Predictive Value of Quantitative MRI Parameters for Clinically Significant Prostate Cancer: A Comparison Between PI-RADS v2.1 Category 4 and 5 Lesions on Biparametric MRI

Predictive Value of Quantitative MRI Parameters for Clinically Significant Prostate Cancer: A Comparison Between PI-RADS v2.1 Category 4 and 5 Lesions on Biparametric MRI | 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 Research Article Predictive Value of Quantitative MRI Parameters for Clinically Significant Prostate Cancer: A Comparison Between PI-RADS v2.1 Category 4 and 5 Lesions on Biparametric MRI Xiaoyan Zhang, Wenya Liu, Li Xu, Minghua Sun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8144341/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose Evaluating the role of quantitative MRI parameters in differentiating PI-RADS v2.1 category 4 and 5 lesions for improved detection of clinically significant prostate cancer(csPCa).​ Methods In a retrospective analysis of 153 biopsy-proven patients with suspected csPCa undergoing PI-RADS v2.1 MRI, two radiologists independently measured lesion dimensions (maximum diameter, mean diameter, area, volume) on tri-planar T2-weighted images(T2WI). Metric differences were assessed between csPCa and benign prostatic hyperplasia (BPH), and across prostatic zones. For PI-RADS ≥ 4 lesions, the diagnostic performance of axial plane measurements was compared using Receiver operating characteristic (ROC)analysis. Results A total of 165 nodules from 153 patients were analyzed, comprising 108 csPCa (69 in the peripheral zone (PZ), 39 in the transition zone (TZ)) and 57 non-csPCa (20 PZ, 37 TZ). csPCa nodules exhibited significantly larger maximum and mean diameters than BPH nodules across all imaging planes (axial, coronal, sagittal; all p < 0.05), with no significant differences in these diameters among the planes themselves. Similarly, csPCa nodules demonstrated greater area and volume than BPH nodules in both the TZ and PZ. For diagnosing csPCa, the respective Area Under the Curves (AUCs)for maximum diameter, mean diameter, area, and volume were approximately 0.701, 0.723, 0.722, and 0.811 in the TZ, and 0.733, 0.738, 0.722, and 0.819 in the PZ. Conclusion In PI-RADS v2.1 category ≥ 4 nodules, which exhibited no dominant growth orientation, volume outperformed diameter and area metrics for diagnosing csPCa, with proposed thresholds of 1.07 mm³ and 1.23 mm³ for differentiating categories 4 and 5, respectively. Prostate cancer PI-RADS v2.1 Volume MRI Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction In recent years, the incidence of prostate cancer (PCa) has been gradually increasing, accompanied by a relatively high mortality [ 1 – 2 ]. Effective screening methods and early appropriate management are therefore crucial [ 3 ]. The Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1) is a semi-quantitative assessment tool based on the morphological features and signal intensity of prostate nodules on multiparametric MRI (mpMRI) [ 4 – 6 ]. Its accuracy in detecting clinically significant prostate cancer (csPCa) has been widely validated in clinical practice [ 7 , 8 ] and is recognized as an effective evaluation method [ 9 , 10 ]. However, the consistency of its accuracy for csPCa grading via targeted biopsy varies across studies [ 11 , 12 ], and false negatives remain a concern [ 13 , 14 ]. A study by Yilmaz EC et al. [ 15 ] reported csPCa detection rates of 0%, 9%, 14%, 37%, and 77% for PI-RADS scores 1 to 5, respectively. Wen J et al. [ 16 ] conducted a study involving 266 patients with suspected PCa and found that the area under the receiver operating characteristic (ROC) curve (AUC) of PI-RADS v2.1 for diagnosing csPCa was 0.85 (95% CI: 0.78–0.93, P = 0.031), which was lower than that of PI-RADS v2.1 combined with prostate-specific antigen density (PSAD)(AUC = 0.90, 95% CI: 0.83–0.96). In another study, van Riel LAMJG et al. [ 17 ] performed biopsies on 232 patients with negative multiparametric MRI results and detected csPCa in 18.1% of cases. Improving the diagnostic performance of PI-RADS for prostate lesions has become a major research focus. PI-RADS v2.1 uses identical morphological criteria for categories 4 and 5 lesions, with differentiation primarily based on whether the longest diameter exceeds 1.5 cm or if extraprostatic extension. Prostate nodules exhibit diverse morphologies, and it remains unclear whether they demonstrate consistent three-dimensional growth patterns. This uncertainty may influence the selection of MRI scanning planes and the measurement of nodule longest diameter, thereby affecting the accurate PI-RADS v2.1 assessment. PI-RADS v2.1 recommends that T2-weighted imaging(T2WI) include at least two orthogonal planes, with the axial plane being mandatory. However, it is still unknown whether adding a second optional plane or acquiring three planes simultaneously could improve the accuracy of csPCa detection using PI-RADS v2.1. Furthermore, the potential of quantitative parameters such as total cross-sectional area (sum of areas across all imaging planes) and volume to better differentiate between PI-RADS category 4 and 5 nodules requires further investigation. This study aims to explore the influence of imaging plane selection for longest diameter measurement, as well as the utility of cross-sectional area and volume measurements, in refining the distinction between PI-RADS 4 and 5 nodules, with the goal of enhancing the accuracy of csPCa detection using PI-RADS v2.1. ​Materials and methods Patients This retrospective study was approved by the local Research Ethics Committee at the XXX (Approval Code: XXX). Written informed consent was obtained from all participants. We initially enrolled 376 patients who presented with either a suspicious digital rectal examination (DRE) finding or a prostate-specific antigen (PSA) level > 10 ng/mL and subsequently underwent multiparametric magnetic resonance imaging (mpMRI) according to the PI-RADS v2.1 protocol at our institution between 2019 and 2024. Among them, 223 patients were excluded for the following reasons: prior radiotherapy before MRI (n = 35), poor image quality insufficient for PI-RADS v2.1 assessment (n = 27), or a PI-RADS v2.1 score of less than 4(n = 161), as detailed in the patient selection flowchart (Fig. 1 ). Consequently, a final cohort of 153 patients was included in the analysis. Pathological confirmation for all included cases was obtained via ultrasound-guided transrectal biopsy. MRI technique and image analysis All mpMRI examinations were conducted on a 3.0 T MRI scanner (Vida, Siemens Healthineers) using a combination of a 4- to 16-channel pelvic surface coil and an 8- to 12-channel spine coil. The imaging protocol comprised the following sequences: axial T1-weighted imaging (T1WI), axial T2WI, coronal and sagittal T2WI, diffusion-weighted imaging (DWI) with b-values of 50, 800, 1200, and 1500 s/mm², and axial dynamic contrast-enhanced (DCE) imaging. Detailed acquisition parameters are provided in Table 1 . The PI-RADS v2.1 scoring of prostate nodules was independently performed by two genitourinary radiologists trained in PI-RADS v2.1, who were blinded to the pathological results. Any discrepancies in scoring were resolved through consensus discussion to determine the final category. The maximum diameter, mean diameter, area, and volume of each prostate nodule were measured on T2WI. For every nodule, the longest diameter was measured separately on axial, coronal, and sagittal planes, from which the average diameter was calculated. The mean diameter of a prostate nodule in a given scanning plane was defined as the average of its longest diameter and the perpendicular diameter in that same plane. The total nodule area was calculated as the sum of the areas of the nodule across all axial slices where it was visible. Nodule volume was estimated using the ellipsoid formula: ML (cm) × AP (cm) × CC (cm) × π/6 [ 18 ]. Ultrasound-guided transrectal prostate biopsy was performed by a urologist with over 10 years of clinical experience, who had performed more than 100 targeted prostate biopsy procedures within the past five years. The procedure utilized real-time multiparametric MRI-ultrasound fusion guidance (systems: Toshiba Aplio 500 or Hitachi HI VISION Preirus). The biopsy protocol included both targeted biopsies of MRI-suspicious lesions and a systematic 12-core + X extended template biopsy. All obtained specimens were labeled according to the biopsy sequence number and prostate zone, and placed in individual containers. Pathological reporting documented complete sampling information. The final diagnosis for each lesion was confirmed by a consensus review conducted by two certified genitourinary radiologists and one uropathologist. Statistical Analysis Statistical analyses were performed using SPSS software (version 23.0; IBM Corp.) and MedCalc Statistical Software (version [specify if known]; MedCalc Software Ltd.). The intraclass correlation coefficient (ICC) was used to evaluate the interobserver agreement between the two radiologists for quantitative measurements. ICC values were interpreted as follows: >0.75 indicated excellent agreement, 0.40–0.75 indicated moderate agreement, and < 0.40 indicated poor agreement. Comparisons between groups were conducted using the independent samples t-test for normally distributed data or the Mann-Whitney U test for non-normally distributed data. Continuous variables are presented as mean ± standard deviation (mean ± SD). ROC curves were generated using MedCalc software to assess the diagnostic performance of various parameters. The optimal cut-off values were determined by maximizing the Youden’s index. The AUC was calculated for each parameter, and a two-tailed p-value of less than 0.05 was considered statistically significant. Results A total of 165 prostate nodules were identified in 153 patients (97 with csPCa and 56 with BPH). Among these nodules, 108 were pathologically confirmed as csPCa (69 in the peripheral zone (PZ) and 39 in the transition zone (TZ)), and 57 were non-csPCa (20 in the PZ and 37 in the TZ). No significant difference in age was observed between patients with csPCa (74.28 ± 7.78 years) and those with benign prostatic hyperplasia (BPH) (71.93 ± 8.80 years) (P > 0.05). However, both total PSA (tPSA) and PSA density (PSAD) were significantly higher in the csPCa group compared to the BPH group (tPSA: 91.51 ± 233.27 ng/mL vs. 27.32 ± 33.05 ng/mL; PSAD: 2.43 ± 6.83 vs. 0.45 ± 0.45; all P < 0.05). Interobserver agreement between the two radiologists was excellent for all quantitative measurements, with ICC as follows: maximum diameter, 0.990 (95% CI: 0.986–0.993); mean diameter, 0.962 (95% CI: 0.948–0.972); area, 0.899 (95% CI: 0.865–0.925); and volume, 0.847 (95% CI: 0.798–0.885). In both the TZ and PZ, the maximum and mean diameters of csPCa nodules measured on axial, coronal, and sagittal planes were significantly larger than those of BPH nodules (all P 0.05) (Table 2 , Table 3 ). Both the area and volume of csPCa nodules were significantly greater than those of BPH nodules in both the TZ and PZ (TZ area: 19.01 ± 18.26 mm² vs. 8.18 ± 8.58 mm², volume: 5.89 ± 5.86 mm³ vs. 1.37 ± 1.29 mm³; PZ area: 16.90 ± 22.09 mm² vs. 3.48 ± 4.12 mm², volume: 5.42 ± 12.23 mm³ vs. 1.08 ± 1.13 mm³; all P < 0.05). All measured parameters (maximum diameter, mean diameter, area, and volume) demonstrated good diagnostic performance for csPCa in both zones. For TZ nodules, the sensitivity and specificity were 51.28% and 86.49% for maximum diameter, 69.23% and 72.97% for mean diameter, 66.7% and 67.6% for area, and 82.1% and 67.6% for volume. The corresponding AUC values were 0.701, 0.723, 0.722, and 0.811, with optimal cut-off values of 19.9 mm, 12.8 mm, 7.7 mm², and 1.07 mm³, respectively. For PZ nodules, the sensitivity and specificity were 50.7% and 90.0% for maximum diameter, 69.6% and 75.0% for mean diameter, 50.7% and 90.0% for area, and 76.8% and 85.0% for volume. The AUC values were 0.733, 0.738, 0.728, and 0.819, with optimal cut-off values of 21.0 mm, 13.2 mm, 6.4 mm², and 1.23 mm³, respectively (Fig. 2 ). Notably, AUC of nodule volume was significantly superior to that of maximum diameter for discriminating csPCa in the TZ, and significantly better than both maximum and mean diameters in the PZ (all P < 0.05) (Table 4 ). Discussion The findings of this study indicate that there was no statistically significant difference in the maximum or mean diameter of BPH and csPCa nodules across the axial, coronal, and sagittal T2WI planes (P > 0.05), suggesting that prostate nodules do not exhibit a dominant spatial growth orientation. This supports the PI-RADS v2.1 recommendation that prostate MRI protocols should include at least two orthogonal planes (axial, coronal, or sagittal) rather than mandating all three. Furthermore, the AUC for diagnosing csPCa was higher for the mean diameter than for the maximum diameter in both the transition zone (TZ: 0.738 vs. 0.733) and the peripheral zone (PZ: 0.723 vs. 0.701). This may be attributed to the variable morphological presentations of prostate nodules (e.g., irregular, brush-like, or elongated shapes), for which the mean diameter may more accurately represent the overall nodule extent. Therefore, we propose that the mean diameter should be considered as a reference for distinguishing between PI-RADS v2.1 category 4 and 5 nodules. Comparative analysis revealed that csPCa nodules in both the TZ and PZ had significantly larger maximum diameter, mean diameter, area, and volume compared to BPH nodules. This may be related to the unlimited proliferative capacity and invasive nature of prostate cancer cells [ 19 , 20 ]. In contrast, the growth of BPH nodules is self-limiting and regulated within normal physiological ranges by hormonal influences [ 21 ]. Additionally, significant differences were observed among volume, area, maximum diameter, and mean diameter in csPCa nodules (P 0.7, indicating good diagnostic performance for csPCa. Notably, nodule volume demonstrated the highest AUC in both zones (PZ: 0.819; TZ: 0.811), supporting its superior diagnostic value. This may be because maximum and mean diameters provide only one-dimensional information, while area is a two-dimensional metric—both of which have limitations in fully capturing three-dimensional morphological characteristics. Volume, as a three-dimensional parameter, reflects the nodule’s true size and spatial growth more comprehensively, further validating the importance of multiplanar MRI acquisition. Mahjoub et al. [ 22 ] compared MRI findings with biopsy results in 259 patients with suspected csPCa and concluded that evaluating the PZ and TZ separately improved diagnostic accuracy. Consistent with this, our zone-specific analysis identified optimal cut-off values for csPCa detection: in the TZ, maximum diameter 19.9 mm, mean diameter 12.8 mm, area 7.7 mm², volume 1.07 mm³; in the PZ, maximum diameter 21.0 mm, mean diameter 13.2 mm, area 6.4 mm², volume 1.23 mm³. The variation in cut-off values between zones (e.g., maximum diameter TZ: 19.9 mm vs. PZ: 21.0 mm) may be influenced by the single-center design and potential sampling bias. Future multi-institutional studies with larger sample sizes are needed to enhance the reliability of these thresholds. This study has several limitations. First, the relatively small sample size may affect the precision of our results; larger, multicenter cohorts are necessary for validation. Second, while we compared morphometric parameters, we did not correlate them with clinicopathological features such as Gleason grade or tumor stage—an important direction for future research. Third, all pathological diagnoses were based on biopsy specimens, which may have missed some cases of csPCa. In conclusion, PI-RADS v2.1 category ≥ 4 prostate nodules show no dominant orientation of growth. Nodule volume is the most discriminative parameter for differentiating between category 4 and 5 lesions, with proposed zone-specific cut-offs of 1.07 mm³ for the TZ and 1.23 mm³ for the PZ. However, considering clinical practicality, the mean diameter may also serve as a useful reference, with thresholds of 12.8 mm for the TZ and 13.2 mm for the PZ. Declarations Consent for publication All authors agreed with the content and that all gave explicit consent to submit this manuscript to Abdominal Radiology. Availability of data and material All authors make sure that all data and materials as well as software application or custom code support their published claims and comply with field standards. Competing interests All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. Funding This work was supported by grants from the Natural Science Research Projects in Higher Education Institutions in Anhui Province (Grant Number: 2023AH050584) and Natural Science Foundation of Anhui Medical University (Grant Number:2022xkj214). Ethics approval This study was approved by the Clinical Research Eth-ics Review ommittee of The Fifth Affiliated Hospital Of Anhui Medical University (approval number: KY2025074). Informed consent The institutional review board of our hospital approved the study protocol and waived the need for individual consent due to its retrospective design. Authors' contributions Conceptualization: XiaoYan Zhang Data curation: XiaoYan Zhang，Li Xu Formal analysis: Xiaoyan Zhang, MingHua Sun, Li Xu Funding acquisition: MingHua Sun Investigation: XiaoYan Zhang, Li Xu, Wenya Liu Methodology: XiaoYan Zhang Project administration: XiaoYan Zhang Resources: Xiaoyan Zhang, MingHua Sun, Wenya Liu Software: MingHua Sun Supervision: MingHua Sun Validation: MingHua Sun Visualization: Li Xu, Wenya Liu Writing-original draft: Xiaoyan Zhang Writing-review & editing: Xiaoyan Zhang, Minghua Sun Acknowledgements Not applicable. References Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A (2024) Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 74:229–263. doi: https://doi.org/10.3322/caac.21834 .Epub 2024 Apr 4. 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Tables Table 1 Protocol sequence parameters for multiparametric MRI of the prostate T1WI TSE T2WI TSE DWI DCE Imaging plane Axial Axial Coronal Sagittal Axial Axial Field of view (FOV) (mm) 350x100 200x100 220x100 220x100 220x100 350x81.3 Matrix size 352x70 320x80 320x80 320x80 100x100 70x70 Slice thickness/gap (mm) 3.0 4.0/0.8 3.0 3.0 3.0/1.0 3.0 TR/TE (ms) 533.0/8.9 3410.0/101 5000.0/108 5000.0/108 5800/78 5.47/2.46 Flip angle 110 160 110 110 9.0 Recelver bandwidth(Hz/voxel) 200 200 274 274 1724 870 Acquisition time (min) 2:05 min 2:32 min 2:20 min 2:20 min 2:43 min 2:54 min Number of signals averaged 1 3 1 1 4 b value (s/mm²) - - - - 50,800,1200,1500 - Table 2 Comparison of the maximum diameter of prostate nodules across different scanning planes P F P ¶ F ¶ Total csPCa(n=108) Axial 19.63±9.88 <0.001 16.493 0.925 0.078 Cor 19.54±9.36 <0.001 22.361 Sag 20.04±10.55 <0.001 25.305 BPH(n=57) Axial 14.85±5.61 0.426 0.859 Cor 13.85±5.05 Sag 13.68±5.06 PZ csPCa(n=69) Axial 19.49±9.74 0.001 6.062 0.924 0.079 Cor 19.45±8.64 <0.001 9.493 Sag 20.03±10.21 <0.001 8.721 BPH(n=20) Axial 13.91±4.47 0.881 0.127 Cor 13.98±4.12 Sag 13.34±4.72 TZ csPCa(n=39) Axial 19.87±10.25 0.022 15.339 0.990 0.010 Cor 19.72±10.63 0.003 20.949 Sag 20.07±11.26 0.003 23.479 BPH(n=37) Axial 15.36±6.14 0.405 0.910 Cor 13.78±5.54 Sag 13.87±5.28 csPCa：Clinically significant prostate cancer, BPH: Benign prostate hyperplasia, TZ: Transition zone. PZ: Peripheral zone. The P and F values represent the comparison results of the maximum diameter of prostate nodules in the same scan plane. The P ¶ and F ¶ values indicate the One-way ANOVA comparison results of the mean diameter for the same prostate nodule across axial, coronal, and sagittal planes. All prostate nodule measurements are expressed in millimeters. P < 0.05 indicates a statistically significant difference. Table 3 Comparison of the mean diameter of prostate nodules across different scanning planes P F P ¶ F ¶ Total csPCa(n=108) Axial 15.9±8.03 0.001 12.273 0.948 0.053 Cor 15.77±7.4 <0.001 18.07 Sag 16.12±8.51 <0.001 19.27 BPH(n=57) Axial 12.10±4.0 0.545 0.609 Cor 11.56±4.16 Sag 11.38±4.25 PZ csPCa(n=69) Axial 15.45±7.85 0.002 4.866 0.959 0.042 Cor 15.63±6.91 0.001 5.533 Sag 15.82±8.03 <0.001 6.936 BPH(n=20) Axial 11.51±3.36 0.716 0.337 Cor 11.32±3.95 Sag 10.6±3.77 TZ csPCa(n=39) Axial 16.68±8.37 0.009 22.324 0.933 0.070 Cor 16.03±8.29 0.006 22.126 Sag 16.65±9.38 0.005 24.174 BPH(n=37) Axial 12.56±4.30 0.649 0.434 Cor 11.70±4.31 Sag 11.80±4.48 csPCa：Clinically significant prostate cancer, BPH: Benign prostate hyperplasia, TZ: Transition zone. PZ: Peripheral zone. The P and F values represent the comparison results of the mean diameter of prostate nodules in the same scan plane. The P ¶ and F ¶ values indicate the One-way ANOVA comparison results of the mean diameter for the same prostate nodule across axial, coronal, and sagittal planes. All prostate nodule measurements are expressed in millimeters. P < 0.05 indicates a statistically significant difference. Table 4 Receiver Operating Characteristic curve (ROC) for diagnostic performance of maximum diameter, mean diameter, nodule area, and nodule volume in prostate cancer Sensitivity (%) Specificity (%) Cut-off value ROC P Total maximum diameter（mm） 50.0 93.0 21.0 0.728 P c mean diameter（mm） 71.3 71.9 13.15 0.737 nodule area（mm 2 ） 46.3 82.5 10.1 0.684 nodule volume（mm 3 ） 75.9 73.7 1.23 0.801 PZ maximum diameter（mm） 50.7 90.0 21.0 0.733 P e,f mean diameter（mm） 69.6 75.0 13.2 0.738 nodule area（mm 2 ） 50.7 90.0 6.4 0.728 nodule volume（mm 3 ） 76.8 85.0 1.23 0.819 TZ maximum diameter（mm） 51.28 86.49 19.9 0.701 P e mean diameter（mm） 69.23 72.97 12.8 0.723 nodule area（mm 2 ） 66.7 67.6 7.7 0.722 nodule volume（mm 3 ） 82.1 67.6 1.07 0.811 TZ: Transition zone. PZ: Peripheral zone. a, b, c, d, e, and f represent the comparison of ROC areas between: area and maximum diameter, area and mean diameter, area and volume, maximum diameter and mean diameter, maximum diameter and volume, and mean diameter and volume, respectively. P < 0.05 indicates a statistically significant difference. Additional Declarations No competing interests reported. Supplementary Files Visualabstracttemplate.pptx Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-8144341","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":551591658,"identity":"e2c1a045-17ac-45fe-b923-f6a18731de57","order_by":0,"name":"Xiaoyan Zhang","email":"","orcid":"","institution":"The Fifth Affiliated Hospital Of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyan","middleName":"","lastName":"Zhang","suffix":""},{"id":551591659,"identity":"61e543b1-ddd1-4259-8ab1-b23dc5e13ed0","order_by":1,"name":"Wenya Liu","email":"","orcid":"","institution":"The Fifth Affiliated Hospital Of Anhui 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Fifth Affiliated Hospital Of Anhui Medical University","correspondingAuthor":true,"prefix":"","firstName":"Minghua","middleName":"","lastName":"Sun","suffix":""}],"badges":[],"createdAt":"2025-11-18 10:38:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8144341/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8144341/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97142251,"identity":"635ce3bb-40bd-4395-8e39-8dec589660c1","added_by":"auto","created_at":"2025-12-01 10:07:27","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":699948,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.docx","url":"https://assets-eu.researchsquare.com/files/rs-8144341/v1/5a03dc5793c2b1a65f878cfb.docx"},{"id":97141837,"identity":"9cc3750d-6089-4700-b9da-ac851c928946","added_by":"auto","created_at":"2025-12-01 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1","display":"","copyAsset":false,"role":"figure","size":37213,"visible":true,"origin":"","legend":"\u003cp\u003ePatient enrollment flowchart for this consecutive retrospective cohort study\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8144341/v1/06d93184b2bf873d79a3ac36.png"},{"id":97142574,"identity":"588abf6c-293f-43b3-84fb-95c3146a7461","added_by":"auto","created_at":"2025-12-01 10:07:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":555732,"visible":true,"origin":"","legend":"\u003cp\u003e78-year-old, male, with a nodule in the right transition zone of the prostate. Images a (prostate axial T2WI), b (prostate coronal T2WI), and c (prostate sagittal T2WI) show that the nodule in the right transition zone of the prostate presents as hypointensity, with ill-defined margins and an incomplete capsule. d (DWI) shows the nodule as hyperintensity. Two radiologists measured the maximum diameter of the prostate nodule by combining axial and coronal images, and classified it as PI-RADS v2.1 score 4. According to the sagittal image c, the PI-RADS v2.1 score is 5. Image e (prostate biopsy histopathology) confirms prostatic acinar cell carcinoma\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8144341/v1/075508f0e9d10a23c53e3d46.png"},{"id":97142433,"identity":"b92cdabc-1d61-4743-8961-c74751f58d2f","added_by":"auto","created_at":"2025-12-01 10:07:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":582343,"visible":true,"origin":"","legend":"\u003cp\u003e73-year-old, male, with a nodule in the right peripheral zone of the prostate. Images a (prostate axial T2WI), b (prostate coronal T2WI), and c (prostate sagittal T2WI) show that the nodule in the right peripheral zone of the prostate presents as hypointensity, with relatively clear margins but an ill-defined capsule. Image d (DWI) shows the nodule as markedly hyperintense. Two radiologists measured the maximum diameter of the prostate nodule by combining axial and sagittal images, and classified it as PI-RADS v2.1 score 5. According to the coronal image b, the PI-RADS v2.1 score is 4. Image e (prostate biopsy histopathology) confirms prostatic acinar cell carcinoma\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8144341/v1/3877dee64faba41852cca710.png"},{"id":97126018,"identity":"7f9fae71-4db2-436e-8b69-67c7d21a969f","added_by":"auto","created_at":"2025-12-01 08:22:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":75224,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curves comparing diagnostic performance for clinically significant prostate cancer (csPCa) detection using four nodule parameters: (a) whole prostate, (b) transitional zone, and (c) peripheral zone. For each anatomical region, the following parameters were evaluated: maximum diameter, mean diameter, area, and volume\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8144341/v1/a97da7bb26cc2b3d760fccef.png"},{"id":97673976,"identity":"9b3b4070-beeb-4488-b93f-88e232394be6","added_by":"auto","created_at":"2025-12-08 09:42:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2265886,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8144341/v1/61c81109-0ded-47da-a8d4-1dd8009e4d62.pdf"},{"id":97126022,"identity":"b587766b-7c13-4c83-a6bc-82a6775d1597","added_by":"auto","created_at":"2025-12-01 08:22:49","extension":"pptx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1292202,"visible":true,"origin":"","legend":"","description":"","filename":"Visualabstracttemplate.pptx","url":"https://assets-eu.researchsquare.com/files/rs-8144341/v1/3908086d81156ea1ee35291f.pptx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictive Value of Quantitative MRI Parameters for Clinically Significant Prostate Cancer: A Comparison Between PI-RADS v2.1 Category 4 and 5 Lesions on Biparametric MRI","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn recent years, the incidence of prostate cancer (PCa) has been gradually increasing, accompanied by a relatively high mortality [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Effective screening methods and early appropriate management are therefore crucial [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1) is a semi-quantitative assessment tool based on the morphological features and signal intensity of prostate nodules on multiparametric MRI (mpMRI) [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Its accuracy in detecting clinically significant prostate cancer (csPCa) has been widely validated in clinical practice [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] and is recognized as an effective evaluation method [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, the consistency of its accuracy for csPCa grading via targeted biopsy varies across studies [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and false negatives remain a concern [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA study by Yilmaz EC et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] reported csPCa detection rates of 0%, 9%, 14%, 37%, and 77% for PI-RADS scores 1 to 5, respectively. Wen J et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] conducted a study involving 266 patients with suspected PCa and found that the area under the receiver operating characteristic (ROC) curve (AUC) of PI-RADS v2.1 for diagnosing csPCa was 0.85 (95% CI: 0.78\u0026ndash;0.93, P\u0026thinsp;=\u0026thinsp;0.031), which was lower than that of PI-RADS v2.1 combined with prostate-specific antigen density (PSAD)(AUC\u0026thinsp;=\u0026thinsp;0.90, 95% CI: 0.83\u0026ndash;0.96). In another study, van Riel LAMJG et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] performed biopsies on 232 patients with negative multiparametric MRI results and detected csPCa in 18.1% of cases.\u003c/p\u003e\u003cp\u003eImproving the diagnostic performance of PI-RADS for prostate lesions has become a major research focus. PI-RADS v2.1 uses identical morphological criteria for categories 4 and 5 lesions, with differentiation primarily based on whether the longest diameter exceeds 1.5 cm or if extraprostatic extension. Prostate nodules exhibit diverse morphologies, and it remains unclear whether they demonstrate consistent three-dimensional growth patterns. This uncertainty may influence the selection of MRI scanning planes and the measurement of nodule longest diameter, thereby affecting the accurate PI-RADS v2.1 assessment.\u003c/p\u003e\u003cp\u003ePI-RADS v2.1 recommends that T2-weighted imaging(T2WI) include at least two orthogonal planes, with the axial plane being mandatory. However, it is still unknown whether adding a second optional plane or acquiring three planes simultaneously could improve the accuracy of csPCa detection using PI-RADS v2.1. Furthermore, the potential of quantitative parameters such as total cross-sectional area (sum of areas across all imaging planes) and volume to better differentiate between PI-RADS category 4 and 5 nodules requires further investigation.\u003c/p\u003e\u003cp\u003eThis study aims to explore the influence of imaging plane selection for longest diameter measurement, as well as the utility of cross-sectional area and volume measurements, in refining the distinction between PI-RADS 4 and 5 nodules, with the goal of enhancing the accuracy of csPCa detection using PI-RADS v2.1.\u003c/p\u003e"},{"header":"​Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003ePatients\u003c/h2\u003e\n \u003cp\u003eThis retrospective study was approved by the local Research Ethics Committee at the XXX (Approval Code: XXX). Written informed consent was obtained from all participants.\u003c/p\u003e\n \u003cp\u003eWe initially enrolled 376 patients who presented with either a suspicious digital rectal examination (DRE) finding or a prostate-specific antigen (PSA) level\u0026thinsp;\u0026gt;\u0026thinsp;10 ng/mL and subsequently underwent multiparametric magnetic resonance imaging (mpMRI) according to the PI-RADS v2.1 protocol at our institution between 2019 and 2024.\u003c/p\u003e\n \u003cp\u003eAmong them, 223 patients were excluded for the following reasons: prior radiotherapy before MRI (n\u0026thinsp;=\u0026thinsp;35), poor image quality insufficient for PI-RADS v2.1 assessment (n\u0026thinsp;=\u0026thinsp;27), or a PI-RADS v2.1 score of less than 4(n\u0026thinsp;=\u0026thinsp;161), as detailed in the patient selection flowchart (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Consequently, a final cohort of 153 patients was included in the analysis. Pathological confirmation for all included cases was obtained via ultrasound-guided transrectal biopsy.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eMRI technique and image analysis\u003c/h3\u003e\n\u003cp\u003eAll mpMRI examinations were conducted on a 3.0 T MRI scanner (Vida, Siemens Healthineers) using a combination of a 4- to 16-channel pelvic surface coil and an 8- to 12-channel spine coil. The imaging protocol comprised the following sequences: axial T1-weighted imaging (T1WI), axial T2WI, coronal and sagittal T2WI, diffusion-weighted imaging (DWI) with b-values of 50, 800, 1200, and 1500 s/mm\u0026sup2;, and axial dynamic contrast-enhanced (DCE) imaging. Detailed acquisition parameters are provided in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eThe PI-RADS v2.1 scoring of prostate nodules was independently performed by two genitourinary radiologists trained in PI-RADS v2.1, who were blinded to the pathological results. Any discrepancies in scoring were resolved through consensus discussion to determine the final category.\u003c/p\u003e\n\u003cp\u003eThe maximum diameter, mean diameter, area, and volume of each prostate nodule were measured on T2WI. For every nodule, the longest diameter was measured separately on axial, coronal, and sagittal planes, from which the average diameter was calculated. The mean diameter of a prostate nodule in a given scanning plane was defined as the average of its longest diameter and the perpendicular diameter in that same plane. The total nodule area was calculated as the sum of the areas of the nodule across all axial slices where it was visible. Nodule volume was estimated using the ellipsoid formula: ML (cm) \u0026times; AP (cm) \u0026times; CC (cm) \u0026times; \u0026pi;/6 [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eUltrasound-guided transrectal prostate biopsy was performed by a urologist with over 10 years of clinical experience, who had performed more than 100 targeted prostate biopsy procedures within the past five years. The procedure utilized real-time multiparametric MRI-ultrasound fusion guidance (systems: Toshiba Aplio 500 or Hitachi HI VISION Preirus). The biopsy protocol included both targeted biopsies of MRI-suspicious lesions and a systematic 12-core\u0026thinsp;+\u0026thinsp;X extended template biopsy.\u003c/p\u003e\n\u003cp\u003eAll obtained specimens were labeled according to the biopsy sequence number and prostate zone, and placed in individual containers. Pathological reporting documented complete sampling information. The final diagnosis for each lesion was confirmed by a consensus review conducted by two certified genitourinary radiologists and one uropathologist.\u003c/p\u003e\n\u003ch3\u003eStatistical Analysis\u003c/h3\u003e\n\u003cp\u003eStatistical analyses were performed using SPSS software (version 23.0; IBM Corp.) and MedCalc Statistical Software (version [specify if known]; MedCalc Software Ltd.). The intraclass correlation coefficient (ICC) was used to evaluate the interobserver agreement between the two radiologists for quantitative measurements. ICC values were interpreted as follows: \u0026gt;0.75 indicated excellent agreement, 0.40\u0026ndash;0.75 indicated moderate agreement, and \u0026lt;\u0026thinsp;0.40 indicated poor agreement.\u003c/p\u003e\n\u003cp\u003eComparisons between groups were conducted using the independent samples t-test for normally distributed data or the Mann-Whitney U test for non-normally distributed data. Continuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD). ROC curves were generated using MedCalc software to assess the diagnostic performance of various parameters. The optimal cut-off values were determined by maximizing the Youden\u0026rsquo;s index. The AUC was calculated for each parameter, and a two-tailed p-value of less than 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 165 prostate nodules were identified in 153 patients (97 with csPCa and 56 with BPH). Among these nodules, 108 were pathologically confirmed as csPCa (69 in the peripheral zone (PZ) and 39 in the transition zone (TZ)), and 57 were non-csPCa (20 in the PZ and 37 in the TZ). No significant difference in age was observed between patients with csPCa (74.28\u0026thinsp;\u0026plusmn;\u0026thinsp;7.78 years) and those with benign prostatic hyperplasia (BPH) (71.93\u0026thinsp;\u0026plusmn;\u0026thinsp;8.80 years) (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, both total PSA (tPSA) and PSA density (PSAD) were significantly higher in the csPCa group compared to the BPH group (tPSA: 91.51\u0026thinsp;\u0026plusmn;\u0026thinsp;233.27 ng/mL vs. 27.32\u0026thinsp;\u0026plusmn;\u0026thinsp;33.05 ng/mL; PSAD: 2.43\u0026thinsp;\u0026plusmn;\u0026thinsp;6.83 vs. 0.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45; all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003cp\u003eInterobserver agreement between the two radiologists was excellent for all quantitative measurements, with ICC as follows: maximum diameter, 0.990 (95% CI: 0.986\u0026ndash;0.993); mean diameter, 0.962 (95% CI: 0.948\u0026ndash;0.972); area, 0.899 (95% CI: 0.865\u0026ndash;0.925); and volume, 0.847 (95% CI: 0.798\u0026ndash;0.885).\u003c/p\u003e\n\u003cp\u003eIn both the TZ and PZ, the maximum and mean diameters of csPCa nodules measured on axial, coronal, and sagittal planes were significantly larger than those of BPH nodules (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). For the same nodule (across all zones), no significant differences were found in the maximum or mean diameters measured among the three orthogonal planes (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eBoth the area and volume of csPCa nodules were significantly greater than those of BPH nodules in both the TZ and PZ (TZ area: 19.01\u0026thinsp;\u0026plusmn;\u0026thinsp;18.26 mm\u0026sup2; vs. 8.18\u0026thinsp;\u0026plusmn;\u0026thinsp;8.58 mm\u0026sup2;, volume: 5.89\u0026thinsp;\u0026plusmn;\u0026thinsp;5.86 mm\u0026sup3; vs. 1.37\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29 mm\u0026sup3;; PZ area: 16.90\u0026thinsp;\u0026plusmn;\u0026thinsp;22.09 mm\u0026sup2; vs. 3.48\u0026thinsp;\u0026plusmn;\u0026thinsp;4.12 mm\u0026sup2;, volume: 5.42\u0026thinsp;\u0026plusmn;\u0026thinsp;12.23 mm\u0026sup3; vs. 1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13 mm\u0026sup3;; all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003cp\u003eAll measured parameters (maximum diameter, mean diameter, area, and volume) demonstrated good diagnostic performance for csPCa in both zones. For TZ nodules, the sensitivity and specificity were 51.28% and 86.49% for maximum diameter, 69.23% and 72.97% for mean diameter, 66.7% and 67.6% for area, and 82.1% and 67.6% for volume. The corresponding AUC values were 0.701, 0.723, 0.722, and 0.811, with optimal cut-off values of 19.9 mm, 12.8 mm, 7.7 mm\u0026sup2;, and 1.07 mm\u0026sup3;, respectively. For PZ nodules, the sensitivity and specificity were 50.7% and 90.0% for maximum diameter, 69.6% and 75.0% for mean diameter, 50.7% and 90.0% for area, and 76.8% and 85.0% for volume. The AUC values were 0.733, 0.738, 0.728, and 0.819, with optimal cut-off values of 21.0 mm, 13.2 mm, 6.4 mm\u0026sup2;, and 1.23 mm\u0026sup3;, respectively (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eNotably, AUC of nodule volume was significantly superior to that of maximum diameter for discriminating csPCa in the TZ, and significantly better than both maximum and mean diameters in the PZ (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings of this study indicate that there was no statistically significant difference in the maximum or mean diameter of BPH and csPCa nodules across the axial, coronal, and sagittal T2WI planes (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting that prostate nodules do not exhibit a dominant spatial growth orientation. This supports the PI-RADS v2.1 recommendation that prostate MRI protocols should include at least two orthogonal planes (axial, coronal, or sagittal) rather than mandating all three.\u003c/p\u003e\u003cp\u003eFurthermore, the AUC for diagnosing csPCa was higher for the mean diameter than for the maximum diameter in both the transition zone (TZ: 0.738 vs. 0.733) and the peripheral zone (PZ: 0.723 vs. 0.701). This may be attributed to the variable morphological presentations of prostate nodules (e.g., irregular, brush-like, or elongated shapes), for which the mean diameter may more accurately represent the overall nodule extent. Therefore, we propose that the mean diameter should be considered as a reference for distinguishing between PI-RADS v2.1 category 4 and 5 nodules.\u003c/p\u003e\u003cp\u003eComparative analysis revealed that csPCa nodules in both the TZ and PZ had significantly larger maximum diameter, mean diameter, area, and volume compared to BPH nodules. This may be related to the unlimited proliferative capacity and invasive nature of prostate cancer cells [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In contrast, the growth of BPH nodules is self-limiting and regulated within normal physiological ranges by hormonal influences [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAdditionally, significant differences were observed among volume, area, maximum diameter, and mean diameter in csPCa nodules (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). All parameters yielded AUCs\u0026thinsp;\u0026gt;\u0026thinsp;0.7, indicating good diagnostic performance for csPCa. Notably, nodule volume demonstrated the highest AUC in both zones (PZ: 0.819; TZ: 0.811), supporting its superior diagnostic value. This may be because maximum and mean diameters provide only one-dimensional information, while area is a two-dimensional metric\u0026mdash;both of which have limitations in fully capturing three-dimensional morphological characteristics. Volume, as a three-dimensional parameter, reflects the nodule\u0026rsquo;s true size and spatial growth more comprehensively, further validating the importance of multiplanar MRI acquisition.\u003c/p\u003e\u003cp\u003eMahjoub et al. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] compared MRI findings with biopsy results in 259 patients with suspected csPCa and concluded that evaluating the PZ and TZ separately improved diagnostic accuracy. Consistent with this, our zone-specific analysis identified optimal cut-off values for csPCa detection: in the TZ, maximum diameter 19.9 mm, mean diameter 12.8 mm, area 7.7 mm\u0026sup2;, volume 1.07 mm\u0026sup3;; in the PZ, maximum diameter 21.0 mm, mean diameter 13.2 mm, area 6.4 mm\u0026sup2;, volume 1.23 mm\u0026sup3;. The variation in cut-off values between zones (e.g., maximum diameter TZ: 19.9 mm vs. PZ: 21.0 mm) may be influenced by the single-center design and potential sampling bias. Future multi-institutional studies with larger sample sizes are needed to enhance the reliability of these thresholds.\u003c/p\u003e\u003cp\u003eThis study has several limitations. First, the relatively small sample size may affect the precision of our results; larger, multicenter cohorts are necessary for validation. Second, while we compared morphometric parameters, we did not correlate them with clinicopathological features such as Gleason grade or tumor stage\u0026mdash;an important direction for future research. Third, all pathological diagnoses were based on biopsy specimens, which may have missed some cases of csPCa.\u003c/p\u003e\u003cp\u003eIn conclusion, PI-RADS v2.1 category\u0026thinsp;\u0026ge;\u0026thinsp;4 prostate nodules show no dominant orientation of growth. Nodule volume is the most discriminative parameter for differentiating between category 4 and 5 lesions, with proposed zone-specific cut-offs of 1.07 mm\u0026sup3; for the TZ and 1.23 mm\u0026sup3; for the PZ. However, considering clinical practicality, the mean diameter may also serve as a useful reference, with thresholds of 12.8 mm for the TZ and 13.2 mm for the PZ.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors agreed with the content and that all gave explicit consent to submit this manuscript to Abdominal Radiology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors make sure that all data and materials as well as software application or custom code support their published claims and comply with field standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the Natural Science Research Projects in Higher Education Institutions in Anhui Province (Grant Number: 2023AH050584)\u0026nbsp;and\u0026nbsp;Natural Science Foundation of Anhui Medical University (Grant Number:2022xkj214).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Clinical Research Eth-ics Review ommittee of The Fifth Affiliated Hospital Of Anhui Medical University (approval number: KY2025074).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe institutional review board of our hospital approved the study protocol and waived the need for individual consent due to its retrospective design.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: XiaoYan Zhang\u003c/p\u003e\n\u003cp\u003eData curation:\u0026nbsp;XiaoYan Zhang，Li Xu\u003c/p\u003e\n\u003cp\u003eFormal analysis: Xiaoyan Zhang, MingHua Sun,\u0026nbsp;Li Xu\u003c/p\u003e\n\u003cp\u003eFunding acquisition: MingHua Sun\u003c/p\u003e\n\u003cp\u003eInvestigation: XiaoYan Zhang, Li Xu, Wenya Liu\u003c/p\u003e\n\u003cp\u003eMethodology: XiaoYan Zhang\u003c/p\u003e\n\u003cp\u003eProject administration: XiaoYan Zhang\u003c/p\u003e\n\u003cp\u003eResources: Xiaoyan Zhang, MingHua Sun, Wenya Liu\u003c/p\u003e\n\u003cp\u003eSoftware: MingHua Sun\u003c/p\u003e\n\u003cp\u003eSupervision: MingHua Sun\u003c/p\u003e\n\u003cp\u003eValidation: MingHua Sun\u003c/p\u003e\n\u003cp\u003eVisualization: Li Xu, Wenya Liu\u003c/p\u003e\n\u003cp\u003eWriting-original draft: Xiaoyan Zhang\u003c/p\u003e\n\u003cp\u003eWriting-review \u0026amp; editing: Xiaoyan Zhang, Minghua Sun\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A (2024) Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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Epub 2020 May 17.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003eProtocol sequence parameters for multiparametric MRI of the prostate\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"567\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eT1WI TSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eT2WI TSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDWI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDCE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eImaging plane\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAxial\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAxial\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoronal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSagittal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAxial\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAxial\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eField of view (FOV) (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e350x100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e200x100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e220x100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e220x100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e220x100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e350x81.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMatrix size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e352x70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e320x80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e320x80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e320x80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e100x100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e70x70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSlice thickness/gap (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.0/0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.0/1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTR/TE (ms)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e533.0/8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3410.0/101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5000.0/108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5000.0/108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5800/78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.47/2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFlip angle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRecelver bandwidth(Hz/voxel)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e870\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAcquisition time (min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2:05 min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2:32 min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2:20 min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2:20 min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2:43 min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2:54 min\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNumber of signals averaged\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eb value (s/mm\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50,800,1200,1500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003eComparison of the maximum diameter of prostate nodules across different scanning planes\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"567\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003eP\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003eF\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003ecsPCa(n=108)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eAxial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e19.63\u0026plusmn;9.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e16.493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eCor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e19.54\u0026plusmn;9.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e22.361\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eSag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e20.04\u0026plusmn;10.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e25.305\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eBPH(n=57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eAxial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e14.85\u0026plusmn;5.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.859\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eCor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e13.85\u0026plusmn;5.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eSag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e13.68\u0026plusmn;5.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003ePZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003ecsPCa(n=69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eAxial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e19.49\u0026plusmn;9.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e6.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eCor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e19.45\u0026plusmn;8.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e9.493\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eSag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e20.03\u0026plusmn;10.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e8.721\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eBPH(n=20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eAxial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e13.91\u0026plusmn;4.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eCor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e13.98\u0026plusmn;4.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eSag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e13.34\u0026plusmn;4.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eTZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003ecsPCa(n=39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eAxial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e19.87\u0026plusmn;10.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e15.339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eCor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e19.72\u0026plusmn;10.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e20.949\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eSag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e20.07\u0026plusmn;11.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e23.479\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eBPH(n=37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eAxial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e15.36\u0026plusmn;6.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.910\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eCor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e13.78\u0026plusmn;5.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eSag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e13.87\u0026plusmn;5.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ecsPCa：Clinically significant prostate cancer, BPH: Benign prostate hyperplasia, TZ: Transition zone. PZ: Peripheral zone. The P and F values represent the comparison results of the maximum diameter of prostate nodules in the same scan plane. The P\u003csup\u003e\u0026para;\u003c/sup\u003e and F\u003csup\u003e\u0026para;\u003c/sup\u003e values indicate the One-way ANOVA comparison results of the mean diameter for the same prostate nodule across axial, coronal, and sagittal planes. All prostate nodule measurements are expressed in millimeters. P \u0026lt; 0.05 indicates a statistically significant difference.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eComparison of the mean diameter of prostate nodules across different scanning planes\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"567\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003eP\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003eF\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003ecsPCa(n=108)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eAxial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e15.9\u0026plusmn;8.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e12.273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eCor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e15.77\u0026plusmn;7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e18.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eSag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e16.12\u0026plusmn;8.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e19.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eBPH(n=57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eAxial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e12.10\u0026plusmn;4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eCor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e11.56\u0026plusmn;4.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eSag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e11.38\u0026plusmn;4.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003ePZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003ecsPCa(n=69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eAxial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e15.45\u0026plusmn;7.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e4.866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eCor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e15.63\u0026plusmn;6.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e5.533\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eSag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e15.82\u0026plusmn;8.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e6.936\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eBPH(n=20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eAxial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e11.51\u0026plusmn;3.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.337\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eCor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e11.32\u0026plusmn;3.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eSag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e10.6\u0026plusmn;3.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eTZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003ecsPCa(n=39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eAxial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e16.68\u0026plusmn;8.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e22.324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eCor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e16.03\u0026plusmn;8.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e22.126\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eSag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e16.65\u0026plusmn;9.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e24.174\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eBPH(n=37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eAxial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e12.56\u0026plusmn;4.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.434\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eCor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e11.70\u0026plusmn;4.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eSag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e11.80\u0026plusmn;4.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ecsPCa：Clinically significant prostate cancer, BPH: Benign prostate hyperplasia, TZ: Transition zone. PZ: Peripheral zone. The P and F values represent the comparison results of the mean diameter of prostate nodules in the same scan plane. The P\u003csup\u003e\u0026para;\u003c/sup\u003e and F\u003csup\u003e\u0026para;\u003c/sup\u003e values indicate the One-way ANOVA comparison results of the mean diameter for the same prostate nodule across axial, coronal, and sagittal planes. All prostate nodule measurements are expressed in millimeters. P \u0026lt; 0.05 indicates a statistically significant difference.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e Receiver Operating Characteristic curve (ROC) for diagnostic performance of maximum diameter, mean diameter, nodule area, and nodule volume in prostate cancer\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"567\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 7.4074%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.7478%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eSensitivity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003eSpecificity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eCut-off value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 7.4074%;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.7478%;\"\u003e\n \u003cp\u003emaximum diameter（mm）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e93.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e21.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eP\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.7478%;\"\u003e\n \u003cp\u003emean diameter（mm）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e71.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e71.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e13.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.737\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.7478%;\"\u003e\n \u003cp\u003enodule area（mm\u003csup\u003e2\u003c/sup\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e46.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e82.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e10.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.684\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.7478%;\"\u003e\n \u003cp\u003enodule volume（mm\u003csup\u003e3\u003c/sup\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e75.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e73.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.801\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 7.4074%;\"\u003e\n \u003cp\u003ePZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.7478%;\"\u003e\n \u003cp\u003emaximum diameter（mm）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e50.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e90.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e21.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eP\u003csup\u003ee,f\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.7478%;\"\u003e\n \u003cp\u003emean diameter（mm）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e69.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e75.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e13.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.738\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.7478%;\"\u003e\n \u003cp\u003enodule area（mm\u003csup\u003e2\u003c/sup\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e50.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e90.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.728\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.7478%;\"\u003e\n \u003cp\u003enodule volume（mm\u003csup\u003e3\u003c/sup\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e76.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e85.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.819\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 7.4074%;\"\u003e\n \u003cp\u003eTZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.7478%;\"\u003e\n \u003cp\u003emaximum diameter（mm）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e51.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e86.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e19.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eP\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.7478%;\"\u003e\n \u003cp\u003emean diameter（mm）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e69.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e72.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e12.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.723\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.7478%;\"\u003e\n \u003cp\u003enodule area（mm\u003csup\u003e2\u003c/sup\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e66.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e67.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.722\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.7478%;\"\u003e\n \u003cp\u003enodule volume（mm\u003csup\u003e3\u003c/sup\u003e）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e82.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e67.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.811\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTZ: Transition zone. PZ: Peripheral zone. a, b, c, d, e, and f represent the comparison of ROC areas between: area and maximum diameter, area and mean diameter, area and volume, maximum diameter and mean diameter, maximum diameter and volume, and mean diameter and volume, respectively. P \u0026lt; 0.05 indicates a statistically significant difference.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Prostate cancer, PI-RADS v2.1, Volume, MRI","lastPublishedDoi":"10.21203/rs.3.rs-8144341/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8144341/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003eEvaluating the role of quantitative MRI parameters in differentiating PI-RADS v2.1 category 4 and 5 lesions for improved detection of clinically significant prostate cancer(csPCa).​\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eIn a retrospective analysis of 153 biopsy-proven patients with suspected csPCa undergoing PI-RADS v2.1 MRI, two radiologists independently measured lesion dimensions (maximum diameter, mean diameter, area, volume) on tri-planar T2-weighted images(T2WI). Metric differences were assessed between csPCa and benign prostatic hyperplasia (BPH), and across prostatic zones. For PI-RADS\u0026thinsp;\u0026ge;\u0026thinsp;4 lesions, the diagnostic performance of axial plane measurements was compared using Receiver operating characteristic (ROC)analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eA total of 165 nodules from 153 patients were analyzed, comprising 108 csPCa (69 in the peripheral zone (PZ), 39 in the transition zone (TZ)) and 57 non-csPCa (20 PZ, 37 TZ). csPCa nodules exhibited significantly larger maximum and mean diameters than BPH nodules across all imaging planes (axial, coronal, sagittal; all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with no significant differences in these diameters among the planes themselves. Similarly, csPCa nodules demonstrated greater area and volume than BPH nodules in both the TZ and PZ. For diagnosing csPCa, the respective Area Under the Curves (AUCs)for maximum diameter, mean diameter, area, and volume were approximately 0.701, 0.723, 0.722, and 0.811 in the TZ, and 0.733, 0.738, 0.722, and 0.819 in the PZ.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eIn PI-RADS v2.1 category\u0026thinsp;\u0026ge;\u0026thinsp;4 nodules, which exhibited no dominant growth orientation, volume outperformed diameter and area metrics for diagnosing csPCa, with proposed thresholds of 1.07 mm\u0026sup3; and 1.23 mm\u0026sup3; for differentiating categories 4 and 5, respectively.\u003c/p\u003e","manuscriptTitle":"Predictive Value of Quantitative MRI Parameters for Clinically Significant Prostate Cancer: A Comparison Between PI-RADS v2.1 Category 4 and 5 Lesions on Biparametric MRI","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-01 08:22:44","doi":"10.21203/rs.3.rs-8144341/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5419d513-8d7b-42f0-8b39-cf039a48fb72","owner":[],"postedDate":"December 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-06T21:53:07+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-01 08:22:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8144341","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8144341","identity":"rs-8144341","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}