Dataset Information

Nomogram Using Prostate Health Index for Predicting Prostate Cancer in the Gray Zone: Prospective, Multicenter Study.

ABSTRACT:

Purpose

To create a nomogram that can predict the probability of prostate cancer using prostate health index (PHI) and clinical parameters of patients. And the optimal cut-off value of PHI for prostate cancer was also assessed.

Materials and methods

A prospective, multi-center study was conducted. PHI was evaluated prior to biopsy in patients requiring prostate biopsy due to high prostate-specific antigen (PSA). Among screened 1,010 patients, 626 patients with clinically suspected prostate cancer with aged 40 to 85 years, and with PSA levels ranging from 2.5 to 10 ng/mL were analyzed.

Results

Among 626 patients, 38.82% (243/626) and 22.52% (141/626) were diagnosed with prostate cancer and clinically significant prostate cancer, respectively. In the PSA 2.5 to 4 ng/mL group, the areas under the curve (AUCs) of the nomograms for overall prostate cancer and clinically significant prostate cancer were 0.796 (0.727-0.866; p<0.001), and 0.697 (0.598-0.795; p=0.001), respectively. In the PSA 4 to 10 ng/mL group, the AUCs of nomograms for overall prostate cancer and clinically significant prostate cancer were 0.812 (0.783-0.842; p<0.001), and 0.839 (0.810-0.869; p<0.001), respectively.

Conclusions

Even though external validations are necessary, a nomogram using PHI might improve the prediction of prostate cancer, reducing the need for prostate biopsies.

SUBMITTER: Chung JH

PROVIDER: S-EPMC10782127 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Publications

Nomogram Using Prostate Health Index for Predicting Prostate Cancer in the Gray Zone: Prospective, Multicenter Study.

Chung Jae Hoon JH Kim Jeong Hyun JH Lee Sang Wook SW Park Hongzoo H Song Geehyun G Song Wan W Kang Minyong M Sung Hyun Hwan HH Jeon Hwang Gyun HG Jeong Byong Chang BC Seo Seong Il SI Lee Hyun Moo HM Jeon Seong Soo SS

The world journal of men's health 20230410 1

<h4>Purpose</h4>To create a nomogram that can predict the probability of prostate cancer using prostate health index (PHI) and clinical parameters of patients. And the optimal cut-off value of PHI for prostate cancer was also assessed.<h4>Materials and methods</h4>A prospective, multi-center study was conducted. PHI was evaluated prior to biopsy in patients requiring prostate biopsy due to high prostate-specific antigen (PSA). Among screened 1,010 patients, 626 patients with clinically suspected ...[more]

PMID: 37118959

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Project description:ObjectiveThe aim of this study was to establish a predictive nomogram for predicting prostate cancer (PCa) in patients with gray-zone prostate-specific antigen (PSA) levels (4-10.0 ng/mL) based on radiomics and other traditional clinical parameters.MethodsIn all, 274 patients with gray-zone PSA levels were included in this retrospective study. They were randomly divided into training and validation sets (n = 191 and 83, respectively). Data on the clinical risk factors related to PCa with gray-zone PSA levels (such as Prostate Imaging Reporting and Data System, version 2.1 [PI-RADS V2.1] category, age, prostate volume, and serum PSA level) were collected for all patients. Lesion volumes of interest (VOI) from T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) imaging were annotated by two radiologists. The radiomics model, clinical model, and combined prediction model, which was presented on a nomogram by incorporating the radiomics signature and clinical and radiological risk factors for PCa, were developed using logistic regression. The area under the receiver operator characteristic (AUC-ROC) and decision, calibration curve were used to compare the three models for the diagnosis of PCa with gray-zone PSA levels.ResultsThe predictive nomogram (AUC: 0.953) incorporating the radiomics score and PI-RADS V2.1 category, age, and the radiomics model (AUC: 0.941) afforded much higher diagnostic efficacy than the clinical model (AUC: 0.866). The addition of the rad score could improve the discriminatory performance of the clinical model. The decision curve analysis indicated that the radiomics or combined model could be more beneficial compared to the clinical model for the prediction of PCa. The nomogram showed good agreement for detecting PCa with gray-zone PSA levels between prediction and histopathologic confirmation.ConclusionThe nomogram, which combined the radiomics score and PI-RADS V2.1 category and age, is an effective and non-invasive method for predicting PCa. Furthermore, as well as good calibration and is clinically useful, which could reduce unnecessary prostate biopsies in patients having PCa with gray-zone PSA levels.

Project description:BackgroundWe aimed to evaluate the usefulness of the Beckman Coulter prostate health index (PHI) and to compare it with total prostate-specific antigen (PSA) levels and related derivatives in predicting the presence and aggressiveness of prostate cancer (PCa) in the Korean population.MethodsA total of 140 men who underwent their first prostate biopsy for suspected PCa were included in this prospective observational study. The diagnostic performance of total PSA, free PSA, %free PSA, [-2] proPSA (p2PSA), %p2PSA, and PHI in detecting and predicting the aggressiveness of PCa was estimated using the receiver operating characteristic curve (ROC) and logistic multivariate regression analyses.ResultsOf 140 patients, PCa was detected in 63 (45%) of participants, and 48 (76.2%) of them had significant cancer with a Gleason score (GS) ≥ 7. In the whole group, the area under the curve (AUC) for ROC analysis of tPSA, free PSA, %fPSA, p2PSA, %p2PSA, and PHI were 0.63, 0.57, 0.69, 0.69, 0.72, and 0.76, respectively, and the AUC was significantly greater in the PHI group than in the tPSA group (p = 0.005). For PCa with GS ≥ 7, the AUCs for tPSA, free PSA, %fPSA, p2PSA, %p2PSA, and PHI were 0.62, 0.58, 0.41, 0.79, 0.86, and 0.87, respectively, and the AUC was significantly greater in the PHI group than in the tPSA group (p < 0.001). In the subgroup with tPSA 4-10 ng/mL, both %p2PSA and PHI were strong independent predictors for PCa (p = 0.007, p = 0.006) and significantly improved the predictive accuracy of a base multivariable model, including age, tPSA, fPSA and %fPSA, using multivariate logistic regression analysis. (p = 0.054, p = 0.048). Additionally, at a cutoff PHI value > 33.4, 22.9% (32/140) of biopsies could be avoided without missing any cases of aggressive cancer.ConclusionsThis study shows that %p2PSA and PHI are superior to total PSA and %fPSA in predicting the presence and aggressiveness (GS ≥ 7) of PCa among Korean men. Using PHI, a significant proportion of unnecessary biopsies can be avoided.

Project description:BackgroundAs the novel serum biomarkers, it has not been clearly clarified that the diagnostic accuracy of prostate health index (PHI) and prostate health index density (PHID) are superior to that of percentage free prostate-specific antigen (%fPSA) in detection of clinically significant prostate cancer (csPCa), especially in the gray zone. Therefore, this study aimed to compare the diagnostic value of PHI, PHID, and %fPSA for csPCa in the patients with prostate-specific antigen (PSA) >4 ng/mL and those with PSA within 4-10 ng/mL.MethodsIn this study, the serum samples and clinicopathological features were prospectively obtained from the patients who underwent prostate biopsy between September 2019 and December 2020. According to the inclusion criteria, the patients with total PSA (tPSA) >4 ng/mL, prostate magnetic resonance imaging or ultrasound clearly suggesting an occupying lesion were enrolled in this study. The patients with Gleason score ≥7 indicated csPCa. The receiver operating characteristic curves and the area under the curve (AUC) values were used to assess the diagnostic performance.ResultsAmong the 296 patients (mean age 67.5 years, median tPSA 7.94 ng/mL) included in this study, there were 54 in the csPCa group (mean age 70.4 years, median tPSA 11.0 ng/mL) and 242 in the non-csPCa group (mean age 66.8 years, median tPSA 7.67 ng/mL). Based on the PSA level, there were 198 patients with PSA within the gray zone, which included 40 patients in the csPCa group and 158 in the non-csPCa group. In all patients, the sensitivity of PHID for detecting csPCa was 96.30%, and the specificity was 33.06% with the cut-off value of 0.51. Moreover, both PHID and PHI did better in the diagnosis of csPCa (AUC: 0.880 and 0.867, respectively) compared with other PSA derivative markers. Similarly, in the patients with PSA level in the gray zone, the diagnostic accuracy of PHID and PHI in predicting csPCa (AUC: 0.788 and 0.777, respectively) were better than other PSA derivative markers.ConclusionsPHID presented the better diagnostic accuracy in predicting csPCa in patients with PSA in the gray zone than other PSA derivative markers, which could be a promising biomarker for making the biopsy strategy.

Project description:ObjectiveProstate cancer (PCa) is the second most common male malignancy globally. Prostate-specific antigen (PSA) is an important biomarker for PCa diagnosis. However, it is not accurate in the diagnostic gray zone of 4-10 ng/ml of PSA. In the current study, the performance of serum metabolomics profiling in discriminating PCa patients from benign prostatic hyperplasia (BPH) individuals with a PSA concentration in the range of 4-10 ng/ml was explored.MethodsA total of 220 individuals, including patients diagnosed with PCa and BPH within PSA levels in the range of 4-10 ng/ml and healthy controls, were enrolled in the study. Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS)-based non-targeted metabolomics method was utilized to characterize serum metabolic profiles of participants. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) methods were used for multivariate analysis. Receiver operating characteristic (ROC) curve analysis was performed to explore the diagnostic value of candidate metabolites in differentiating PCa from BPH. Correlation analysis was conducted to explore the relationship between serum metabolites and common clinically used fasting lipid profiles.ResultsSeveral differential metabolites were identified. The top enriched pathways in PCa subjects such as glycerophospholipid and glycerolipid metabolisms were associated with lipid metabolism. Lipids and lipid-like compounds were the predominant metabolites within the top 50 differential metabolites selected using fold-change threshold >1.5 or <2/3, variable importance in projection (VIP) > 1, and Student's t-test threshold p < 0.05. Eighteen lipid or lipid-related metabolites were selected including 4-oxoretinol, anandamide, palmitic acid, glycerol 1-hexadecanoate, dl-dihydrosphingosine, 2-methoxy-6Z-hexadecenoic acid, 3-oxo-nonadecanoic acid, 2-hydroxy-nonadecanoic acid, N-palmitoyl glycine, 2-palmitoylglycerol, hexadecenal, d-erythro-sphingosine C-15, N-methyl arachidonoyl amine, 9-octadecenal, hexadecyl acetyl glycerol, 1-(9Z-pentadecenoyl)-2-eicosanoyl-glycero-3-phosphate, 3Z,6Z,9Z-octadecatriene, and glycidyl stearate. Selected metabolites effectively discriminated PCa from BPH when PSA levels were in the range of 4-10 ng/ml (area under the curve (AUC) > 0.80). Notably, the 18 identified metabolites were negatively corrected with total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and Apo-B levels in PCa patients; and some were negatively correlated with high-density lipoprotein cholesterol (HDL-C) and Apo-A levels. However, the metabolites were not correlated with triglycerides (TG).ConclusionThe findings of the present study indicate that metabolic reprogramming, mainly lipid metabolism, is a key signature of PCa. The 18 lipid or lipid-associated metabolites identified in this study are potential diagnostic markers for differential diagnosis of PCa patients and BPH individuals within a PSA level in the gray zone of 4-10 ng/ml.

Dataset Information

Nomogram Using Prostate Health Index for Predicting Prostate Cancer in the Gray Zone: Prospective, Multicenter Study.

Purpose

Materials and methods

Results

Conclusions

Publications

Nomogram Using Prostate Health Index for Predicting Prostate Cancer in the Gray Zone: Prospective, Multicenter Study.

Similar Datasets

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