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Rapid Detection of Recurrent Non-Muscle Invasive Bladder Cancer in Urine Using ATR-FTIR Technology
Non-muscle Invasive Bladder Cancer (NMIBC) accounts for 80% of all bladder cancers. Although it is mostly low-grade tumors, its high recurrence rate necessitates three-times-monthly follow-ups and cystoscopy examinations to detect and prevent its progression. A rapid liquid biopsy-based assay is nee...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9785428/ https://www.ncbi.nlm.nih.gov/pubmed/36558023 http://dx.doi.org/10.3390/molecules27248890 |
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author | El-Falouji, Abdullah I. Sabri, Dalia M. Lotfi, Naira M. Medany, Doaa M. Mohamed, Samar A. Alaa-eldin, Mai Selim, Amr Mounir El Leithy, Asmaa A. Kalil, Haitham El-Tobgy, Ahmed Mohamed, Ahmed |
author_facet | El-Falouji, Abdullah I. Sabri, Dalia M. Lotfi, Naira M. Medany, Doaa M. Mohamed, Samar A. Alaa-eldin, Mai Selim, Amr Mounir El Leithy, Asmaa A. Kalil, Haitham El-Tobgy, Ahmed Mohamed, Ahmed |
author_sort | El-Falouji, Abdullah I. |
collection | PubMed |
description | Non-muscle Invasive Bladder Cancer (NMIBC) accounts for 80% of all bladder cancers. Although it is mostly low-grade tumors, its high recurrence rate necessitates three-times-monthly follow-ups and cystoscopy examinations to detect and prevent its progression. A rapid liquid biopsy-based assay is needed to improve detection and reduce complications from invasive cystoscopy. Here, we present a rapid spectroscopic method to detect the recurrence of NMIBC in urine. Urine samples from previously-diagnosed NMIBC patients (n = 62) were collected during their follow-up visits before cystoscopy examination. Cystoscopy results were recorded (41 cancer-free and 21 recurrence) and attenuated total refraction Fourier transform infrared (ATR-FTIR) spectra were acquired from urine samples using direct application. Spectral processing and normalization were optimized using parameter grid searching. We assessed their technical variability through multivariate analysis and principal component analysis (PCA). We assessed 35 machine learning models on a training set (70%), and the performance was evaluated on a held-out test set (30%). A Regularized Random Forests (RRF) model achieved a 0.92 area under the receiver operating characteristic (AUROC) with 86% sensitivity and 77% specificity. In conclusion, our spectroscopic liquid biopsy approach provides a promising technique for the early identification of NMIBC with a less invasive examination. |
format | Online Article Text |
id | pubmed-9785428 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97854282022-12-24 Rapid Detection of Recurrent Non-Muscle Invasive Bladder Cancer in Urine Using ATR-FTIR Technology El-Falouji, Abdullah I. Sabri, Dalia M. Lotfi, Naira M. Medany, Doaa M. Mohamed, Samar A. Alaa-eldin, Mai Selim, Amr Mounir El Leithy, Asmaa A. Kalil, Haitham El-Tobgy, Ahmed Mohamed, Ahmed Molecules Article Non-muscle Invasive Bladder Cancer (NMIBC) accounts for 80% of all bladder cancers. Although it is mostly low-grade tumors, its high recurrence rate necessitates three-times-monthly follow-ups and cystoscopy examinations to detect and prevent its progression. A rapid liquid biopsy-based assay is needed to improve detection and reduce complications from invasive cystoscopy. Here, we present a rapid spectroscopic method to detect the recurrence of NMIBC in urine. Urine samples from previously-diagnosed NMIBC patients (n = 62) were collected during their follow-up visits before cystoscopy examination. Cystoscopy results were recorded (41 cancer-free and 21 recurrence) and attenuated total refraction Fourier transform infrared (ATR-FTIR) spectra were acquired from urine samples using direct application. Spectral processing and normalization were optimized using parameter grid searching. We assessed their technical variability through multivariate analysis and principal component analysis (PCA). We assessed 35 machine learning models on a training set (70%), and the performance was evaluated on a held-out test set (30%). A Regularized Random Forests (RRF) model achieved a 0.92 area under the receiver operating characteristic (AUROC) with 86% sensitivity and 77% specificity. In conclusion, our spectroscopic liquid biopsy approach provides a promising technique for the early identification of NMIBC with a less invasive examination. MDPI 2022-12-14 /pmc/articles/PMC9785428/ /pubmed/36558023 http://dx.doi.org/10.3390/molecules27248890 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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/licenses/by/4.0/). |
spellingShingle | Article El-Falouji, Abdullah I. Sabri, Dalia M. Lotfi, Naira M. Medany, Doaa M. Mohamed, Samar A. Alaa-eldin, Mai Selim, Amr Mounir El Leithy, Asmaa A. Kalil, Haitham El-Tobgy, Ahmed Mohamed, Ahmed Rapid Detection of Recurrent Non-Muscle Invasive Bladder Cancer in Urine Using ATR-FTIR Technology |
title | Rapid Detection of Recurrent Non-Muscle Invasive Bladder Cancer in Urine Using ATR-FTIR Technology |
title_full | Rapid Detection of Recurrent Non-Muscle Invasive Bladder Cancer in Urine Using ATR-FTIR Technology |
title_fullStr | Rapid Detection of Recurrent Non-Muscle Invasive Bladder Cancer in Urine Using ATR-FTIR Technology |
title_full_unstemmed | Rapid Detection of Recurrent Non-Muscle Invasive Bladder Cancer in Urine Using ATR-FTIR Technology |
title_short | Rapid Detection of Recurrent Non-Muscle Invasive Bladder Cancer in Urine Using ATR-FTIR Technology |
title_sort | rapid detection of recurrent non-muscle invasive bladder cancer in urine using atr-ftir technology |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9785428/ https://www.ncbi.nlm.nih.gov/pubmed/36558023 http://dx.doi.org/10.3390/molecules27248890 |
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