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|a 10.1080/02664763.2024.2346355
|2 doi
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|a eng
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| 100 |
1 |
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|a Quaye, George Ekow
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Urinary volatile organic compounds (VOCs) based prostate cancer diagnosis via high-dimensional classification
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|c 2024
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 05.12.2024
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|a published: Electronic-eCollection
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|a Citation Status PubMed-not-MEDLINE
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|a © 2024 Informa UK Limited, trading as Taylor & Francis Group.
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|a Early detection of prostate cancer is critical for successful treatment and survival. However, current diagnostic methods such as prostate-specific antigen (PSA) testing and digital rectal examination (DRE) have limitations in accuracy, specificity, and sensitivity. Recent research suggests that urinary volatile organic compounds (VOCs) could serve as potential biomarkers for prostate cancer diagnosis. In this study, urine samples from 337 PCa-positive and 233 PCa-negative patients were collected to develop a diagnosis model. The study involves a high dimensional (HD) classification problem due to the vast number of measured VOCs. Our findings reveal that regularized logistic regression outperforms numerous other classifiers when analyzing the collected data. In particular, we have selected a regularized logistic model with the SCAD (smoothly clipped absolute deviation) penalty as the final model, which attains an AUC (area under the ROC curve) of 0.748, in contrast to a PSA-based AUC of 0.540. These results underscore the potential of VOC-based diagnosis as a clinically feasible approach for PCa screening
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|a Journal Article
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4 |
|a Classification
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4 |
|a PLS-DA
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| 650 |
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4 |
|a high dimensional (HD) modeling
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| 650 |
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4 |
|a prostate cancer screening and diagnosis
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| 650 |
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4 |
|a regularized logistic regression
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| 650 |
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4 |
|a volatile organic compounds (VOC)
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| 700 |
1 |
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|a Lee, Wen-Yee
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Noriega Landa, Elizabeth
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Badmos, Sabur
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Holbrook, Kiana L
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Su, Xiaogang
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t Journal of applied statistics
|d 1991
|g 51(2024), 16 vom: 03., Seite 3468-3485
|w (DE-627)NLM098188178
|x 0266-4763
|7 nnns
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| 773 |
1 |
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|g volume:51
|g year:2024
|g number:16
|g day:03
|g pages:3468-3485
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| 856 |
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|u http://dx.doi.org/10.1080/02664763.2024.2346355
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