Development and Validation of Newly Biopsy-Free Nomograms for Predicting Clinically Significant Prostate Cancer in Men with PI-RADS ≥4 Lesions

Development and Validation of Newly Biopsy-Free Nomograms for Predicting Clinically Significant Prostate Cancer in Men with PI-RADS ≥4 Lesions | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Development and Validation of Newly Biopsy-Free Nomograms for Predicting Clinically Significant Prostate Cancer in Men with PI-RADS ≥4 Lesions Junxin Wang, Mingzhe Chen, Yong Xu, Shanqi Guo, Xingkang Jiang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4695012/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract To develop and validate biopsy-free nomograms to more accurately predict clinically significant prostate cancer (csPCa) in biopsy-naïve men with Prostate Imaging Reporting and Data System (PI-RADS) ≥ 4 lesions. A cohort of 931 patients with PI-RADS ≥ 4 lesions, undergoing prostate biopsies or radical prostatectomy from January 2020 to August 2023, was analyzed. Various clinical variables, including age, prostate-specific antigen (PSA) levels, prostate volume (PV), PSA density (PSAD), prostate health index (PHI), and maximum standardized uptake values (SUVmax) from PSMA PET-CT imaging, were assessed for predicting csPCa. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), calibration plots, and decision-curve analyses, with internal validation. The foundational model (nomogram 1) encompassed the entire cohort, accurately predicting csPCa by incorporating variables such as age, PSAD, PV, PSA ratio variations, suspicious lesion location, and history of acute urinary retention (AUR). The AUC for csPCa prediction achieved by the foundational model was 0.918, with internal validation confirming reliability (AUC: 0.908). Advanced models (nomogram 2 and 3), incorporating PHI and PHI + PSMA SUVmax, achieved AUCs of 0.908 and 0.955 in the training set and 0.847 and 0.949 in the validation set, respectively. Decision analysis indicated enhanced biopsy outcome predictions with the advanced models. Nomogram 3 could potentially reduce biopsies by 92.41%, while missing only 1.53% of csPCa cases. In conclusion, the newly biopsy-free approaches for patients with PI-RADS ≥ 4 lesions represent a significant advancement in csPCa diagnosis in this high-risk population. Health sciences/Oncology/Cancer/Urological cancer Health sciences/Urology/Prostate Magnetic Resonance Imaging Nomograms Prostate Neoplasms Positron Emission Tomography Computed Tomography (18)F-PSMA-11 Figures Figure 1 Figure 2 Figure 3 Introduction Prostate cancer (PCa) currently ranks as the most prevalent cancer and the second leading cause of cancer-related deaths among men, based on 2024 data [ 1 ]. Despite the widespread acceptance of prostate biopsy as the gold standard for diagnosis, it presents risks of physiological complications and psychological burdens, including urinary retention, hematuria, and sepsis, which can heighten preoperative anxiety [ 2 – 3 ]. Multiparametric magnetic resonance imaging (mpMRI) paired with the Prostate Imaging Reporting and Data System (PI-RADS) version 2.1 grading system has garnered significant attention in assessing the likelihood of clinically significant cancer. Elevated PI-RADS grades are linked to an increased likelihood of clinically significant prostate cancer (csPCa), sparking ongoing debates on the necessity of prostate biopsy, particularly for PI-RADS 4 and 5 grades. While some advocate for bypassing biopsy entirely, others stress the importance of biopsy while minimizing unnecessary procedures [ 4 – 6 ]. While mpMRI accurately identifies csPCa, its low positive predictive value restricts its efficacy. Addressing this limitation, recent studies have introduced multivariable risk-based nomograms drawing on data from the European Randomized Study of Screening for PCa (ERSPC) and integrating mpMRI findings. This has resulted in enhanced cancer detection rates and a decrease in unnecessary biopsies [ 7 – 9 ]. Moreover, the incorporation of precision clinical parameters, such as the Prostate Health Index (PHI) - comprising total PSA (tPSA), free PSA (fPSA), and the PSA isoform [-2]proPSA (p2PSA) - into a comprehensive formula has transformed csPCa screening practices. Meta-analyses have demonstrated that PHI exhibits superior sensitivity and specificity compared to traditional PSA markers for detecting csPCa, reaffirming its significance in screening guidelines [ 9 – 10 ]. Furthermore, molecular imaging techniques like prostate-specific membrane antigen (PSMA) positron emission tomography (PET)/computed tomography (CT) have been proposed for enhanced diagnostic accuracy in primary staging for PCa patients. Findings from the prospective PRIMARY trial have shown that combining PSMA PET with mpMRI surpasses the performance of mpMRI alone in detecting csPCa, indicating that men with suspicious PSMA-PET and mpMRI findings may potentially forego biopsy and proceed directly to definitive treatment [ 11 – 12 ]. However, to date, no studies have been conducted in highly suspicious patients incorporating both PHI and PSMA PET/CT images, along with multivariable clinical parameters, to predict the presence of csPCa. Our study aims to investigate the potential development of biopsy-free diagnostic nomograms for csPCa in selected men with a high suspicion (PI-RADS ≥ 4) of significant malignancy in both PHI and PSMA PET/CT. The increasing interest in innovative imaging modalities for csPCa detection has prompted the creation and validation of biopsy-free nomograms based on a multivariate model incorporating clinical variables, PHI, and PSMA-PET/CT to estimate individual probabilities of aggressive PCa. Methods Study population Data were retrospectively collected from two prospectively clinical studies (ChiCTR2200066455 and ChiCTR2000038696) conducted at the Second Hospital of Tianjin Medical University from January 2020 to August 2023. Biopsy-naïve patients suspected of having PCa due to elevated PSA and/or an abnormal DRE, along with highly suspicious lesions identified on the mpMRI (PI-RADS ≥ 4) were included. Exclusion criteria comprised: 1) Patients with urinary tract infection or prostatitis, 2) Patients who had undergone prostate surgery before biopsy, 3) Patients with incomplete clinical and pathological data, 4) Patients with poor MRI quality or low image resolution, and 5) Patients who had undergone previous prostate biopsies. The study was conducted in compliance with the guiding principles of the Declaration of Helsinki, and approved by the ethics committee of the Second Hospital of Tianjin Medical University (No.KY2020K130), and informed consent was obtained from each patient. Collection of clinical information The collected data for the whole population, and after stratification of the cohort according to the presence of csPCa includes the patients’ age, height, weight, history of hypertension, history of diabetes, history of acute urinary retention (AUR) (within 1 month before biopsy), prostate volume (PV), last tPSA before biopsy, initial tPSA (within 1 month before biopsy), free PSA (fPSA), ratio of free PSA to total PSA (f/tPSA), PSA density (PSAD), digital rectal examination (DRE) findings, lesion localization on mpMRI, PI-RADS score, PHI, maximum standardized uptake value (SUVmax) on PSMA PET/CT, and pathological results. Calculation formulas for relevant clinical indicators are as follows: Body Mass Index (BMI): weight (kg) / height^2 (m^2), PSA differences to ratio: (last PSA before biopsy - initial PSA) / initial PSA * 100%. The serum concentration of total PSA, free PSA, and p2PSA was measured on the Access 2 analyzer (Beckman Coulter, Bream, CA, USA). The percentage of p2PSA (%p2PSA) was calculated using the formula [(p2PSA pg/ml)/(fPSA ug/L × 1000)] × 100. PHI was calculated using the formula [(p2PSA pg/ml)/(fPSA ug/L)] × total PSA (ug/L). PV was calculated using the formula PV = ([maximum anteroposterior diameter] × [maximum transverse diameter] × [maximum longitudinal diameter] × 0.52), as assessed through MRI imaging. PSAD was calculated by dividing total PSA by PV. mpMRI protocol Following the recommendations of the European Society of Urogenital Radiology (ESUR), a 3.0-T MRI protocol was conducted on all participants without using an endorectal coil. The MRI scans were evaluated by a specialized genitourinary radiologist using the PI-RADS v2.1 protocols, which included multiparametric sequences such as T2-weighted fast spin echo imaging (T2WI), diffusion-weighted spin echo planar imaging (DWI), and calculation of apparent diffusion coefficient (ADC) maps using linear least squares regression. Two experienced urological radiologists, with a minimum of five years of experience in prostate MRI and blinded to the patients’ clinical information, independently reviewed and assessed all MRI images based on the PI-RADS v2.1 criteria. The PI-RADS scoring system designates PI-RADS 4 and 5 as indicating a high likelihood of csPCa. PSMA PET-CT All PSMA PET scans were conducted according to our local protocol and interpreted in a clinical setting. The recommended dosage range for 68Ga-PSMA-11 is typically 1.5-3.0 MBq/kg. However, the specific dosage for each patient is determined by the nuclear medicine physician based on their individual circumstances. After injection, there is a waiting period of usually 60 minutes to allow the tracer to distribute throughout the body and bind to PSMA. The patient lies on the scanning table, which gradually moves through the PET/CT machine. The entire process generally takes around 20–30 minutes. All PSMA PET scans are presented and discussed in a multidisciplinary meeting attended by at least two highly experienced nuclear medicine physicians. During the analysis, several factors are considered, including PSMA uptake. Areas of high PSMA uptake may indicate the presence of prostate cancer. The uptake intensity and pattern, as well as the intensity of uptake, can provide insights into the nature of the cancer. Uptake in other areas, such as lymph nodes or other organs, may suggest the spread of cancer. Standardized uptake values (SUV) are typically used to quantify PSMA uptake. Histopathological analysis Enrolled patients were underwent either ultrasound-guided transperineal prostate biopsy (combined systematic and targeted biopsy) or radical prostatectomy. Tissue samples were fixed in formalin and evaluated by two senior pathologists specializing in prostate evaluation, adhering to 2019 International Society of Urological Pathology standards. csPCa was defined as those with a Gleason score of ≥ 3 + 4, while non-csPCa was defined by the absence of csPCa and included cases of benign prostatic hyperplasia, prostatitis, prostatic hyperplasia, and normal prostate tissue with calcification. Statistical analyses The statistical analyses were performed using Statistical Package for Social Science version 22.0 (SPSS 22.0, IBM Corp) and R version 4.3.2 ( www.r-project.org ). Descriptive statistics were used to summarize continuous variables, which were compared between the diagnostic and validation cohorts using the Wilcoxon rank-sum and Kruskal-Wallis tests. Categorical variables were presented as frequencies (percentages), and group comparisons were conducted using the chi-square test or Fisher’s exact test. Multiple imputation was utilized for variables with missing or outlier values, with the normality of continuous variables assessed using the Shapiro-Wilk test. Disaggregated data were presented as numbers (n) and percentages (%). Normally distributed continuous variables were expressed as mean ± standard deviation (SD), while non-normally distributed ones were described as the median (interquartile range (IQR)). The cut-off value of the nomogram was determined using the maximum Youden index, with a significance level set at p < 0.05. The entire cohort was randomly divided into a training cohort and a validation cohort at a 7:3 ratio. Univariate logistic regression analysis was initially conducted in the modeling dataset, followed by backward multiple logistic regression analysis after excluding variables exhibiting multicollinearity. Variables with p < 0.05 were retained for model establishment. The prediction model was developed using a nomogram and internally validated to assess its predictive performance. The area under the receiver operating characteristic (ROC) curve (AUC) was calculated to evaluate discrimination ability, with model calibration assessed using the Hosmer-Lemeshow test and calibration curve. Decision curve analysis (DCA) evaluated net benefit and clinical utility. Results Clinical characteristics A total of 931 patients met the inclusion criteria and were included in the overall population (Fig. 1 ). The baseline characteristics of the patients are presented in Table 1 . Among them, 779 (83.7%) patients received a diagnosis of csPCa and 152 (16.3%) patients were diagnosed with non-csPCa. Patients with csPCa exhibited higher levels of age, PSA, fPSA, PSAD, percentage differences in PSA ratio, PI-RADS score, peripheral zone location, acute urinary retention, diabetes, DRE findings, and lower PV and %f/TPSA compared to those with non-csPCa. In the cohort of 316 and 198 patients undergoing PHI and PSMA PET/CT imaging, the PHI and PSMA SUVmax were notably elevated in individuals with csPCa compared to those with non-csPCa ( Table S1 -2 ). Among these patients, a random assignment of 7:3 was made to the training and validation cohorts, and a detailed comparison of demographic data, comorbidities, and characteristics between the three cohorts is presented in Table S3-6 . Table 1 Patients’ Characteristics of Enrolled Population (n = 931). Whole cohort (n = 931) non-csPCa (n = 152) csPCa (n = 779) p value Age at biopsy (yr), median (IQR) 71 (66–76) 69 (65.00-73.75) 71 (66–76) < 0.001 BMI (kg/m2),median (IQR) 24.45 (22.49–26.45) 24.25 (22.49–26.83) 24.45 (22.49–26.42) 0.865 PSA (ng/ml), median (IQR) 31.90 (12.30-78.58) 10.25 (6.11–20.48) 42.33 (16.53–87.58) < 0.001 PSA, n (%) 50 350 (37.60) 6 (3.95) 344 (44.16) fPSA (ng/ml), median (IQR) 3.49 (1.51–8.82) 1.71 (0.86–2.81) 4.49 (1.75-10.00) < 0.001 % f/tPSA, median (IQR) 10.95 (8.23–15.19) 14.03 (9.26–20.81) 10.61 (8.13–14.16) < 0.001 Testosterone (ng/dl), median (IQR) 377.62 (299.80-449.57) 385.74 (330.90-452.86) 374.76 (297.12-449.25) 0.244 PV (ml), median (IQR) 55.09 (37.61–75.79) 65.10 (49.22–96.78) 53.20 (36.36–72.80) < 0.001 PV, n (%) 50 534 (57.36) 112 (73.68) 422 (54.17) PSAD (ng/ml2), median (IQR) 0.62 (0.25–1.28) 0.17 (0.10–0.29) 0.78 (0.37–1.45) < 0.001 PSAD, n (%) 1.0 309 (33.19) 1 (0.65) 308 (39.54) %PSA differences to ratio, median (IQR) -0.33 (-16.32-10.15) -21.93 (-40.94–2.70) 1.84 (-9.84-12.12) < 0.001 %PSA differences to ratio, n (%) -20 747 (80.23) 74 (48.68) 673 (86.40) PI-RADS score, n (%) < 0.001 4 374 (40.17) 123 (80.92) 251 (32.22) 5 557 (59.83) 29 (19.08) 528 (67.78) Localization of suspicious lesion, n (%) < 0.001 PZ 466 (50.05) 81 (53.29) 385 (49.42) TZ 124 (13.32) 46 (30.26) 78 (10.01) Others 61 (6.55) 23 (15.13) 38 (4.88) PZ + TZ 280 (30.08) 2 (1.32) 278 (35.69) AUR, n (%) 144 (15.47) 45 (29.61) 99 (12.71) < 0.001 Diabetes, n (%) 197 (21.16) 22 (14.47) 175 (22.46) 0.027 Hypertension, n (%) 412 (44.25) 70 (46.05) 342 (43.90) 0.625 DRE, n (%) 521 (55.96) 66 (43.42) 455 (58.41) 0.001 BMI = body mass index; IQR = inter quartile range; PI-RADS = Prostate Imaging Reporting and Data System; PSA = prostate-specific antigen; PSAD = prostate-specific antigen density; PZ = peripheral zone; TZ = transition zone; Others = lesions beyond the peripheral and transition zones; DRE = Digital Rectal Examination; The p values were calculated using the chi-square (categorical variables) and Mann-Whitney (continuous variables) tests. p-values < 0.05 in bold. Prediction Model Development After multivariate analysis, factors including age, PV, PSAD, and %PSA differences to ratio, peripheral zone location, and AUC were identified as strong associated with csPCa findings within the whole cohort (Table 2 , Table S7 ). Subsequently, the foundational nomogram (model 1) comprised of age, PV, PSAD, %PSA differences to ratio, localization of suspicious lesion, and AUC, demonstrated an AUC of 0.918 (95% confidence interval [CI] 0.894–0.943) following internal validation. Additionally, when integrating PHI into the baseline model, age and %PSA differences to ratio were excluded from nomogram 2, resulting in an AUC of 0.908 (95% CI 0.863–0.953). The incorporation of PHI and PSMA SUVmax into the foundational nomograms (model 3), which included PHI, PSMA SUVmax, localization of suspicious lesion, and AUR, led to a substantial enhancement in the AUC for predicting csPCa, elevating it to 0.955 (95% CI 0.923–0.987) (Fig. 2 – 3 ). The aforementioned three nomograms were validated using their validation cohorts and are readily accessible online ( https://www.evidencio.com/models/show/10355 ). Table 2 Multivariable Logistic Regression Analysis for Predicting Clinically Significant Prostate Cancer (csPCa) Using Three Models. Model 1 Model 2 Model 3 OR (95%CI) p value OR (95%CI) p value OR (95%CI) p value Age 1.07 (1.02–1.11) 0.003 - - - - PV 0.98 (0.97-1.00) 0.014 - - ≤ 50 reference group > 50 0.38 (0.21–0.70) 0.002 PSAD 3.65 (0.48–28.02) 0.213 - - ≤ 0.2 reference group 0.2–0.5 2.67 (1.43–4.99) 0.002 0.5-1.0 5.73 (2.64–12.42) 1.0 0.977 %PSA differences to ratio - - - - ≤-50 reference group -50–20 4.32 (1.35–13.86) 0.014 >-20 7.27 (2.46–21.44) < 0.001 PHI, median - - 1.02 (1.01–1.03) 0.001 1.01 (1.00-1.02) 0.047 PSMA PET/CT SUV max - - - - 1.25 (1.06–1.47) 0.009 Localization of suspicious lesion PZ reference group reference group reference group TZ 0.28 (0.15–0.54) < 0.001 0.17 (0.05–0.52) 0.002 0.08 (0.02–0.46) 0.004 Others 0.31 (0.12–0.82) 0.018 0.24 (0.05–1.09) 0.065 1.28 (0.10-16.93) 0.851 PZ + TZ 18.50 (2.35–145.4) 0.006 2.50 (0.28–22.07) 0.411 6.26 (0.53–74.45) 0.146 AUR none reference group reference group reference group yes 0.25 (0.11–0.57) 0.001 0.21 (0.02–0.71) 0.012 0.10 (0.02–0.54) 0.007 csPCa = clinically significant prostate cancer; OR = odds ratio; CI = confidence interval; AIC = Akaike Information Criterion; PHI = Prostate Health Index; PET = positron emission tomography; PI-RADS = Prostate Imaging Reporting and Data System; PSAD = prostate-specific antigen density; PSMA = prostate-specific membrane antigen; PZ = peripheral zone; TZ = transition zone; Others = lesions beyond the peripheral and transition zones. a AIC min = 344.82.b AIC min = 142.28.c AIC min = 71.13. Decision Curve Analysis The calibration plot demonstrates superior fit of the advanced model compared with the baseline model in both the training cohort and validation cohort ( Figure S1 ). To validate the efficacy of the nomogram, a Decision Curve Analysis (DCA) was conducted, revealing that advanced models enhanced clinical risk prediction for csPCa with a threshold probability of 80%, with models 2 and 3 graphically superior to model 1 (38.78 vs. 51.86 and 53.57)( Figure S2 ). For the optimal cutoff values of PSAD, PHI, SUVmax, and nomograms in predicting csPCa in these highly suspicious patients with PI-RADS 4 and 5 lesions, a comprehensive analysis of various thresholds was conducted, and the findings are summarized in Table 3 . Using a cutoff of 38.9%, the proportion of patients eligible for biopsy-free was 92.41%, at the cost of missing 1.53% patients with csPCa. Table 3 Predictive Performance of Different Cut-off Values of Prostate-specific Antigen Density (PSAD), Prostate Health Index (PHI), Maximum Standardized Uptake Value (SUVmax), and Three Nomograms. Decision to biopsy sensitivity,% Specificity,% PPV,% NPV,% %Avoided biopsy %Missed CsPCa n = 931 NP a ≥0.757 87.16 79.61 95.63 54.82 85.96 10.71 NP b ≥0.458 98.10 51.30 91.17 84.05 90.46 1.59 NP c ≥0.991 46.10 100.00 100.00 26.58 54.90 45.10 n = 316 NP a ≥0.852 77.20 83.90 95.16 47.32 78.51 18.33 NP b ≥0.389 96.50 53.20 89.42 78.77 88.00 2.81 NP c ≥0.987 37.00 100.00 100.00 27.93 49.36 50.00 PHI a ≥94.570 68.90 88.90 96.22 41.10 72.82 25.00 PHI b ≥45.59 95.50 50.00 88.71 74.85 86.89 3.30 PHI c ≥203.160 32.40 100.00 100.00 23.53 45.66 54.34 PSAD a ≥9.480 78.40 61.10 89.20 40.84 75.01 17.36 PSAD b ≥4.545 89.20 44.40 86.79 50.09 80.41 8.68 PSAD c ≥1.640 14.90 100.00 100.00 22.29 31.60 68.40 n = 198 NP a ≥0.852 86.31 93.33 98.63 54.88 87.36 11.62 NP b ≥0.389 98.20 60.00 93.22 85.62 92.41 1.53 NP c ≥0.954 72.60 100.00 100.00 39.46 76.75 23.25 PHI a ≥94.570 79.17 83.33 96.37 41.70 79.82 17.65 PHI b ≥45.59 97.60 43.30 90.60 76.31 89.37 2.04 PHI c ≥200.715 46.50 100.00 100.00 24.99 54.52 45.48 PSMA SUV max a ≥9.480 78.57 76.67 94.97 39.03 78.31 18.16 PSMA SUV max b ≥4.545 97.00 36.70 89.56 68.60 87.86 2.55 PSMA SUV max c ≥16.520 45.80 100.00 100.00 24.78 54.01 45.99 csPCa = clinically significant prostate cancer; PI-RADS = Prostate Imaging Reporting and Data System; NP = nomogram predictive; NPV = negative predictive value; PHI = Prostate Health Index; PPV = positive predictive value; PSAD = prostate-specific antigen density; PET = positron emission tomography; PSMA = prostate-specific membrane antigen. a the cut-off value at the maximum Youden index. b the cut-off value at maximum accuracy. C the cut-off value at maximum specificity. Discussion In 2020, a comprehensive cross-sectional study unveiled that PI-RADS scores of 4 and 5 are robust indicators of a high suspicion of csPCa, with corresponding positive predictive values of 39% and 72% [ 13 ]. By 2023, Xiang et al. developed a predictive model based on patient-related characteristics for the detection of PCa in individuals with PI-RADS 4–5 lesions. Their study, which involved 833 patients, demonstrated that 83.0% of prostate cancer cases were identified in those with PI-RADS scores of 4 or higher, with 74.5% in PI-RADS 4 lesions and 91.8% in PI-RADS 5 lesions. Notably, independent characteristics within the PI-RADS 4 subgroup, such as lesion location, age, fPSA/total PSA ratio, and PSAD, were identified and used to establish the predictive model, achieving an area under the curve (AUC) of 0.748 (95% CI 0.694–0.803). Additionally, the prediction model for PI-RADS 5 was developed based on PSA and PSAD, resulting in an AUC of 0.893 (95% CI 0.844–0.941) [ 14 ]. In the present study involving 981 patients, the diagnostic rate of csPCa was 83.7%, with 67.1% identified in PI-RADS 4 lesions and 94.8% in PI-RADS 5 lesions. Our study established and validated a fundamental diagnostic nomogram that obviates the need for biopsy in predicting csPCa, achieving an AUC of 0.918 (95% CI 0.894–0.943). This nomogram incorporates preclinical parameters such as age, PV, PSAD, suspicious lesion location, and %PSA differences to ratio, and AUR. While our results reinforce the connection between higher PI-RADS scores and an increased likelihood of csPCa, a small subset of individuals still received negative biopsy results, highlighting the hesitance to avoid prostate biopsies. Factors contributing to “false-positive MRI diagnoses” include PI-RADS overestimation, ambiguous images leading to inflated PI-RADS scores, diseases posing challenges in differentiation, and missed lesions during initial biopsies, with the former two factors being predominant [ 15 – 17 ]. Given these considerations, integrating additional clinical parameters and molecular imaging may be essential to enhance multiparametric MRI interpretations for accurate csPCa prediction and potentially reduce the necessity for unnecessary prostate biopsies in individuals with highly suspicious PI-RADS ≥ 4 lesions. Several studies have indicated that integrating PHI into multivariate models comprising clinical and demographic variables enhances diagnostic accuracy in predicting csPCa. For instance, Zhou et al. [ 18 ] showed that the combined assessment of PHI, PI-RADS scores, and other clinical factors (such as age, PI-RADS, and Log PSA Density) yielded AUC values of 0.902 for PCa and 0.896 for csPCa, respectively. Similarly, Mo et al. [ 19 ] presented a multivariable model incorporating PI-RADS, fPSA, PHI, we evaluated the diagnostic precision of PHI and apparent diffusion coefficient (ADC) values based on PI-RADS v2.1 for guiding prostate biopsy in patients with PSA levels ranging from 4 to 20 ng/mL. The predictive model we devised, comprising age, PHI, PV, and ADC values as independent predictors, demonstrated an AUC of 0.856 for predicting csPCa [ 20 ]. In the current work, we also validated that PHI improves the detection rate of csPCa in patients with PI-RADS ≥ 4 lesions, in line with previous findings. Incorporating PHI into the basic variables, Model 2 demonstrated a comparable AUC to Model 1 in both the training and validation cohorts (AUC: 0.918 vs. 0.908, and 0.908 vs. 0.847, respectively). The observed similarity in AUC between Model 1 and Model 2 may be attributed to the relatively small sample size. With a smaller sample size, the statistical power to detect differences between models might be diminished. Additionally, we utilized the entire cohort of 316 patients for training and consistently obtained similar results during nomogram validation. Consequently, even if Model 2 exhibits improvement over Model 1, it might not achieve statistical significance due to the limitations imposed by the sample size. Therefore, a larger sample size might be necessary to more accurately assess the performance differences between these models. The advanced molecular imaging technique, PSMA PET/CT, offers superior diagnostic precision in identifying various conditions of PCa, including active surveillance, biochemical recurrence, lymph node metastasis, as well as metastatic castration-resistant disease, potentially influencing treatment decisions [ 21 ]. Therefore, incorporating PSMA PET/CT into screening programs as an adjunctive tool can assist in decreasing the overdiagnosis of insignificant cancer, while also enhancing the diagnostic accuracy for csPCa [ 22 – 24 ]. Despite cost considerations, several authors have recommended the use of PSMA PET/CT due to potential cost savings and improved quality of life resulting from avoid unnecessary biopsies. By employing preclinical risk stratification with PSMA-PET/CT imaging, some individuals have successfully undergone radical prostatectomy without prior biopsy, presenting promising outcomes and potentially eliminating the need for biopsy in patients with highly suspicious lesions rated PI-RADS ≥ 4 [ 5 , 25 – 28 ]. However, as the saying goes, “all truth passes through three stages: first, it is ridiculed; second, it is violently opposed; third, it is accepted as self-evident.” Considering this context, our objective was to investigate the feasibility and outcomes of a biopsy-free approach based on preclinical risk stratification, integrating PHI and PSMA SUVmax in patients with highly suspicious lesions rated PI-RADS ≥ 4. By incorporating both PHI and PSMA SUVmax, the model 3 exhibited superior discriminatory power compared to the foundational nomogram (model 1) and model 2 (AUC: from 0.918 and 0.908 to 0.955). Additionally, model 3 exhibited outstanding calibration in predicting the risk of csPCa, suggesting that incorporation of this advanced model into clinical practice could potentially eliminate the need for prostate biopsy. While our study possesses notable strengths, it is imperative to acknowledge its limitations. Firstly, the retrospective nature of our analysis may have introduced patient selection biases, potentially impacting the generalizability of our findings. We recognize this limitation and comprehend the potential influence of selection bias on our results. Secondly, our study concentrated on patients with PI-RADS ≥ 4 lesions from a single institution, where mpMRIs and PSMA PET/CT were predominantly interpreted by experienced radiologists. This scenario may restrict the applicability of our findings to institutions with less experienced radiologists, thus affecting their generalizability. Additionally, variations in PSMA tracers can also influence the values of SUVmax. It is essential to acknowledge this potential limitation and underscore the importance of conducting future research in diverse clinical settings. Thirdly, we acknowledge the limitation of including a relatively small number of patients, particularly those who underwent both PHI and PSMA PET/CT imaging. Additionally, we did not conduct external validation for our novel nomogram. This step is crucial before applying the nomogram in clinical practice, as external validation helps ensure its reliability and generalizability across different patient populations and settings. Fourthly, it’s important to note that the majority of data on pathological findings used in the whole cohort are based on the prostate biopsy results without radical prostatectomy, which may potentially underestimate the actual tumor burden. Therefore, while acknowledging these potential limitations, it’s essential to consider the various biopsy methods and their implications when interpreting our findings. Finally, it is important to emphasize that these nomograms are intended to provide clinicians with a quantitative tool to support the decision-making process in identifying suitable candidates for avoiding prostate biopsy. It is essential to avoid viewing the nomogram as a simplistic binary decision-making tool. Moreover, it is essential to consider the time and cost implications of employing PHI and PSMA PET/CT when evaluating the benefits of their utilization in detecting csPCa within a biopsy-free approach. Conclusions Our study developed a novel multivariate biopsy-free nomogram that incorporates PHI and PSMA SUVmax data, with the specific aim of avoid unnecessary prostate biopsies in highly suspicious patients with PI-RADS ≥ 4 lesions. The integration of this nomogram into preoperative counseling sessions provides clinicians with a valuable tool for making well-informed decisions regarding prostate biopsy, thereby ensuring that only those who truly necessitate it undergo the procedure. However, to definitively establish its efficacy and reliability of this nomogram, further prospective studies are essential. Declarations Conflict of Interest The authors have nothing to disclose. Research involving human participants Institutional Review Board approval was obtained (No.KY2020K130). Funding: Dr. Xingkang Jiang reports support from the Tianjin Health Science and Technology Project (No. KJ20169), the Second Hospital of Tianjin Medical University (No. MNRC202312 and No. MYSRC202306). The remaining authors have nothing to disclose. Author Contribution X. Jiang and S. Guo designed the study and wrote the main manuscript text; J. Wang and M. Chen assisted with data analysis and mauscript preparation, data collection and analysis; Y. Xu reviewed the mauscript. All authors contributed to the article and approved the submitted version. Acknowledgments: None. Data Availability The data are available from the corresponding author upon reasonable request. References Siegel, RL. et al. Cancer statistics, 2024. CA Cancer J Clin. 74(1):12–49. doi: 10.3322/caac.21820 (2024). Connor, MJ. et al. Landmarks in the evolution of prostate biopsy. Nat Rev Urol. 20(4):241–258. doi: 10.1038/s41585-022-00684-0 (2023). Vanoli S, et al. Evolution of anxiety management in prostate biopsy under local anesthesia: a narrative review. World J Urol. 42(1):43. doi: 10.1007/s00345-023-04723-2 (2024). Meissner VH, et al. Radical Prostatectomy Without Prior Biopsy Following Multiparametric Magnetic Resonance Imaging and Prostate-specific Membrane Antigen Positron Emission Tomography. Eur Urol. 82(2):156–160. doi: 10.1016/j.eururo.2021.11.019 (2022). Modi PK, et al. Radical Prostatectomy Without Biopsy: Audacious, Imprudent, or Innovative? Eur Urol. 82(2):161–162. doi: 10.1016/j.eururo.2022.03.008 . Epub 2022 Mar 25. PMID: 35346513 (2022). Turkbey B, et al. PI-RADS: Where Next? Radiology. 307(5):e223128. doi: 10.1148/radiol.223128 (2023). Remmers S, et al. Reducing Biopsies and Magnetic Resonance Imaging Scans During the Diagnostic Pathway of Prostate Cancer: Applying the Rotterdam Prostate Cancer Risk Calculator to the PRECISION Trial Data. Eur Urol Open Sci. 36:1–8. doi: 10.1016/j.euros.2021.11.002 (2021). Siddiqui MR, et al. Optimizing detection of clinically significant prostate cancer through nomograms incorporating mri, clinical features, and advanced serum biomarkers in biopsy naïve men. Prostate Cancer Prostatic Dis.26(3):588–595. doi: 10.1038/s41391-023-00660-8 (2023). Chung JH, et al. Nomogram Using Prostate Health Index for Predicting Prostate Cancer in the Gray Zone: Prospective, Multicenter Study. World J Mens Health. 42(1):168–177. doi: 10.5534/wjmh.220223 (2024). Huang H, et al. Based on PI-RADS v2.1 combining PHI and ADC values to guide prostate biopsy in patients with PSA 4–20 ng/mL. Prostate. 84(4):376–388. doi: 10.1002/pros.24658 (2024). Emmett L, et al. The PRIMARY Score: Using Intraprostatic 68Ga-PSMA PET/CT Patterns to Optimize Prostate Cancer Diagnosis. J Nucl Med. 63(11):1644–1650. doi: 10.2967/jnumed.121.263448 (2022). Kelly BD, et al. A Novel Risk Calculator Incorporating Clinical Parameters, Multiparametric Magnetic Resonance Imaging, and Prostate-Specific Membrane Antigen Positron Emission Tomography for Prostate Cancer Risk Stratification Before Transperineal Prostate Biopsy. Eur Urol Open Sci. 53:90–97. doi: 10.1016/j.euros.2023.05.002 (2023). Westphalen AC, et al. Variability of the Positive Predictive Value of PI-RADS for Prostate MRI across 26 Centers: Experience of the Society of Abdominal Radiology Prostate Cancer Disease-focused Panel. Radiology. 296(1):76–84. doi: 10.1148/radiol.2020190646 (2020). Xiang L, et al. Patient-related characteristics predict prostate cancers in men with PI-RADS 4–5 to further optimize the diagnostic performance of MRI. Abdom Radiol (NY). 48(12):3766–3773. doi: 10.1007/s00261-023-04011-y (2023). Wang YH, et al. Improving the understanding of PI-RADS in practice: characters of PI-RADS 4 and 5 lesions with negative biopsy. Asian J Androl. 25(2):217–222. doi: 10.4103/aja2022112 (2023). Stavrinides V, et al. Regional Histopathology and Prostate MRI Positivity: A Secondary Analysis of the PROMIS Trial. Radiology. 307(1):e220762. doi: 10.1148/radiol.220762 (2023). Norris JM, et al. What Type of Prostate Cancer Is Systematically Overlooked by Multiparametric Magnetic Resonance Imaging? An Analysis from the PROMIS Cohort. Eur Urol. 78(2):163–170. doi: 10.1016/j.eururo.2020.04.029 (2020). Zhou Y, et al. Nomograms Combining PHI and PI-RADS in Detecting Prostate Cancer: A Multicenter Prospective Study. J Clin Med. 12(1):339. doi: 10.3390/jcm12010339 (2023). Mo LC, et al. Development of a novel nomogram for predicting clinically significant prostate cancer with the prostate health index and multiparametric MRI. Front Oncol. 12:1068893. doi: 10.3389/fonc.2022.1068893 (2022). Huang H, et al. Based on PI-RADS v2.1 combining PHI and ADC values to guide prostate biopsy in patients with PSA 4–20 ng/mL. Prostate. 84(4):376–388. doi: 10.1002/pros.24658 (2024). Caracciolo M, et al. PSMA PET/CT Versus mpMRI for the Detection of Clinically Significant Prostate Cancer: An Updated Overview. Semin Nucl Med. 54(1):30–38. doi: 10.1053/j.semnuclmed.2023.10.002 (2024). Xiang M, et al. Performance of a Prostate-Specific Membrane Antigen Positron Emission Tomography/Computed Tomography-Derived Risk-Stratification Tool for High-risk and Very High-risk Prostate Cancer. JAMA Netw Open. 4(12):e2138550. doi: 10.1001/jamanetworkopen.2021.38550 (2021). Cheng C, et al. Prediction of clinically significant prostate cancer using a novel 68Ga-PSMA PET-CT and multiparametric MRI-based model. Transl Androl Urol. 12(7):1115–1126. doi: 10.21037/tau-22-832 (2023). Tayara OM, et al. Comparison of Multiparametric MRI, [68Ga]Ga-PSMA-11 PET-CT, and Clinical Nomograms for Primary T and N Staging of Intermediate-to-High-Risk Prostate Cancer. Cancers (Basel). 15(24):5838. doi: 10.3390/cancers15245838 (2023). Xing NZ, et al. Feasibility of prostatectomy without prostate biopsy in the era of new imaging technology and minimally invasive techniques. World J Clin Cases. 7(12):1403–1409. doi: 10.12998/wjcc.v7.i12.1403 (2023). Chaloupka M, et al. Radical Prostatectomy without Prior Biopsy in Patients with High Suspicion of Prostate Cancer Based on Multiparametric Magnetic Resonance Imaging and Prostate-Specific Membrane Antigen Positron Emission Tomography: A Prospective Cohort Study. Cancers (Basel). 15(4):1266. doi: 10.3390/cancers15041266 (2023). Falkenbach F, et al. PSA-density, DRE, and PI-RADS 5: potential surrogates for omitting biopsy? World J Urol. 42(1):182. doi: 10.1007/s00345-024-04894-6 (2024). Sharma AP, et al. Accuracy of combined multi-parametric MRI and PSMA PET-CT in diagnosing localized prostate cancer: newer horizons for a biopsy-free pathway. Eur J Hybrid Imaging. 7(1):24. doi: 10.1186/s41824-023-00182-5 (2023). Additional Declarations No competing interests reported. Supplementary Files SupplementaryTables.doc Supplementaryfigures.pptx Cite Share Download PDF Status: Published Journal Publication published 20 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 20 Dec, 2024 Reviews received at journal 18 Dec, 2024 Reviewers agreed at journal 13 Dec, 2024 Reviews received at journal 04 Dec, 2024 Reviewers agreed at journal 24 Nov, 2024 Reviewers agreed at journal 03 Oct, 2024 Reviewers invited by journal 17 Sep, 2024 Editor assigned by journal 18 Jul, 2024 Editor invited by journal 12 Jul, 2024 Submission checks completed at journal 11 Jul, 2024 First submitted to journal 06 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4695012","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":332275892,"identity":"21fe979f-bb55-48eb-99c7-6e891cdaa1f2","order_by":0,"name":"Junxin Wang","email":"","orcid":"","institution":"The Second Hospital of Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Junxin","middleName":"","lastName":"Wang","suffix":""},{"id":332275893,"identity":"cf3be016-0e37-458a-a476-6d914881a1ed","order_by":1,"name":"Mingzhe Chen","email":"","orcid":"","institution":"The Second Hospital of Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Mingzhe","middleName":"","lastName":"Chen","suffix":""},{"id":332275894,"identity":"786c532c-aebe-46d6-b5b1-67d67018824e","order_by":2,"name":"Yong Xu","email":"","orcid":"","institution":"The Second Hospital of Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yong","middleName":"","lastName":"Xu","suffix":""},{"id":332275895,"identity":"b2012d45-eec6-475f-bc66-df56c0b7a3af","order_by":3,"name":"Shanqi Guo","email":"","orcid":"","institution":"Tianjin Medical University General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shanqi","middleName":"","lastName":"Guo","suffix":""},{"id":332275896,"identity":"1e012ae8-51fa-4b05-9182-85fe4a1b8605","order_by":4,"name":"Xingkang Jiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvklEQVRIiWNgGAWjYBACxmYwZQPh8ZCgJU2CeC1QcJgELcztzM8efm07X6c7I4Hxwds2Bnlzwg5jMzeWOXNbwuxGArPh3DYGw50NBLUwmElLVIC1sEnztjEkGBwgqIX9m7SEwTmQFvbfRGrhMZP8UHEAbAszsVrKpBnOJEtuO/OwWXLOOQnDDYS0GPYf3yb5s82O3+x48sEPb8ps5AnaYtgADGhIdDACmQwSBNQDgTxI7Q/C6kbBKBgFo2AkAwB2QjrnfMuGnAAAAABJRU5ErkJggg==","orcid":"","institution":"The Second Hospital of Tianjin Medical University","correspondingAuthor":true,"prefix":"","firstName":"Xingkang","middleName":"","lastName":"Jiang","suffix":""}],"badges":[],"createdAt":"2024-07-06 04:54:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4695012/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4695012/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-86607-6","type":"published","date":"2025-01-20T15:58:21+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":62185643,"identity":"db8ad02d-8ab7-4dbb-91be-613018cccd3a","added_by":"auto","created_at":"2024-08-10 11:55:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":59561,"visible":true,"origin":"","legend":"\u003cp\u003eConsolidated Standards of Reporting Trials (CONSORT) diagram illustrating the inclusion of patients in the whole cohorts.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4695012/v1/54f6af63525e028d5e1310e3.png"},{"id":62184756,"identity":"1ad23a80-7e1c-4956-8d88-adb39f76fe48","added_by":"auto","created_at":"2024-08-10 11:47:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":50263,"visible":true,"origin":"","legend":"\u003cp\u003eThree Nomograms for predicting clinically significant prostate cancer. (A) Nomogram 1 (fundamental nomogram) featuring multivariable preclinical parameters. (B) Nomogram 2, incorporating the combination of PHI with basic parameters. (C) Nomogram 3, incorporating PHI, PMSA SUVmax, and basic parameters.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4695012/v1/03d1dcf93f58791fbc33998d.png"},{"id":62184754,"identity":"b769f827-e8b1-4287-83b7-a016cfb5c207","added_by":"auto","created_at":"2024-08-10 11:47:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":29641,"visible":true,"origin":"","legend":"\u003cp\u003eThe receiver operating characteristic (ROC) curve of (A) training cohort and (B) validation cohort for highly suspected prostate cancer (PI-RADS ≥4 lesions). Model 1 includes age, PV, PSAD, PSA differences to ratio, localization, and AUR. Model 2 includes PV, PSAD, PHI, localization, and AUR. Model 3 includes PHI, PSMA SUVmax, localization, and AUR.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4695012/v1/ae3f262a44b0d415a33bbab0.png"},{"id":74859086,"identity":"01082aeb-22a9-403c-b459-e529fb4d8888","added_by":"auto","created_at":"2025-01-27 16:13:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1240538,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4695012/v1/91b03cd5-445e-4110-a5c2-a17c01f4b3b3.pdf"},{"id":62184757,"identity":"82f6e735-32f9-4fb0-850c-44af98236a1b","added_by":"auto","created_at":"2024-08-10 11:47:11","extension":"doc","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":668628,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.doc","url":"https://assets-eu.researchsquare.com/files/rs-4695012/v1/fbdb3f97f26b09bd838d5708.doc"},{"id":62184759,"identity":"1a85ea1d-75d2-4f04-8e81-fca3d92115e9","added_by":"auto","created_at":"2024-08-10 11:47:11","extension":"pptx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":316800,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigures.pptx","url":"https://assets-eu.researchsquare.com/files/rs-4695012/v1/cfd56b564a97b14fe4e995f3.pptx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Validation of Newly Biopsy-Free Nomograms for Predicting Clinically Significant Prostate Cancer in Men with PI-RADS ≥4 Lesions","fulltext":[{"header":"Introduction","content":"\u003cp\u003eProstate cancer (PCa) currently ranks as the most prevalent cancer and the second leading cause of cancer-related deaths among men, based on 2024 data [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite the widespread acceptance of prostate biopsy as the gold standard for diagnosis, it presents risks of physiological complications and psychological burdens, including urinary retention, hematuria, and sepsis, which can heighten preoperative anxiety [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Multiparametric magnetic resonance imaging (mpMRI) paired with the Prostate Imaging Reporting and Data System (PI-RADS) version 2.1 grading system has garnered significant attention in assessing the likelihood of clinically significant cancer. Elevated PI-RADS grades are linked to an increased likelihood of clinically significant prostate cancer (csPCa), sparking ongoing debates on the necessity of prostate biopsy, particularly for PI-RADS 4 and 5 grades. While some advocate for bypassing biopsy entirely, others stress the importance of biopsy while minimizing unnecessary procedures [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile mpMRI accurately identifies csPCa, its low positive predictive value restricts its efficacy. Addressing this limitation, recent studies have introduced multivariable risk-based nomograms drawing on data from the European Randomized Study of Screening for PCa (ERSPC) and integrating mpMRI findings. This has resulted in enhanced cancer detection rates and a decrease in unnecessary biopsies [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Moreover, the incorporation of precision clinical parameters, such as the Prostate Health Index (PHI) - comprising total PSA (tPSA), free PSA (fPSA), and the PSA isoform [-2]proPSA (p2PSA) - into a comprehensive formula has transformed csPCa screening practices. Meta-analyses have demonstrated that PHI exhibits superior sensitivity and specificity compared to traditional PSA markers for detecting csPCa, reaffirming its significance in screening guidelines [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Furthermore, molecular imaging techniques like prostate-specific membrane antigen (PSMA) positron emission tomography (PET)/computed tomography (CT) have been proposed for enhanced diagnostic accuracy in primary staging for PCa patients. Findings from the prospective PRIMARY trial have shown that combining PSMA PET with mpMRI surpasses the performance of mpMRI alone in detecting csPCa, indicating that men with suspicious PSMA-PET and mpMRI findings may potentially forego biopsy and proceed directly to definitive treatment [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, to date, no studies have been conducted in highly suspicious patients incorporating both PHI and PSMA PET/CT images, along with multivariable clinical parameters, to predict the presence of csPCa.\u003c/p\u003e \u003cp\u003eOur study aims to investigate the potential development of biopsy-free diagnostic nomograms for csPCa in selected men with a high suspicion (PI-RADS\u0026thinsp;\u0026ge;\u0026thinsp;4) of significant malignancy in both PHI and PSMA PET/CT. The increasing interest in innovative imaging modalities for csPCa detection has prompted the creation and validation of biopsy-free nomograms based on a multivariate model incorporating clinical variables, PHI, and PSMA-PET/CT to estimate individual probabilities of aggressive PCa.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eData were retrospectively collected from two prospectively clinical studies (ChiCTR2200066455 and ChiCTR2000038696) conducted at the Second Hospital of Tianjin Medical University from January 2020 to August 2023. Biopsy-na\u0026iuml;ve patients suspected of having PCa due to elevated PSA and/or an abnormal DRE, along with highly suspicious lesions identified on the mpMRI (PI-RADS\u0026thinsp;\u0026ge;\u0026thinsp;4) were included. Exclusion criteria comprised: 1) Patients with urinary tract infection or prostatitis, 2) Patients who had undergone prostate surgery before biopsy, 3) Patients with incomplete clinical and pathological data, 4) Patients with poor MRI quality or low image resolution, and 5) Patients who had undergone previous prostate biopsies. The study was conducted in compliance with the guiding principles of the Declaration of Helsinki, and approved by the ethics committee of the Second Hospital of Tianjin Medical University (No.KY2020K130), and informed consent was obtained from each patient.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCollection of clinical information\u003c/h2\u003e \u003cp\u003eThe collected data for the whole population, and after stratification of the cohort according to the presence of csPCa includes the patients\u0026rsquo; age, height, weight, history of hypertension, history of diabetes, history of acute urinary retention (AUR) (within 1 month before biopsy), prostate volume (PV), last tPSA before biopsy, initial tPSA (within 1 month before biopsy), free PSA (fPSA), ratio of free PSA to total PSA (f/tPSA), PSA density (PSAD), digital rectal examination (DRE) findings, lesion localization on mpMRI, PI-RADS score, PHI, maximum standardized uptake value (SUVmax) on PSMA PET/CT, and pathological results. Calculation formulas for relevant clinical indicators are as follows: Body Mass Index (BMI): weight (kg) / height^2 (m^2), PSA differences to ratio: (last PSA before biopsy - initial PSA) / initial PSA * 100%. The serum concentration of total PSA, free PSA, and p2PSA was measured on the Access 2 analyzer (Beckman Coulter, Bream, CA, USA). The percentage of p2PSA (%p2PSA) was calculated using the formula [(p2PSA pg/ml)/(fPSA ug/L \u0026times; 1000)] \u0026times; 100. PHI was calculated using the formula [(p2PSA pg/ml)/(fPSA ug/L)] \u0026times; total PSA (ug/L). PV was calculated using the formula PV = ([maximum anteroposterior diameter] \u0026times; [maximum transverse diameter] \u0026times; [maximum longitudinal diameter] \u0026times; 0.52), as assessed through MRI imaging. PSAD was calculated by dividing total PSA by PV.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003empMRI protocol\u003c/h2\u003e \u003cp\u003eFollowing the recommendations of the European Society of Urogenital Radiology (ESUR), a 3.0-T MRI protocol was conducted on all participants without using an endorectal coil. The MRI scans were evaluated by a specialized genitourinary radiologist using the PI-RADS v2.1 protocols, which included multiparametric sequences such as T2-weighted fast spin echo imaging (T2WI), diffusion-weighted spin echo planar imaging (DWI), and calculation of apparent diffusion coefficient (ADC) maps using linear least squares regression. Two experienced urological radiologists, with a minimum of five years of experience in prostate MRI and blinded to the patients\u0026rsquo; clinical information, independently reviewed and assessed all MRI images based on the PI-RADS v2.1 criteria. The PI-RADS scoring system designates PI-RADS 4 and 5 as indicating a high likelihood of csPCa.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003ePSMA PET-CT\u003c/h2\u003e \u003cp\u003eAll PSMA PET scans were conducted according to our local protocol and interpreted in a clinical setting. The recommended dosage range for 68Ga-PSMA-11 is typically 1.5-3.0 MBq/kg. However, the specific dosage for each patient is determined by the nuclear medicine physician based on their individual circumstances. After injection, there is a waiting period of usually 60 minutes to allow the tracer to distribute throughout the body and bind to PSMA. The patient lies on the scanning table, which gradually moves through the PET/CT machine. The entire process generally takes around 20\u0026ndash;30 minutes. All PSMA PET scans are presented and discussed in a multidisciplinary meeting attended by at least two highly experienced nuclear medicine physicians. During the analysis, several factors are considered, including PSMA uptake. Areas of high PSMA uptake may indicate the presence of prostate cancer. The uptake intensity and pattern, as well as the intensity of uptake, can provide insights into the nature of the cancer. Uptake in other areas, such as lymph nodes or other organs, may suggest the spread of cancer. Standardized uptake values (SUV) are typically used to quantify PSMA uptake.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eHistopathological analysis\u003c/h2\u003e \u003cp\u003eEnrolled patients were underwent either ultrasound-guided transperineal prostate biopsy (combined systematic and targeted biopsy) or radical prostatectomy. Tissue samples were fixed in formalin and evaluated by two senior pathologists specializing in prostate evaluation, adhering to 2019 International Society of Urological Pathology standards. csPCa was defined as those with a Gleason score of \u0026ge;\u0026thinsp;3\u0026thinsp;+\u0026thinsp;4, while non-csPCa was defined by the absence of csPCa and included cases of benign prostatic hyperplasia, prostatitis, prostatic hyperplasia, and normal prostate tissue with calcification.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eThe statistical analyses were performed using Statistical Package for Social Science version 22.0 (SPSS 22.0, IBM Corp) and R version 4.3.2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://orcid.org/0009-0002-6654-9541\" target=\"_blank\"\u003ewww.r-project.org\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.r-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Descriptive statistics were used to summarize continuous variables, which were compared between the diagnostic and validation cohorts using the Wilcoxon rank-sum and Kruskal-Wallis tests. Categorical variables were presented as frequencies (percentages), and group comparisons were conducted using the chi-square test or Fisher\u0026rsquo;s exact test. Multiple imputation was utilized for variables with missing or outlier values, with the normality of continuous variables assessed using the Shapiro-Wilk test. Disaggregated data were presented as numbers (n) and percentages (%). Normally distributed continuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), while non-normally distributed ones were described as the median (interquartile range (IQR)). The cut-off value of the nomogram was determined using the maximum Youden index, with a significance level set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The entire cohort was randomly divided into a training cohort and a validation cohort at a 7:3 ratio. Univariate logistic regression analysis was initially conducted in the modeling dataset, followed by backward multiple logistic regression analysis after excluding variables exhibiting multicollinearity. Variables with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were retained for model establishment. The prediction model was developed using a nomogram and internally validated to assess its predictive performance. The area under the receiver operating characteristic (ROC) curve (AUC) was calculated to evaluate discrimination ability, with model calibration assessed using the Hosmer-Lemeshow test and calibration curve. Decision curve analysis (DCA) evaluated net benefit and clinical utility.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eClinical characteristics\u003c/h2\u003e\n \u003cp\u003eA total of 931 patients met the inclusion criteria and were included in the overall population (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The baseline characteristics of the patients are presented in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Among them, 779 (83.7%) patients received a diagnosis of csPCa and 152 (16.3%) patients were diagnosed with non-csPCa. Patients with csPCa exhibited higher levels of age, PSA, fPSA, PSAD, percentage differences in PSA ratio, PI-RADS score, peripheral zone location, acute urinary retention, diabetes, DRE findings, and lower PV and %f/TPSA compared to those with non-csPCa. In the cohort of 316 and 198 patients undergoing PHI and PSMA PET/CT imaging, the PHI and PSMA SUVmax were notably elevated in individuals with csPCa compared to those with non-csPCa (\u003cstrong\u003eTable \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e-2\u003c/strong\u003e). Among these patients, a random assignment of 7:3 was made to the training and validation cohorts, and a detailed comparison of demographic data, comorbidities, and characteristics between the three cohorts is presented in \u003cstrong\u003eTable S3-6\u003c/strong\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePatients\u0026rsquo; Characteristics of Enrolled Population (n\u0026thinsp;=\u0026thinsp;931).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWhole cohort (n\u0026thinsp;=\u0026thinsp;931)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003enon-csPCa (n\u0026thinsp;=\u0026thinsp;152)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ecsPCa (n\u0026thinsp;=\u0026thinsp;779)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge at biopsy (yr), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71 (66\u0026ndash;76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69 (65.00-73.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71 (66\u0026ndash;76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m2),median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.45 (22.49\u0026ndash;26.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.25 (22.49\u0026ndash;26.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.45 (22.49\u0026ndash;26.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.865\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePSA (ng/ml), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.90 (12.30-78.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.25 (6.11\u0026ndash;20.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.33 (16.53\u0026ndash;87.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePSA, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (2.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (9.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (0.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u0026ndash;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e319 (34.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100 (65.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e219 (28.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u0026ndash;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e243 (26.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32 (21.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e211 (27.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e350 (37.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (3.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e344 (44.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003efPSA (ng/ml), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.49 (1.51\u0026ndash;8.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.71 (0.86\u0026ndash;2.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.49 (1.75-10.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e% f/tPSA, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.95 (8.23\u0026ndash;15.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.03 (9.26\u0026ndash;20.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.61 (8.13\u0026ndash;14.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTestosterone (ng/dl), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e377.62 (299.80-449.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e385.74 (330.90-452.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e374.76 (297.12-449.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.244\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePV (ml), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.09 (37.61\u0026ndash;75.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.10 (49.22\u0026ndash;96.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.20 (36.36\u0026ndash;72.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePV, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e397 (42.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40 (26.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e357 (45.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e534 (57.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112 (73.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e422 (54.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePSAD (ng/ml2), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62 (0.25\u0026ndash;1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17 (0.10\u0026ndash;0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78 (0.37\u0026ndash;1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePSAD, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e187 (20.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91 (59.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96 (12.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u0026ndash;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e208 (22.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44 (28.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e164 (21.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5-1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e227 (24.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (10.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e211 (27.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e309 (33.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e308 (39.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e%PSA differences to ratio, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.33 (-16.32-10.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-21.93 (-40.94\u0026ndash;2.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.84 (-9.84-12.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e%PSA differences to ratio, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;-50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51 (5.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 (22.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (2.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-50\u0026ndash;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e133 (14.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44 (28.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89 (11.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;-20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e747 (80.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74 (48.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e673 (86.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePI-RADS score, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e374 (40.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e123 (80.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e251 (32.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e557 (59.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29 (19.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e528 (67.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLocalization of suspicious lesion, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e466 (50.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81 (53.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e385 (49.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e124 (13.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46 (30.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78 (10.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61 (6.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (15.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38 (4.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePZ\u0026thinsp;+\u0026thinsp;TZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e280 (30.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e278 (35.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAUR, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e144 (15.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45 (29.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99 (12.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e197 (21.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22 (14.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e175 (22.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.027\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e412 (44.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70 (46.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e342 (43.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.625\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDRE, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e521 (55.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66 (43.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e455 (58.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eBMI\u0026thinsp;=\u0026thinsp;body mass index; IQR\u0026thinsp;=\u0026thinsp;inter quartile range; PI-RADS\u0026thinsp;=\u0026thinsp;Prostate Imaging Reporting and Data System; PSA\u0026thinsp;=\u0026thinsp;prostate-specific antigen; PSAD\u0026thinsp;=\u0026thinsp;prostate-specific antigen density; PZ\u0026thinsp;=\u0026thinsp;peripheral zone; TZ\u0026thinsp;=\u0026thinsp;transition zone; Others\u0026thinsp;=\u0026thinsp;lesions beyond the peripheral and transition zones; DRE\u0026thinsp;=\u0026thinsp;Digital Rectal Examination; The p values were calculated using the chi-square (categorical variables) and Mann-Whitney (continuous variables) tests. p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in bold.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003ePrediction Model Development\u003c/h2\u003e\n \u003cp\u003eAfter multivariate analysis, factors including age, PV, PSAD, and %PSA differences to ratio, peripheral zone location, and AUC were identified as strong associated with csPCa findings within the whole cohort (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cstrong\u003eTable S7\u003c/strong\u003e). Subsequently, the foundational nomogram (model 1) comprised of age, PV, PSAD, %PSA differences to ratio, localization of suspicious lesion, and AUC, demonstrated an AUC of 0.918 (95% confidence interval [CI] 0.894\u0026ndash;0.943) following internal validation. Additionally, when integrating PHI into the baseline model, age and %PSA differences to ratio were excluded from nomogram 2, resulting in an AUC of 0.908 (95% CI 0.863\u0026ndash;0.953). The incorporation of PHI and PSMA SUVmax into the foundational nomograms (model 3), which included PHI, PSMA SUVmax, localization of suspicious lesion, and AUR, led to a substantial enhancement in the AUC for predicting csPCa, elevating it to 0.955 (95% CI 0.923\u0026ndash;0.987) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The aforementioned three nomograms were validated using their validation cohorts and are readily accessible online (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.evidencio.com/models/show/10355\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMultivariable Logistic Regression Analysis for Predicting Clinically Significant Prostate Cancer (csPCa) Using Three Models.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07 (1.02\u0026ndash;1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98 (0.97-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.014\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ereference group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38 (0.21\u0026ndash;0.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePSAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.65 (0.48\u0026ndash;28.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ereference group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u0026ndash;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.67 (1.43\u0026ndash;4.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5-1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.73 (2.64\u0026ndash;12.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e%PSA differences to ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;-50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ereference group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-50\u0026ndash;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.32 (1.35\u0026ndash;13.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.014\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;-20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.27 (2.46\u0026ndash;21.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePHI, median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02 (1.01\u0026ndash;1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01 (1.00-1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.047\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePSMA PET/CT SUV max\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.25 (1.06\u0026ndash;1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLocalization of suspicious lesion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ereference group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ereference group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ereference group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28 (0.15\u0026ndash;0.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17 (0.05\u0026ndash;0.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08 (0.02\u0026ndash;0.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31 (0.12\u0026ndash;0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24 (0.05\u0026ndash;1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.065\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.28 (0.10-16.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.851\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePZ\u0026thinsp;+\u0026thinsp;TZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.50 (2.35\u0026ndash;145.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.50 (0.28\u0026ndash;22.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.26 (0.53\u0026ndash;74.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAUR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ereference group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ereference group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ereference group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25 (0.11\u0026ndash;0.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21 (0.02\u0026ndash;0.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.012\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10 (0.02\u0026ndash;0.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003ecsPCa\u0026thinsp;=\u0026thinsp;clinically significant prostate cancer; OR\u0026thinsp;=\u0026thinsp;odds ratio; CI\u0026thinsp;=\u0026thinsp;confidence interval; AIC\u0026thinsp;=\u0026thinsp;Akaike Information Criterion; PHI\u0026thinsp;=\u0026thinsp;Prostate Health Index; PET\u0026thinsp;=\u0026thinsp;positron emission tomography; PI-RADS\u0026thinsp;=\u0026thinsp;Prostate Imaging Reporting and Data System; PSAD\u0026thinsp;=\u0026thinsp;prostate-specific antigen density; PSMA\u0026thinsp;=\u0026thinsp;prostate-specific membrane antigen; PZ\u0026thinsp;=\u0026thinsp;peripheral zone; TZ\u0026thinsp;=\u0026thinsp;transition zone; Others\u0026thinsp;=\u0026thinsp;lesions beyond the peripheral and transition zones.\u003c/p\u003e\n \u003cp\u003ea AIC min\u0026thinsp;=\u0026thinsp;344.82.b AIC min\u0026thinsp;=\u0026thinsp;142.28.c AIC min\u0026thinsp;=\u0026thinsp;71.13.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eDecision Curve Analysis\u003c/h2\u003e\n \u003cp\u003eThe calibration plot demonstrates superior fit of the advanced model compared with the baseline model in both the training cohort and validation cohort (\u003cstrong\u003eFigure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e). To validate the efficacy of the nomogram, a Decision Curve Analysis (DCA) was conducted, revealing that advanced models enhanced clinical risk prediction for csPCa with a threshold probability of 80%, with models 2 and 3 graphically superior to model 1 (38.78 vs. 51.86 and 53.57)(\u003cstrong\u003eFigure \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/strong\u003e). For the optimal cutoff values of PSAD, PHI, SUVmax, and nomograms in predicting csPCa in these highly suspicious patients with PI-RADS 4 and 5 lesions, a comprehensive analysis of various thresholds was conducted, and the findings are summarized in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. Using a cutoff of 38.9%, the proportion of patients eligible for biopsy-free was 92.41%, at the cost of missing 1.53% patients with csPCa.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePredictive Performance of Different Cut-off Values of Prostate-specific Antigen Density (PSAD), Prostate Health Index (PHI), Maximum Standardized Uptake Value (SUVmax), and Three Nomograms.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDecision to biopsy\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003esensitivity,%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpecificity,%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePPV,%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNPV,%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%Avoided biopsy\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%Missed CsPCa\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNP\u003csup\u003ea\u003c/sup\u003e\u0026ge;0.757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNP\u003csup\u003eb\u003c/sup\u003e\u0026ge;0.458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNP\u003csup\u003ec\u003c/sup\u003e\u0026ge;0.991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"9\"\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNP\u003csup\u003ea\u003c/sup\u003e\u0026ge;0.852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNP\u003csup\u003eb\u003c/sup\u003e\u0026ge;0.389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNP\u003csup\u003ec\u003c/sup\u003e\u0026ge;0.987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePHI\u003csup\u003ea\u003c/sup\u003e\u0026ge;94.570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePHI\u003csup\u003eb\u003c/sup\u003e\u0026ge;45.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePHI\u003csup\u003ec\u003c/sup\u003e\u0026ge;203.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePSAD\u003csup\u003ea\u003c/sup\u003e\u0026ge;9.480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePSAD\u003csup\u003eb\u003c/sup\u003e\u0026ge;4.545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePSAD\u003csup\u003ec\u003c/sup\u003e\u0026ge;1.640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"9\"\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNP\u003csup\u003ea\u003c/sup\u003e\u0026ge;0.852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNP\u003csup\u003eb\u003c/sup\u003e\u0026ge;0.389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNP\u003csup\u003ec\u003c/sup\u003e\u0026ge;0.954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePHI\u003csup\u003ea\u003c/sup\u003e\u0026ge;94.570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePHI\u003csup\u003eb\u003c/sup\u003e\u0026ge;45.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePHI\u003csup\u003ec\u003c/sup\u003e\u0026ge;200.715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePSMA SUV max\u003csup\u003ea\u003c/sup\u003e\u0026ge;9.480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePSMA SUV max\u003csup\u003eb\u003c/sup\u003e\u0026ge;4.545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePSMA SUV max\u003csup\u003ec\u003c/sup\u003e\u0026ge;16.520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003ecsPCa\u0026thinsp;=\u0026thinsp;clinically significant prostate cancer; PI-RADS\u0026thinsp;=\u0026thinsp;Prostate Imaging Reporting and Data System; NP\u0026thinsp;=\u0026thinsp;nomogram predictive; NPV\u0026thinsp;=\u0026thinsp;negative predictive value; PHI\u0026thinsp;=\u0026thinsp;Prostate Health Index; PPV\u0026thinsp;=\u0026thinsp;positive predictive value; PSAD\u0026thinsp;=\u0026thinsp;prostate-specific antigen density; PET\u0026thinsp;=\u0026thinsp;positron emission tomography; PSMA\u0026thinsp;=\u0026thinsp;prostate-specific membrane antigen.\u003c/p\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e the cut-off value at the maximum Youden index.\u003c/p\u003e\n \u003cp\u003e\u003csup\u003eb\u003c/sup\u003e the cut-off value at maximum accuracy.\u003c/p\u003e\n \u003cp\u003e\u003csup\u003eC\u003c/sup\u003e the cut-off value at maximum specificity.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn 2020, a comprehensive cross-sectional study unveiled that PI-RADS scores of 4 and 5 are robust indicators of a high suspicion of csPCa, with corresponding positive predictive values of 39% and 72% [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. By 2023, Xiang et al. developed a predictive model based on patient-related characteristics for the detection of PCa in individuals with PI-RADS 4\u0026ndash;5 lesions. Their study, which involved 833 patients, demonstrated that 83.0% of prostate cancer cases were identified in those with PI-RADS scores of 4 or higher, with 74.5% in PI-RADS 4 lesions and 91.8% in PI-RADS 5 lesions. Notably, independent characteristics within the PI-RADS 4 subgroup, such as lesion location, age, fPSA/total PSA ratio, and PSAD, were identified and used to establish the predictive model, achieving an area under the curve (AUC) of 0.748 (95% CI 0.694\u0026ndash;0.803). Additionally, the prediction model for PI-RADS 5 was developed based on PSA and PSAD, resulting in an AUC of 0.893 (95% CI 0.844\u0026ndash;0.941) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In the present study involving 981 patients, the diagnostic rate of csPCa was 83.7%, with 67.1% identified in PI-RADS 4 lesions and 94.8% in PI-RADS 5 lesions. Our study established and validated a fundamental diagnostic nomogram that obviates the need for biopsy in predicting csPCa, achieving an AUC of 0.918 (95% CI 0.894\u0026ndash;0.943). This nomogram incorporates preclinical parameters such as age, PV, PSAD, suspicious lesion location, and %PSA differences to ratio, and AUR. While our results reinforce the connection between higher PI-RADS scores and an increased likelihood of csPCa, a small subset of individuals still received negative biopsy results, highlighting the hesitance to avoid prostate biopsies. Factors contributing to \u0026ldquo;false-positive MRI diagnoses\u0026rdquo; include PI-RADS overestimation, ambiguous images leading to inflated PI-RADS scores, diseases posing challenges in differentiation, and missed lesions during initial biopsies, with the former two factors being predominant [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Given these considerations, integrating additional clinical parameters and molecular imaging may be essential to enhance multiparametric MRI interpretations for accurate csPCa prediction and potentially reduce the necessity for unnecessary prostate biopsies in individuals with highly suspicious PI-RADS\u0026thinsp;\u0026ge;\u0026thinsp;4 lesions.\u003c/p\u003e \u003cp\u003eSeveral studies have indicated that integrating PHI into multivariate models comprising clinical and demographic variables enhances diagnostic accuracy in predicting csPCa. For instance, Zhou et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] showed that the combined assessment of PHI, PI-RADS scores, and other clinical factors (such as age, PI-RADS, and Log PSA Density) yielded AUC values of 0.902 for PCa and 0.896 for csPCa, respectively. Similarly, Mo et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] presented a multivariable model incorporating PI-RADS, fPSA, PHI, we evaluated the diagnostic precision of PHI and apparent diffusion coefficient (ADC) values based on PI-RADS v2.1 for guiding prostate biopsy in patients with PSA levels ranging from 4 to 20 ng/mL. The predictive model we devised, comprising age, PHI, PV, and ADC values as independent predictors, demonstrated an AUC of 0.856 for predicting csPCa [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In the current work, we also validated that PHI improves the detection rate of csPCa in patients with PI-RADS\u0026thinsp;\u0026ge;\u0026thinsp;4 lesions, in line with previous findings. Incorporating PHI into the basic variables, Model 2 demonstrated a comparable AUC to Model 1 in both the training and validation cohorts (AUC: 0.918 vs. 0.908, and 0.908 vs. 0.847, respectively). The observed similarity in AUC between Model 1 and Model 2 may be attributed to the relatively small sample size. With a smaller sample size, the statistical power to detect differences between models might be diminished. Additionally, we utilized the entire cohort of 316 patients for training and consistently obtained similar results during nomogram validation. Consequently, even if Model 2 exhibits improvement over Model 1, it might not achieve statistical significance due to the limitations imposed by the sample size. Therefore, a larger sample size might be necessary to more accurately assess the performance differences between these models.\u003c/p\u003e \u003cp\u003eThe advanced molecular imaging technique, PSMA PET/CT, offers superior diagnostic precision in identifying various conditions of PCa, including active surveillance, biochemical recurrence, lymph node metastasis, as well as metastatic castration-resistant disease, potentially influencing treatment decisions [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Therefore, incorporating PSMA PET/CT into screening programs as an adjunctive tool can assist in decreasing the overdiagnosis of insignificant cancer, while also enhancing the diagnostic accuracy for csPCa [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Despite cost considerations, several authors have recommended the use of PSMA PET/CT due to potential cost savings and improved quality of life resulting from avoid unnecessary biopsies. By employing preclinical risk stratification with PSMA-PET/CT imaging, some individuals have successfully undergone radical prostatectomy without prior biopsy, presenting promising outcomes and potentially eliminating the need for biopsy in patients with highly suspicious lesions rated PI-RADS\u0026thinsp;\u0026ge;\u0026thinsp;4 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, as the saying goes, \u0026ldquo;all truth passes through three stages: first, it is ridiculed; second, it is violently opposed; third, it is accepted as self-evident.\u0026rdquo; Considering this context, our objective was to investigate the feasibility and outcomes of a biopsy-free approach based on preclinical risk stratification, integrating PHI and PSMA SUVmax in patients with highly suspicious lesions rated PI-RADS\u0026thinsp;\u0026ge;\u0026thinsp;4. By incorporating both PHI and PSMA SUVmax, the model 3 exhibited superior discriminatory power compared to the foundational nomogram (model 1) and model 2 (AUC: from 0.918 and 0.908 to 0.955). Additionally, model 3 exhibited outstanding calibration in predicting the risk of csPCa, suggesting that incorporation of this advanced model into clinical practice could potentially eliminate the need for prostate biopsy.\u003c/p\u003e \u003cp\u003eWhile our study possesses notable strengths, it is imperative to acknowledge its limitations. Firstly, the retrospective nature of our analysis may have introduced patient selection biases, potentially impacting the generalizability of our findings. We recognize this limitation and comprehend the potential influence of selection bias on our results. Secondly, our study concentrated on patients with PI-RADS\u0026thinsp;\u0026ge;\u0026thinsp;4 lesions from a single institution, where mpMRIs and PSMA PET/CT were predominantly interpreted by experienced radiologists. This scenario may restrict the applicability of our findings to institutions with less experienced radiologists, thus affecting their generalizability. Additionally, variations in PSMA tracers can also influence the values of SUVmax. It is essential to acknowledge this potential limitation and underscore the importance of conducting future research in diverse clinical settings. Thirdly, we acknowledge the limitation of including a relatively small number of patients, particularly those who underwent both PHI and PSMA PET/CT imaging. Additionally, we did not conduct external validation for our novel nomogram. This step is crucial before applying the nomogram in clinical practice, as external validation helps ensure its reliability and generalizability across different patient populations and settings. Fourthly, it\u0026rsquo;s important to note that the majority of data on pathological findings used in the whole cohort are based on the prostate biopsy results without radical prostatectomy, which may potentially underestimate the actual tumor burden. Therefore, while acknowledging these potential limitations, it\u0026rsquo;s essential to consider the various biopsy methods and their implications when interpreting our findings. Finally, it is important to emphasize that these nomograms are intended to provide clinicians with a quantitative tool to support the decision-making process in identifying suitable candidates for avoiding prostate biopsy. It is essential to avoid viewing the nomogram as a simplistic binary decision-making tool. Moreover, it is essential to consider the time and cost implications of employing PHI and PSMA PET/CT when evaluating the benefits of their utilization in detecting csPCa within a biopsy-free approach.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study developed a novel multivariate biopsy-free nomogram that incorporates PHI and PSMA SUVmax data, with the specific aim of avoid unnecessary prostate biopsies in highly suspicious patients with PI-RADS\u0026thinsp;\u0026ge;\u0026thinsp;4 lesions. The integration of this nomogram into preoperative counseling sessions provides clinicians with a valuable tool for making well-informed decisions regarding prostate biopsy, thereby ensuring that only those who truly necessitate it undergo the procedure. However, to definitively establish its efficacy and reliability of this nomogram, further prospective studies are essential.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of Interest\u003c/h2\u003e \u003cp\u003eThe authors have nothing to disclose.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eResearch involving human participants\u003c/h2\u003e \u003cp\u003e Institutional Review Board approval was obtained (No.KY2020K130).\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eDr. Xingkang Jiang reports support from the Tianjin Health Science and Technology Project (No. KJ20169), the Second Hospital of Tianjin Medical University (No. MNRC202312 and No. MYSRC202306). The remaining authors have nothing to disclose.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eX. Jiang and S. Guo designed the study and wrote the main manuscript text; J. Wang and M. Chen assisted with data analysis and mauscript preparation, data collection and analysis; Y. Xu reviewed the mauscript. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\u003ch2\u003eAcknowledgments:\u003c/h2\u003e \u003cp\u003eNone.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel, RL. et al. Cancer statistics, 2024. CA Cancer J Clin. 74(1):12\u0026ndash;49. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3322/caac.21820\u003c/span\u003e\u003cspan address=\"10.3322/caac.21820\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eConnor, MJ. et al. Landmarks in the evolution of prostate biopsy. 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Eur J Hybrid Imaging. 7(1):24. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s41824-023-00182-5\u003c/span\u003e\u003cspan address=\"10.1186/s41824-023-00182-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Magnetic Resonance Imaging, Nomograms, Prostate Neoplasms, Positron Emission Tomography Computed Tomography, (18)F-PSMA-11","lastPublishedDoi":"10.21203/rs.3.rs-4695012/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4695012/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo develop and validate biopsy-free nomograms to more accurately predict clinically significant prostate cancer (csPCa) in biopsy-na\u0026iuml;ve men with Prostate Imaging Reporting and Data System (PI-RADS)\u0026thinsp;\u0026ge;\u0026thinsp;4 lesions. A cohort of 931 patients with PI-RADS\u0026thinsp;\u0026ge;\u0026thinsp;4 lesions, undergoing prostate biopsies or radical prostatectomy from January 2020 to August 2023, was analyzed. Various clinical variables, including age, prostate-specific antigen (PSA) levels, prostate volume (PV), PSA density (PSAD), prostate health index (PHI), and maximum standardized uptake values (SUVmax) from PSMA PET-CT imaging, were assessed for predicting csPCa. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), calibration plots, and decision-curve analyses, with internal validation. The foundational model (nomogram 1) encompassed the entire cohort, accurately predicting csPCa by incorporating variables such as age, PSAD, PV, PSA ratio variations, suspicious lesion location, and history of acute urinary retention (AUR). The AUC for csPCa prediction achieved by the foundational model was 0.918, with internal validation confirming reliability (AUC: 0.908). Advanced models (nomogram 2 and 3), incorporating PHI and PHI\u0026thinsp;+\u0026thinsp;PSMA SUVmax, achieved AUCs of 0.908 and 0.955 in the training set and 0.847 and 0.949 in the validation set, respectively. Decision analysis indicated enhanced biopsy outcome predictions with the advanced models. Nomogram 3 could potentially reduce biopsies by 92.41%, while missing only 1.53% of csPCa cases. In conclusion, the newly biopsy-free approaches for patients with PI-RADS\u0026thinsp;\u0026ge;\u0026thinsp;4 lesions represent a significant advancement in csPCa diagnosis in this high-risk population.\u003c/p\u003e","manuscriptTitle":"Development and Validation of Newly Biopsy-Free Nomograms for Predicting Clinically Significant Prostate Cancer in Men with PI-RADS ≥4 Lesions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-10 11:47:06","doi":"10.21203/rs.3.rs-4695012/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-20T05:04:39+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-18T11:37:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"104049431746538742857633805540939332559","date":"2024-12-13T22:11:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-04T19:38:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"42030166846769735403954639267627398125","date":"2024-11-25T03:13:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"137826267642376832293781500214875528798","date":"2024-10-03T08:35:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-17T17:27:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-18T15:46:09+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-07-12T07:38:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-11T04:16:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-07-06T04:52:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"71c679e0-078d-419c-8d87-8b8a53ef3a0b","owner":[],"postedDate":"August 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":35184768,"name":"Health sciences/Oncology/Cancer/Urological cancer"},{"id":35184769,"name":"Health sciences/Urology/Prostate"}],"tags":[],"updatedAt":"2025-01-27T16:10:37+00:00","versionOfRecord":{"articleIdentity":"rs-4695012","link":"https://doi.org/10.1038/s41598-025-86607-6","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-01-20 15:58:21","publishedOnDateReadable":"January 20th, 2025"},"versionCreatedAt":"2024-08-10 11:47:06","video":"","vorDoi":"10.1038/s41598-025-86607-6","vorDoiUrl":"https://doi.org/10.1038/s41598-025-86607-6","workflowStages":[]},"version":"v1","identity":"rs-4695012","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4695012","identity":"rs-4695012","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}