Raman Spectroscopy Reveals Abnormal Changes in the Urine Composition of Prostate Cancer: An Application of an Intelligent Diagnostic Model with a Deep Learning Algorithm

Abstract

Early diagnosis of prostate cancer (PCa) is always a great challenge in clinical practice, especially in distinguishing benign prostatic hyperplasia (BPH) from early cancer, due to the high similarity in pathology from the prostate‐specific antigen (PSA) test and radiological detection. The conventional diagnostic methods are often less efficient in specificity and accuracy, leading to quite a few unnecessary biopsies. This work establishes a noninvasive diagnostic method for PCa by investigating urine samples using Raman spectroscopy and convolutional neural network (CNN) algorithm. The results of urine Raman spectra show the intensities of characteristic peaks for lipids, nucleic acids, and some amino acids are distinguishable between PCa and BPH, suggesting an abnormal metabolism caused by PCa, which can be detected by Raman spectroscopy. These data are then used to train an intelligent diagnostic model with CNN algorithm. The cross‐validation results show the mean diagnostic accuracy, sensitivity, and specificity for PCa are 74.95%, 77.32%, and 72.46%, respectively. This noninvasive diagnostic method is a promising method for the early diagnosis of PCa, and the idea of using urine Raman spectroscopy with deep learning techniques for diagnosing PCa provides a reference for the application of artificial intelligence in the field of clinical medicine research.

Current Trends of Raman Spectroscopy in Clinic Settings: Opportunities and Challenges

“…[47] Chen et al integrated Raman spectroscopy with CNNs to introduce a novel approach for detecting PCa through urine analysis. [48] PCa and prostatic hyperplasia (BPH) are discernible through variations in the intensity of the characteristic peaks related to lipids, NAs, and selected amino acids within the urine Raman spectra. This differential pattern indicates the presence of anomalous metabolic activity associated with PCa, which can be captured by Raman spectroscopy.…”

“…In Figure 6b, (i) presents the bright-field image and the area indicated by the arrow is rich in lipids and drug compounds; (ii) displays the reconstructed Raman image of the sample [48] Copyright 2021, Wiley-VCH. This study directly integrates a data-processing engine into the acquisition flow to address the current drawbacks of conventional Raman systems.…”

Application and Progress of Raman Spectroscopy in Male Reproductive System

“…Owing to these discoveries so far, there is no doubt that detecting more accurately spectral peak differences, implementing more accurate algorithms and building more accurate diagnostic models have become a new challenge. With the help of Raman spectroscopy and convolutional neural network (CNN) algorithm to study urine samples, Chen et al (2021) established a non-invasive diagnostic method for prostate carcinoma. Obviously, the results of urine Raman spectroscopy showed that the intensity of characteristic peaks of lipids, nucleic acids and some amino acids of prostate carcinoma and BPH could be distinguished, and then these data were used to train an intelligent diagnostic model of CNN algorithm to deep study, and the idea of using urine Raman spectroscopy combined with deep learning technology to diagnose prostate carcinoma provides a reference for the application of artificial intelligence in the field of clinical medical research.…”

Engineering the MoS2/MXene Heterostructure for Precise and Noninvasive Diagnosis of Prostate Cancer with Clinical Specimens

“…[5][6][7] Especially, the high false positives (≈75%) in the PSA gray zone of 4-10 ng mL −1 lead to many unnecessary biopsies, [8,9] which aggravate the medical/physiological burden of patients and affect their life quality. [8,10] Meanwhile, imaging techniques such as ultrasound and magnetic resonance (MR) suffer from low accuracy, high costs, radiologist dependency, and time-consuming testing, rendering them unsuitable for the practical, precise screening of PCa in the gray zone. [11,12] In this context, a precise and noninvasive platform is desirable for PCa diagnosis.…”

“…[3,4] As the most widely used biomarker for PCa screening, the prostate-specific antigen (PSA) provides limited sensitivity and specificity, which may lead to overdiagnosis and subsequent overtreatment. [5][6][7] Especially, the high false positives (≈75%) in the PSA gray zone of 4-10 ng mL −1 lead to many unnecessary biopsies, [8,9] which aggravate the medical/physiological burden of patients and affect their life quality. [8,10] Meanwhile, imaging techniques such as ultrasound and magnetic resonance (MR) suffer from low accuracy, high costs, radiologist dependency, and time-consuming testing, rendering them unsuitable for the practical, precise screening of PCa in the gray zone.…”