independent test set of samples.

KW - Classification

KW - Diagnostics

KW - Logistic regression

KW - Potentiometric multisensor system

KW - Prostate cancer

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JO - Sensors and Actuators, B: Chemical

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