Nsugbe, Ejay and Ser, Hooi Leng * and Ong, Huey Fang and Long, Chiau Ming * and Goh, Khang Wen and Goh, Bey Hing * and Lee, Wai Leng (2022) On an Affordable Approach towards the Diagnosis and Care for Prostate Cancer Patients Using Urine, FTIR and Prediction Machines. Diagnostics, 12 (9). ISSN 2075-4418
|
Text
Ser Hooi Leng_On an affordable approach towards the diagnosis and care for prostate cancer patients.pdf - Published Version Available under License Creative Commons Attribution. Download (1MB) | Preview |
Abstract
Prostate cancer is a widespread form of cancer that affects patients globally and is challenging to diagnose, especially in its early stages. The common means of diagnosing cancer involve mostly invasive methods, such as the use of patient’s blood as well as digital biopsies, which are relatively expensive and require a considerable amount of expertise. Studies have shown that various cancer biomarkers can be present in urine samples from patients who have prostate cancers; this paper aimed to leverage this information and investigate this further by using urine samples from a group of patients alongside FTIR analysis for the prediction of prostate cancer. This investigation was carried out using three sets of data where all spectra were preprocessed with the linear series decomposition learner (LSDL) and post-processed using signal processing methods alongside a contrast across nine machine-learning models, the results of which showcased that the proposed modeling approach carries potential to be used for clinical prediction of prostate cancer. This would allow for a much more affordable and high-throughput means for active prediction and associated care for patients with prostate cancer. Further investigations on the prediction of cancer stage (i.e., early or late stage) were carried out, where high prediction accuracy was obtained across the various metrics that were investigated, further showing the promise and capability of urine sample analysis alongside the proposed and presented modeling approaches.
Item Type: | Article |
---|---|
Uncontrolled Keywords: | prostate cancer; FTIR; extracellular vesicles; LSDL; signal processing; oncology; machine learning; public health |
Subjects: | Q Science > Q Science (General) R Medicine > RC Internal medicine |
Divisions: | Others > Non Sunway Academics Sunway University > School of Medical and Life Sciences [formerly School of Healthcare and Medical Sciences until 2020] > Department of Medical Sciences Sunway University > School of Medical and Life Sciences [formerly School of Healthcare and Medical Sciences until 2020] > Dept. Biological Sciences |
Depositing User: | Ms Yong Yee Chan |
Related URLs: | |
Date Deposited: | 13 Aug 2024 00:29 |
Last Modified: | 13 Aug 2024 00:29 |
URI: | http://eprints.sunway.edu.my/id/eprint/3095 |
Actions (login required)
![]() |
View Item |