Combination of PI-RADS version 2.1 and amide proton transfer values for the detection of clinically significant prostate cancer

Combination of PI-RADS version 2.1 and amide proton transfer values for the detection of clinically significant prostate cancer | 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 Combination of PI-RADS version 2.1 and amide proton transfer values for the detection of clinically significant prostate cancer Li Zhang, Longchao Li, Xia Zhe, Min Tang, Xiaoyan Lei, Jing Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4168033/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Jul, 2025 Read the published version in BMC Cancer → Version 1 posted 11 You are reading this latest preprint version Abstract Background The goal of this study was to assess whether combining amide proton transfer (APT)-weighted MRI with the Prostate Imaging Reporting and Data System scoring system version 2.1 (PI-RADS V2.1) could increase diagnostic accuracy compared to PI-RADS V2.1 alone in predicting clinically significant prostate cancer (csPCa). Methods The present study retrospectively analyzed data from patients who underwent prostate magnetic resonance imaging(MRI) examinations from July 2022 to August 2023. All patients underwent T2-weighted imaging (T2WI), amide proton transfer (APT), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) MRI. Two radiologists independently examined the images. The independent samples t test or the Wilcoxon rank sum test was employed to investigate the statistical variance in the demographic and APT parameters of the two groups. We utilized receiver operating characteristic (ROC) curve analysis to assess the diagnostic accuracy of PI-RADS V2.1 and the combination model (APT-weighted signal values and PI-RADS V2.1). The comparison of the area under the curve (AUC)s were conducted using the Delong method. Results A total of 289 patients were eventually included in this study; 102 had csPCa, and 187 had either benign lesions or clinically insignificant prostate cancer (cisPCa). The APTmean, APTmax, and APTmin values were significantly different between the two groups in both the peripheral zone (PZ) and transition zone (TZ). The combined models were significantly more effective than the use of PI-RADS V2.1 alone for the whole gland and PZ, with areas under the curve (AUC)s of 0.874–0.883 compared to 0.803 and 0.885 compared to 0.798, respectively ( P < 0.05). However, there was no substantial improvement in diagnostic accuracy when APT-weighted signal values were incorporated into PI-RADS V2.1 for the TZ, as the AUC increased from 0.791 to 0.865, with a P value of 0.202. Conclusion By incorporating APT-weighted signal values with PI-RADS V2.1, there was a notable improvement in the diagnostic accuracy of csPCa detection in both the whole gland and the PZ compared to PI-RADS V2.1 alone. However, there was no significant enhancement in terms of csPCa in TZ. Amide proton transfer-weighted MR image Prostate-specific antigen Clinically significant prostate cancer PI-RADS V2.1 Benign lesions Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Prostate cancer (PCa) is the most common cancer among men in the United States and the second most prevalent malignancy in men globally, affecting approximately 375,000 men each year and being a leading cause of cancer-related deaths[ 1 – 3 ]. However, not all individuals with PCa require curative treatment[ 4 ]. Precise identification of clinically significant prostate cancer (CsPCa) is crucial for preventing under- or overtreatment. CsPCa can be characterized by International Society of Urological Pathology (ISUP) grade > 1 (Gleason score 3 + 3 = 6) cancer with a volume exceeding 0.5 mL[ 5 ]. Currently, multiparametric magnetic resonance imaging (mpMRI) is considered an effective noninvasive approach for identifying, staging, and managing csPCa[ 6 ]. The Prostate Imaging Reporting and Data System(PI-RADS) has been established to standardize diagnostic criteria for mpMRI, providing consistent and precise definitions for interpreting prostate MRI findings[ 7 ]. To address the limitations of earlier versions, PI-RADS was updated to version 2.1 (V2.1) in 2019. This updated system utilizes a 5-point scale to assess the probability of csPCa based on mpMRI results from axial T2-weighted imaging(T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) maps[ 7 ]. However, prior studies have shown that the specificity of PI-RADS V2.1 can still improve to reduce the number of unnecessary biopsies [ 8 – 9 ]. Lesions categorized as PI-RADS V2.1 category 3 are typically considered inconclusive or uncertain [ 10 , 11 ]. Moreover, the absence of quantitative metrics in PI-RADS V2.1 has been noted [ 12 ]. The PI-RADS Steering Committee strongly advocates for the exploration of new MRI techniques using innovative research tools that are not covered in PI-RADS V2.1 for assessing csPCa. The committee will consider incorporating relevant data and experiences into future iterations of PI-RADS [ 7 ]. Hence, there is a call for new quantitative imaging markers to enhance the characterization and visualization of csPCa. The use of APT-weighted MRI relies on the concept that tumor cells, which are driven by high protein synthesis and proliferation, exhibit different protein levels in benign and malignant lesions [ 13 – 14 ]. Therefore, quantitatively analyzing APT-weighted MRI could lead to the use of APT values as an imaging biomarker that accurately reflects molecular changes. Some studies have demonstrated the utility of prostate APT-weighted MRI for detecting PCa [ 15 – 18 ]. However, these studies had small sample sizes, did not fully differentiate between clinically insignificant prostate cancer (cisPCa) or benign lesions and csPCa; failed to specifically address peripheral zone (PZ) csPCa; moreover, they did not utilize a combination study with PI-RADS V2.1. Currently, lesions with a PI-RADS score of 3, indicating an uncertain likelihood of csPCa, pose a significant clinical challenge. Approximately 20%-30% of these lesions are PCa, and deciding whether to proceed with a biopsy in these cases is complex[ 19 ]. In the realm of prostate MRI, quantitative methods have become increasingly favored. Notably, there is a lack of research on the utility of APT-weighted MRI for evaluating PI-RADS 3 lesions. Consequently, this research sought to assess whether combining APT-weighted MRI with the PI-RADS V2.1 could increase diagnostic accuracy compared to PI-RADS V2.1 alone in predicting csPCa. Furthermore, we assessed the additional benefit of APT-weighted MRI in predicting csPCa in PI-RADS 3 lesions. Materials and Methods Study population The Ethics Committee of Shaanxi Provincial People's Hospital approved this study in accordance with the Declaration of Helsinki. And all subjects signed an informed consent form before the examination. From July 2022 to August 2023, consecutive patients underwent preoperative prostate mpMRI, and either prostate biopsy or radical individuals who had undergone a prostatectomy were enlisted. The inclusion criteria were as follows: Patients who underwent mpMRI scans, which included T2WI, APT-weighted imaging, DWI, and DCE. Patients who underwent a biopsy and/or radical prostatectomy within one month following mpMRI. The exclusion criteria for patients were as follows: First, patients had previously undergone prostate surgery or had previously received endocrine therapy. Second, participants were found to have poor MRI quality, as noted by two radiologists. Additionally, individuals with acute prostatitis or prostatic abscess were excluded from the study. MRI Parameters An INGENIA 3.0 T MRI scanner (Philips Healthcare Co., Ltd., Best, The Netherlands) equipped with a 16-channel phased-array body coil was utilized for prostate MRI scans of all patients. The scan sequences can be found in Table 1 . Table 1 displays the sequences obtained from multiparametric MRI scans. Axial DWI was performed using b values of 50, 1000, and 2000 sec/mm 2 . The scanner automatically generated apparent diffusion coefficient (ADC) maps from the DWI data. To reduce or eliminate motion artifacts, four regional saturation technique slabs were used during APT scanning to suppress the MRI signal from moving tissues beyond the scanned region. Two slabs were placed over the rectum and bladder to mitigate motion artifacts, while the remaining two were positioned over the left and right iliac crests to improve the uniformity of the B1 field. Adjustments to the slab angulation, center, and width were based on the patient's size(Figure S1 ). Figure S1 Indication of REST slabs that were used during the amide proton transfer sequence scanning. (a) REST slabs in the sagittal section. (b) REST slabs in the coronal section. (c) REST slabs in the transversal section. REST, regional saturation technique; APT, amide proton transfer. The only parameter in the APT model is the asymmetric magnetization transfer rate at 3.5 ppm [MTRasym (3.5 ppm)], which is determined using the following equation: MTRasym (3.5 ppm) is expressed as the difference between the signal intensity at + 3.5 ppm and − 3.5 ppm divided by the signal intensity without the saturation pulse (S0). Here, S0 represents the baseline signal intensity, while Ssat denotes the signal intensity following the application of the saturation pulse. MTRasym (3.5 ppm) refers to the asymmetry in the magnetization transfer ratio at 3.5 ppm. Image analysis The APT-weighted image was aligned with the T2WI using the postprocessing workstation "IntelliSpace Portal" version 8 developed by Philips Healthcare in the Netherlands. Two radiologists specializing in fellowship training (Z.L., 8 years of experience; L.L.C., 3 years of experience) independently evaluated all prostate MRI, including APT-weighted signal value results. The patients were kept unaware of clinical and pathological information to prevent bias. The assessments of PI-RADS V2.1 and APT data were conducted separately with a one-month gap to minimize recall bias. Every lesion was evaluated according to the PI-RADS V2.1 criteria using DWI, DCE, and T2WI sequences, and the highest total PI-RADS score from each mpMRI scan was selected. The highest lesion level was identified using T2WI as a reference. APT-weighted signal values were subsequently assessed by choosing a region of interest (ROI) at the corresponding level on the APT images. To prevent any volumetric bias, the ROIs were positioned away from the periphery of the lesion's solid components. It is important to steer clear of placing ROIs close to areas showing cystic changes, necrosis, calcification, or the urethra. In cases where multiple suspected csPCa lesions were observed, all assessors unanimously agreed that the largest lesion visible on T2WI and DWI was the primary lesion. Histopathological analysis Twelve tissue samples plus X (targeted biopsy) was performed with transrectal ultrasound guidance via a transperineal approach. Prostate biopsy was performed, and the operator reviewed and visually confirmed the findings. A total of 195 patients underwent rebiopsy with MRI and real-time US images (known as cognitive fusion biopsy), while 94 patients underwent radical prostatectomy[ 20 ]. A uropathologist with 18 years of experience examined all surgical and biopsy specimens in our institution using the updated Gleason score grading system from the ISUP 2014. The pathologist was not provided with any clinical or imaging data during the examination[ 21 ]. Statistical analysis Statistical analysis was performed with software (SPSS Version 18.0, SPSS; and R version 3.4.3, R Foundation for Statistical Computing). P values less than .05 were considered to indicate a statistically significant difference. The interreader agreement for the PI-RADS V2.1 and APT-weighted signal values was assessed using the k statistic and the intraclass correlation coefficient (ICC), respectively. For the k value, agreement levels were categorized as follows: <0.20 indicated slight agreement, 0.21–0.40 suggested fair agreement, 0.41–0.60 reflected moderate agreement, 0.61–0.80 represented substantial agreement, and 0.81-1.00 signified almost perfect agreement. The ICCs were defined as follows: 0.80-1.00 indicated excellent agreement, 0.60–0.79 indicated good agreement, 0.40–0.59 indicated moderate agreement, 0.20–0.39 indicated fair agreement, and 0.00-0.19 represented poor agreement[ 22 ]. The patients' APT max, APT mean, APT min, Prostate-specific antigen(PSA), and age are presented as the means and standard deviations. The normality of the distribution of the data was determined using the Kolmogorov‒Smirnov test. Subsequently, either the independent samples t test or the Wilcoxon rank sum test was employed to investigate the statistical variance in the demographic, clinical variables and APT parameters. For the parameters that displayed statistically significant differences, logistic regression analysis was conducted to predict the probability of the combined models. The diagnostic performance of PI-RADS V2.1 and the combined models was evaluated through receiver operating characteristic (ROC) curve analysis. The threshold, sensitivity, and specificity were determined utilizing the maximum Youden's index, while the area under the curve (AUC) was compared using the Delong method. Results Patient characteristics A total of 332 patients were recruited, 43 of whom were excluded based on the exclusion criteria. Ultimately, 289 individuals were enrolled; 102 had csPCa verified by histopathology, 187 had benign prostatic lesions or cisPCa, 102 had benign prostatic hyperplasia (BPH), 65 had chronic prostatitis, and 20 had cisPCa (Gleason = 6). Figure 1 shows a flow chart of participant recruitment. The demographic and clinical characteristics of the participants are summarized in Table 2 . Figure 1 Flow diagram of the patient selection process Table 2 The demographic and clinical characteristics of the participants Observer Consistency A weighted k value of 0.65 (95%CI 0.58–0.75) indicated substantial interobserver agreement with PI-RADS V2.1. The ICC results showed that the APT max, APT mean, and APT min values for csPCa and benign or cisPCa lesions measured by the two observers were in good agreement. The ICCs for csPCa were 0.889 for APTmax, 0.853 for APTmean, and 0.816 for APTmin. The ICCs for benign lesions or cisPCa were 0.910 for APTmax, 0.887 for APTmean, and 0.841 for APTmin. Differences in Parameters The mean and standard deviation of the APTmax, APTmean, and APTmin were 3.936 ± 0.922%, 3.019 ± 0.786%, and 2.094 ± 0.836%, respectively, in PZ csPCa; 2.995 ± 0.833%, 2.221 ± 0.772%, and 1.315 ± 0.837%, respectively, in PZ benign lesions and cisPCa; 4.006 ± 1.155%, 3.119 ± 1.006%, and 2.097 ± 0.866%, respectively, in TZ csPCa; and 2.806 ± 0.625%, 1.973 ± 0.691%, and 1.076 ± 0.887%, respectively, in TZ BPH or cisPCa.The details are shown in Table 3 and Figs. 2 and 3 Figure 2 Case examples. 1 80-year-old man was hospitalized, and the physical examination revealed that PSA level increased. (1a) T2WI. (1b) APT, APT max was 3.6%. (1c) DWI. (1d) Pathological images (original magnification, ×40) with PZ csPCa. 2 A 73-year-old man. (2a) T2WI. (2b) APT, APT max was 4.6%. (2c) DWI. (2d) Pathological images (original magnification, ×40) with TZ csPCa. Figure 3 Independent sample t tests of APT max, APT mean, and APT min between patients with csPCa and patients with benign lesions or cisPCa in the whole gland, PZ, and TZ. ****P < 0.0001. Table 3 Ability of the quantitative parameters in differentiating csPCa from benign prostate diseases or cisPCa Diagnostic Performance of PI-RADS V2.1 The diagnostic accuracy of PI-RADS V2.1 was evaluated. For detecting csPCa in the whole gland, PI-RADS category 4 had a sensitivity of 73% and specificity of 87%. In TZ, a threshold of 4 had a sensitivity of 83% and specificity of 75% for 131 lesions. Moreover, for 158 lesions in PZ, the sensitivity was 89%, with a specificity of 71%, using the same threshold. ROC analysis ROC analyses were used to evaluate the diagnostic accuracy of PI-RADS V2.1 and the combined models for distinguishing between csPCa and benign or cisPCa lesions in the whole gland, TZ, and PZ. The results are presented in Table 4 and Fig. 4. Table 4 Analysis of the ROC curves of the combinations of different quantitative parameters Figure 4 ROC curves analyses for assessing the diagnostic efficacy of apt parameters, PI-RADS V2.1 and combined models in the csPCa. a ROC curves of whole gland lesions. b ROC curves of the TZ lesions. c ROC curves of the PZ lesions. d ROC curves of the PI-RADS V2.1 = 3 lesions The combination of PI-RADS V2.1 with APTmax [AUC 0.883, 95% CI (0.842–0.923)], APTmean [0.877, 95% CI (0.835–0.919)], and APTmin [0.874, 95% CI (0.833–0.916)] demonstrated significantly better predictive performance than the PI-RADS V2.1 model alone [AUC 0.803, 95% CI (0.757–0.848), P = 0.01, 0.019, 0.023, respectively] for the whole gland. We conducted an analysis based on the locations of the lesions. In terms of the PZ, PI-RADS V2.1 in conjunction with the APTmax produced the highest AUC (0.885; 95% CI = 0.832–0.938) among the models tested. However, for the TZ, the improvements in the combination models [AUC = 0.854, 95% CI = 0.766–0.942; 0.865, 95% CI = 0.786–0.944; and 0.853, 95% CI = 0.770–0.935] were not significant compared to those in the PI-RADS V2.1 [AUC = 0.791, 95% CI = 0.710–0.873], P = 0.309, 0.203, 0.298]. PI-RADS V2.1 = 3 lesions Analysis of the ROC curve demonstrated that the PI-RADS 3 subgroup yielded the highest AUC for APTmax, which was 0.6 [95% CI (0.356–0.844)] for distinguishing csPCa from benign lesions or cisPCa (Table 4 and Fig. 4). DISCUSSION This study delves into the significance of this innovative functional MRI technique. When used in conjunction with PI-RADS V2.1, APT-weighted MRI helps to distinguish between csPCa and benign prostate lesions or cisPCa. It means APT-weighted imaging could provide additional value to PI-RADS V2.1. The combination of PI-RADS V2.1 and the APT max value yielded the most effective results in distinguishing csPCa from benign lesions or cisPCa in the whole prostate gland, with an AUC of 0.883. Moreover, the combined approach was successful at distinguishing csPCa from benign lesions or cisPCa in PZ (AUC = 0.885 vs 0.798, P = 0.036), indicating a significant improvement in performance. However, in TZ, the addition of APT-weighted signal values to PI-RADS V2.1 did not lead to a noteworthy improvement in diagnostic accuracy, with the AUC increasing from 0.791 to 0.853–0.865 ( P = 0.202). While PI-RADS V2.1 showed good overall performance in diagnosing csPCa, its specificity for PZ csPCa was low, potentially leading to unnecessary biopsies[ 23 , 24 ]. In our evaluation, the highest Youden's index was obtained at a PI-RADS V2.1 specificity of 0.706. Currently, APT-weighted MRI has shown promise as a good predictor for detecting PCa in PZ[ 17 , 18 ]. Our study revealed that combining APTmax with the PI-RADS V2.1 score improved the specificity of diagnosing PZ csPCa and successfully enhanced the diagnostic efficiency. Moreover, APT signal values may help address the limitations of PI-RADS V2.1 and contribute positively to the specificity of identifying different csPCa lesions in PZ. For detecting csPCa in TZ, APT-weighted signals values were included in the multivariate model. It was found to be beneficial, albeit with a minor additional impact when combined with PI-RADS V2.1, in predicting csPCa in the TZ. According to the PI-RADS V2.1, T2WI is considered the most crucial sequence for identifying and characterizing prostate lesions in TZ, followed by DWI. However, the morphological features of TZ csPCa on T2WI in the context of PI-RADS V2.1 are subjective. Therefore, other quantitative parameters should be further analyzed to improve the diagnostic performance of PI-RADS V2.1 for the detection of TZ csPCa. Our results indicated that APT max, APT mean, and APT min were independent predictors of csPCa, the APT-weighted signal values in patients with csPCa was generally greater than that in patients with benign prostate lesions or cisPCa. However, these conclusions are not consistent with the findings of Yang et al. [ 25 ], who demonstrated that APTmax and APTmean could accurately distinguish between malignant and benign prostate lesions and that the differences in APTmin were not statistically significant. The reason could be that the samples were different. In the present study, it was diagnostic for csPCa but not for all PCa cases. Guo et al[ 26 ] reported that APT-weighted signal value can provide more accurate lesion characterization for distinguishing TZ PCa from BPH, with an AUC of 0.812. According to the findings of Yin et al[ 27 ], APT imaging can be used to discriminate PCa from BPH, with an AUC of 0.8. Qin et al[ 28 ] reported that APT imaging performed well in PCa risk classification and had reproducible cutoff values in TZ and PZ. In line with our findings, these early results suggest that APT-weighted imaging is a promising valuable imaging marker for detecting PCa. Theoretically, increased APT-weighted signal values indicate enhanced concentrations of proteins and peptides due to abnormal protein synthesis by rapidly dividing tumor cells and altered cellular metabolism in high-grade malignancies. Malignant prostate lesions exhibit more active metabolism than benign lesions, leading to a denser cell arrangement, reduced intercellular space, and greater secretion of macromolecules and peptides in csPCa, all of which correspond to elevated APT-weighted signal values[ 29 , 30 ]. The sub-analysis demonstrated moderate diagnostic accuracy, with APTmax values of 0.6 indicating the potential to differentiate csPCa in patients with PI-RADS 3 lesions. Although PI-RADS 3 lesions are commonly observed and exhibit a moderate to high risk of malignancy, their optimal treatment remains under investigation[ 19 , 31 ]. Stratifying these lesions using the PI-RADS V2.1 algorithm continues to be challenging. Our results indicated that APTmax exhibited high sensitivity and low specificity for PI-RADS 3 lesions, suggesting that APTmax has moderate diagnostic accuracy but a relatively high false-negative rate. This finding implies that functional MRI sequences such as APT imaging may not serve as effective stand-alone predictive markers for distinguishing benign and malignant prostate lesions within PI-RADS V2.1 3 lesions. Our preliminary findings provide initial support for utilizing APT-weighted imaging for stratifying PI-RADS 3 lesions. Strengths and limitations To the best of our knowledge, this study is the first to assess the effectiveness of APT-weighted imaging in diagnosing csPCa compared to that of combination model (PI-RADS V2.1 and the 3D APT approach). The 3D APT sequence offers a comprehensive scan of the entire prostate area, a higher signal-to-noise ratio, and less image distortion than does the 2D APT sequence[ 32 , 33 ]. Notably, we also conducted an initial investigation into the diagnostic accuracy of APT-weighted signal values for detecting csPCa in patients with PI-RADS 3 lesions. There are a few potential limitations to consider. First, this study was retrospectively conducted at a single center, potentially leading to patient selection bias that may restrict generalizability. Hence, the current findings may require further validation in prospective multicenter studies involving a larger patient cohort. Second, using freehand ROI analysis may introduce artificial errors that could impact accuracy. Additionally, it is not possible to completely eliminate the risk of undetected necrosis or cystic changes, potentially contaminating the results. Utilizing methods such as histograms and iconography may offer more objectivity and enhance accuracy. Third, in some cases, the reference standard was MRI-based targeted biopsy via transrectal ultrasound, which could overlook potential lesions that were negative on MRI but positive on pathology. Conclusions A combination model incorporating APT-weighted signal values and the PI-RADS V2.1 score may enhance the diagnostic efficacy for csPCa in the whole gland and PZ compared to PI-RADS V2.1 alone. However, no significant improvement in accuracy was noted for csPCa in the TZ. For lesions classified as PI-RADS V2.1, quantitative APT values may not be an effective parameter for diagnosing csPCa. Abbreviations PCa Prostate cancer APT Amide proton transfer PI-RADS V2.1 Prostate Imaging Reporting and Data System scoring system version 2.1 csPCa Clinically significant prostate cancer T2WI T2-weighted imaging DWI Diffusion-weighted imaging DCE Dynamic contrast-enhanced ROC Receiver operating characteristic AUC Area under the curve CisPCa Clinically insignificant prostate cancer PZ Peripheral zone TZ Transition zone ISUP International Society of Urological Pathology MpMRI Multiparametric magnetic resonance imaging ICC Intraclass correlation coefficient ADC Apparent diffusion coefficient REST Regional saturation technique ROI Region of interest BPH Benign prostatic hyperplasia Declarations Acknowledgement No. Authors’ contributions L.Z, LC.L study concepts and design: X.Z literature research: J.Z, M.T clinical studies: J.Z, LC.L data analysis: L.Z manuscript preparation: XY.L, XL. D manuscript editing. All authors read and approved the final manuscript. Funding This work was supported by Shaanxi Provincial People's Hospital Science and technology talent support plan (2021JY-43), Shaanxi Provincial People's Hospital incubation Fund (2022YJY-13). Availability of data and materials The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. Ethics approval and consent to participate This study was approved by the ethics committee of the Shaanxi Provincial People's Hospital, and all subjects signed an informed consent form before the examination, and all methods were carried out in accordance with relevant guidelines. Consent for publication Not Applicable. Competing interests The authors declare that they have no competing interests. References Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics. CA Cancer J Clin.2022;72:7-33. https://doi.org/10.3322/caac.21708. NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines). Prostate Cancer. Version 3.2020. 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Konsenskonferenz 2014 der ISUP zur Gleason-Graduierung des Prostatakarzinoms [The 2014 consensus conference of the ISUP on Gleason grading of prostatic carcinoma]. Pathologe. 2016;37:17-26. https://doi.org/10.1007/s00292-015-0136-6. Ahlawat S, Khandheria P, Del Grande F, Morelli J, Subhawong TK, Demehri S, et al. Interobserver variability of selective region-of-interest measurement protocols for quantitative diffusion weighted imaging in soft tissue masses: Comparison with whole tumor volume measurements. J Magn Reson Imaging. 2016;43:446-454. https://doi.org/10.1002/jmri.24994. Kim HS, Kwon GY, Kim MJ, Park SY. Prostate Imaging-Reporting and Data System: Comparison of the Diagnostic Performance between Version 2.0 and 2.1 for Prostatic Peripheral Zone. Korean J Radiol. 2021;22:1100-1109. https://doi.org/10.3348/kjr.2020.0837. Rudolph MM, Baur ADJ, Cash H, Haas M, Mahjoub S, Hartenstein A, et al. Diagnostic performance of PI-RADS version 2.1 compared to version 2.0 for detection of peripheral and transition zone prostate cancer. Sci Rep. 2020;10:15982. https://doi.org/10.1038/s41598-020-72544-z. Yang L, Wang L, Tan Y, Dan H, Xian P, Zhang Y, et al. Amide Proton Transfer-weighted MRI combined with serum prostate-specific antigen levels for differentiating malignant prostate lesions from benign prostate lesions: a retrospective cohort study. Cancer Imaging. 2023 Jan 7;23:3. https://doi.org/10.1186/s40644-022-00515-w. Guo Z, Qin X, Mu R, Lv J, Meng Z, Zheng W, et al. Amide Proton Transfer Could Provide More Accurate Lesion Characterization in the Transition Zone of the Prostate. J Magn Reson Imaging. 2022;56:1311-1319. https://doi.org/10.1002/jmri.28204. Yin H, Wang D, Yan R, Jin X, Hu Y, Zhai Z, et al. Comparison of Diffusion Kurtosis Imaging and Amide Proton Transfer Imaging in the Diagnosis and Risk Assessment of Prostate Cancer. Front Oncol. 2021;11:640906. https://doi.org/10.3389/fonc.2021.640906. Qin X, Mu R, Zheng W, Li X, Liu F, Zhuang Z, et al. Comparison and combination of amide proton transfer magnetic resonance imaging and the apparent diffusion coefficient in differentiating the grades of prostate cancer. Quantitative Imaging In Medicine And Surgery.2022;13, 812-824.https://doi.org/10.21037/qims-22-721. Jia G, Abaza R, Williams JD, Zynger DL, Zhou J, Shah ZK, et al. Amide proton transfer MR imaging of prostate cancer: a preliminary study. J Magn Reson Imaging. 2011;33:647-654. https://doi.org/10.1002/jmri.22480. Takayama Y, Nishie A, Sugimoto M, Togao O, Asayama Y, Ishigami K, et al. Amide proton transfer (APT) magnetic resonance imaging of prostate cancer: comparison with Gleason scores. MAGMA. 2016;29:671-679. https://doi.org/10.1007/s10334-016-0537-4. Schoots I G . MRI in early prostate cancer detection: how to manage indeterminate or equivocal PI-RADS 3 lesions? Translational Andrology & Urology, 2018, 7:70-82. https://doi.org/10.21037/tau.2017.12.31. Chen W, Li L, Yan Z, Hu S, Feng J, Liu G, et al. Three-dimension amide proton transfer MRI of rectal adenocarcinoma: correlation with pathologic prognostic factors and comparison with diffusion kurtosis imaging. Eur Radiol. 2021;31:3286-3296. https://doi.org/10.1007/s00330-020-07397-1. Wang HJ, Cai Q, Huang YP, Li MQ, Wen ZH, Lin YY, et al. Amide Proton Transfer-weighted MRI in Predicting Histologic Grade of Bladder Cancer [published correction appears in Radiology. Radiology. 2022;305:127-134. https://doi.org/10.1148/radiol.211804. Tables Table 1 Imaging protocol parameters Sacn protocol Scan sequences TR(ms) TE(ms) FOV(mm2) Slice thickness(mm) Number of slices Matrix Scan time(min) T1 TSE 582 8 260×260 4 24 288×228 1:24 T2 TSE 2757 110 260×260 4 24 432×366 2:12 APT TSE 7280 8.3 230×180 6 10 128×100 4:29 DWI EPI 2982 75 260×260 4 24 88×86 3:38 DCE TFE 3.2 1.52 260×260 4 52 216×217 4:07 Table 2 The demographic and clinical characteristics of the participants Characteristic csPCa(n = 102) Benign or cisPCa(n = 187) P -value Age (year, mean ± SD) 73.11 ± 9.17 69.41 ± 9.31 0.001 tPSA (ng/ml, IQR) 17.7(8.67,69.98) 9.4(6.62,16.87) < 0.001 Location PZ 73 85 < 0.001 TZ 29 102 < 0.001 Gleason score 3 + 3 20 3 + 4 22 3 + 5 9 4 + 3 18 4 + 4 6 4 + 5 29 5 + 3 1 5 + 4 11 5 + 5 6 Tissue acquisition method < 0.001 Cognitive fusion biopsy 75 113 Radical prostatectomy 27 74 PI-RADS V2.1 score < 0.001 1 0 7 2 3 59 3 5 63 4 42 50 5 52 8 Table 3 Ability of the quantitative parameters in differentiating csPCa from benign prostate diseases or cisPCa Variable Malignant(%) Benign or cisPCa(%) t p Parameters for PZ APT max 3.936 ± 0.9217 2.995 ± 0.8327 -12.997 < 0.001 APTmean 3.019 ± 0.7856 2.221 ± 0.7715 -12.534 < 0.001 APTmin 2.094 ± 0.8355 1.315 ± 0.8365 -11.4 < 0.001 Parameters for TZ APT max 4.006 ± 1.1549 2.806 ± 0.6248 -10.911 < 0.001 APTmean 3.119 ± 1.0055 1.973 ± 0.6912 -11.593 < 0.001 APTmin 2.097 ± 0.8660 1.076 ± 0.8867 -10.835 < 0.001 Parameters for Whole Gland APT max 3.953 ± 0.9865 2.895 ± 0.7348 -19.718 < 0.001 APTmean 3.045 ± 0.8480 2.089 ± 0.7396 -19.363 < 0.001 APTmin 2.095 ± 0.8426 1.188 ± 0.8711 -16.985 < 0.001 PI-RADS V2.1 = 3 lesions APT max 2.940 ± 0.4925 2.795 ± 0.5402 -1.005 0.316 APTmean 2.060 ± 0.5462 1.943 ± 0.5637 -0.776 0.438 APTmin 1.380 ± 0.7589 1.125 ± 0.7667 -1.239 0.217 Table 4 Analysis of the ROC curves of the combinations of different quantitative parameters Whole Gland AUC 95%CI Sensitivity Specificity Youden index J Threshold P PI-RADS V2.1 0.803 (0.757–0.848) 0.733 0.873 1.605 PI-RADS V2.1 + APT max 0.883 (0.842–0.923) 0.861 0.784 1.645 0.484 0.011 PI-RADS V2.1 + APT mean 0.877 (0.835–0.919) 0.843 0.813 1.656 0.389 0.019 PI-RADS V2.1 + APT min 0.874 (0.833–0.916) 0.824 0.797 1.621 0.409 0.023 PZ PI-RADS V2.1 0.798 (0.738–0.859) 0.890 0.706 1.596 PI-RADS V2.1 + APT max 0.885 (0.832–0.938) 0.795 0.871 1.665 0.611 0.036 PI-RADS V2.1 + APT mean 0.872 (0.815–0.928) 0.808 0.859 1.667 0.574 0.082 PI-RADS V2.1 + APT min 0.876 (0.821–0.930) 0.795 0.835 1.629 0.578 0.062 TZ PI-RADS V2.1 0.791 (0.710–0.873) 0.828 0.755 1.582 PI-RADS V2.1 + APT max 0.854 (0.766–0.942) 0.759 0.892 1.651 0.328 0.309 PI-RADS V2.1 + APT mean 0.865 (0.786–0.944) 0.828 0.833 1.661 0.240 0.203 PI-RADS V2.1 + APT min 0.853 (0.770–0.935) 0.724 0.902 1.626 0.426 0.298 PI-RADS V2.1 = 3 lesions APT max 0.6 (0.356–0.844) 1 0.302 1.302 2.45 APT mean 0.537 (0.299–0.774) 1 0.302 1.302 1.65 APT min 0.563 (0.321–0.806) 1 0.238 1.238 0.65 Additional Declarations No competing interests reported. Supplementary Files figureS1.jpg Cite Share Download PDF Status: Published Journal Publication published 30 Jul, 2025 Read the published version in BMC Cancer → Version 1 posted Editorial decision: Revision requested 10 Dec, 2024 Reviews received at journal 05 Dec, 2024 Reviewers agreed at journal 26 Nov, 2024 Reviews received at journal 23 Oct, 2024 Reviewers agreed at journal 19 Oct, 2024 Editor invited by journal 25 Sep, 2024 Reviewers agreed at journal 22 Jun, 2024 Reviewers invited by journal 17 Jun, 2024 Editor assigned by journal 29 Mar, 2024 Submission checks completed at journal 29 Mar, 2024 First submitted to journal 26 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-4168033","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":285539274,"identity":"5d400735-5e53-458b-904d-b892f086650b","order_by":0,"name":"Li Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYBACNvbmAwc+VNjwyMuff/gg4YcNYS18PMcSH844kyZnOIOH2eBhTxphLXISOcbGvG2HjRlu8LBJPmA7TITDeA6YSQC1JDbO7j1WkcBzmIG/vTuBgF8a0iQkzqUntsucS7uRYJHOIHHm7AZCthyTMCizTmxsSDC7kcBjzWAgkUtAi0Rim0QCG3Niw4EEswIggxgtycwGB9qcgd7PMWNIYHMmQgvPMcaHDaBA7jmWLJHYk8ZD0C/y7f0fDv8BRSV788GPP37YyPG39+LXggF4SFM+CkbBKBgFowArAADtW01L317HOgAAAABJRU5ErkJggg==","orcid":"","institution":"Institute of Medical Research Northwestern Polytechnical University, Xi'an","correspondingAuthor":true,"prefix":"","firstName":"Li","middleName":"","lastName":"Zhang","suffix":""},{"id":285539275,"identity":"27157934-1a5e-44c7-af9f-f034ce13aecb","order_by":1,"name":"Longchao Li","email":"","orcid":"","institution":"Department of MRI, Shaanxi Provincial People's Hospital, Xi'an","correspondingAuthor":false,"prefix":"","firstName":"Longchao","middleName":"","lastName":"Li","suffix":""},{"id":285539276,"identity":"98192205-6b77-4f4f-92d4-f22cebe85033","order_by":2,"name":"Xia Zhe","email":"","orcid":"","institution":"Department of MRI, Shaanxi Provincial People's Hospital, Xi'an","correspondingAuthor":false,"prefix":"","firstName":"Xia","middleName":"","lastName":"Zhe","suffix":""},{"id":285539277,"identity":"3e263863-b282-4826-8d8c-2456b6132a06","order_by":3,"name":"Min Tang","email":"","orcid":"","institution":"Department of MRI, Shaanxi Provincial People's Hospital, Xi'an","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Tang","suffix":""},{"id":285539278,"identity":"e10879cb-e043-4b3f-b090-b8a38c98f8f5","order_by":4,"name":"Xiaoyan Lei","email":"","orcid":"","institution":"Department of MRI, Shaanxi Provincial People's Hospital, Xi'an","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyan","middleName":"","lastName":"Lei","suffix":""},{"id":285539279,"identity":"42cbab94-2f86-4810-b225-5b3c5672b56b","order_by":5,"name":"Jing Zhang","email":"","orcid":"","institution":"Department of MRI, Shaanxi Provincial People's Hospital, Xi'an","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Zhang","suffix":""},{"id":285539280,"identity":"656b0628-d82d-44b9-b579-5a4a6665dafb","order_by":6,"name":"Xianglong Duan","email":"","orcid":"","institution":"Institute of Medical Research Northwestern Polytechnical University, Xi'an","correspondingAuthor":false,"prefix":"","firstName":"Xianglong","middleName":"","lastName":"Duan","suffix":""}],"badges":[],"createdAt":"2024-03-26 08:00:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4168033/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4168033/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12885-025-14610-1","type":"published","date":"2025-07-30T16:13:02+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":54106530,"identity":"7ab572db-26b3-4c58-a23a-14df880f238e","added_by":"auto","created_at":"2024-04-04 17:22:26","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":559559,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of the patient selection process\u003c/p\u003e","description":"","filename":"figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4168033/v1/2274c47540f2cce05e26af6e.jpg"},{"id":54106459,"identity":"1357c232-02b4-4523-916e-76810ce024b6","added_by":"auto","created_at":"2024-04-04 17:22:25","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1124755,"visible":true,"origin":"","legend":"\u003cp\u003eCase examples. 1 80-year-old man was hospitalized, and the physical examination revealed that PSA level increased. (1a) T2WI. (1b) APT, APT max was 3.6%. (1c) DWI. (1d) Pathological images (original magnification, ×40)with PZ csPCa. 2 A 73-year-old man. (2a) T2WI. (2b) APT, APT max was 4.6%. (2c) DWI. (2d) Pathological images (original magnification, ×40) with TZ csPCa.\u003c/p\u003e","description":"","filename":"figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4168033/v1/21d746a1a140575905f68a06.jpg"},{"id":54106528,"identity":"528ae0b2-6afe-4b8b-97ec-bc91a6d40859","added_by":"auto","created_at":"2024-04-04 17:22:26","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":863015,"visible":true,"origin":"","legend":"\u003cp\u003eIndependent sample t tests of APT max, APT mean, and APT min between patients with csPCa and patients with benign lesions or cisPCa in the whole gland, PZ, and TZ. ****P \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4168033/v1/87342a4f18eb24d9f58f5542.jpg"},{"id":54106529,"identity":"f0aaea59-1bce-43b3-8b0c-3707d34ec895","added_by":"auto","created_at":"2024-04-04 17:22:26","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":869522,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves analyses for assessing the diagnostic efficacy of apt parameters, PI-RADS V2.1 and combined models in the csPCa. a ROC curves of whole gland lesions. b ROC curves of the TZ lesions. c ROC curves of the PZ lesions. d ROC curves of the PI-RADS V2.1=3 lesions\u003c/p\u003e","description":"","filename":"figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4168033/v1/af6d667284f70335eb83993e.jpg"},{"id":88268191,"identity":"6880fac5-0a7a-4841-a431-963ebe11b6d0","added_by":"auto","created_at":"2025-08-04 16:49:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4384891,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4168033/v1/55c47e1e-7d28-462f-89ab-dbc2ce0730dc.pdf"},{"id":54106531,"identity":"b474e085-8d72-46ea-891b-2a331d2321e7","added_by":"auto","created_at":"2024-04-04 17:22:26","extension":"jpg","order_by":16,"title":"","display":"","copyAsset":false,"role":"supplement","size":98945,"visible":true,"origin":"","legend":"","description":"","filename":"figureS1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4168033/v1/16996bdcba460fcacdbd1f69.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Combination of PI-RADS version 2.1 and amide proton transfer values for the detection of clinically significant prostate cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eProstate cancer (PCa) is the most common cancer among men in the United States and the second most prevalent malignancy in men globally, affecting approximately 375,000 men each year and being a leading cause of cancer-related deaths[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, not all individuals with PCa require curative treatment[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrecise identification of clinically significant prostate cancer (CsPCa) is crucial for preventing under- or overtreatment. CsPCa can be characterized by International Society of Urological Pathology (ISUP) grade\u0026thinsp;\u0026gt;\u0026thinsp;1 (Gleason score 3\u0026thinsp;+\u0026thinsp;3\u0026thinsp;=\u0026thinsp;6) cancer with a volume exceeding 0.5 mL[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrently, multiparametric magnetic resonance imaging (mpMRI) is considered an effective noninvasive approach for identifying, staging, and managing csPCa[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The Prostate Imaging Reporting and Data System(PI-RADS) has been established to standardize diagnostic criteria for mpMRI, providing consistent and precise definitions for interpreting prostate MRI findings[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo address the limitations of earlier versions, PI-RADS was updated to version 2.1 (V2.1) in 2019. This updated system utilizes a 5-point scale to assess the probability of csPCa based on mpMRI results from axial T2-weighted imaging(T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) maps[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, prior studies have shown that the specificity of PI-RADS V2.1 can still improve to reduce the number of unnecessary biopsies [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Lesions categorized as PI-RADS V2.1 category 3 are typically considered inconclusive or uncertain [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Moreover, the absence of quantitative metrics in PI-RADS V2.1 has been noted [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The PI-RADS Steering Committee strongly advocates for the exploration of new MRI techniques using innovative research tools that are not covered in PI-RADS V2.1 for assessing csPCa. The committee will consider incorporating relevant data and experiences into future iterations of PI-RADS [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Hence, there is a call for new quantitative imaging markers to enhance the characterization and visualization of csPCa.\u003c/p\u003e \u003cp\u003eThe use of APT-weighted MRI relies on the concept that tumor cells, which are driven by high protein synthesis and proliferation, exhibit different protein levels in benign and malignant lesions [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Therefore, quantitatively analyzing APT-weighted MRI could lead to the use of APT values as an imaging biomarker that accurately reflects molecular changes.\u003c/p\u003e \u003cp\u003eSome studies have demonstrated the utility of prostate APT-weighted MRI for detecting PCa [\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, these studies had small sample sizes, did not fully differentiate between clinically insignificant prostate cancer (cisPCa) or benign lesions and csPCa; failed to specifically address peripheral zone (PZ) csPCa; moreover, they did not utilize a combination study with PI-RADS V2.1.\u003c/p\u003e \u003cp\u003eCurrently, lesions with a PI-RADS score of 3, indicating an uncertain likelihood of csPCa, pose a significant clinical challenge. Approximately 20%-30% of these lesions are PCa, and deciding whether to proceed with a biopsy in these cases is complex[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In the realm of prostate MRI, quantitative methods have become increasingly favored. Notably, there is a lack of research on the utility of APT-weighted MRI for evaluating PI-RADS 3 lesions.\u003c/p\u003e \u003cp\u003eConsequently, this research sought to assess whether combining APT-weighted MRI with the PI-RADS V2.1 could increase diagnostic accuracy compared to PI-RADS V2.1 alone in predicting csPCa. Furthermore, we assessed the additional benefit of APT-weighted MRI in predicting csPCa in PI-RADS 3 lesions.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003e The Ethics Committee of Shaanxi Provincial People's Hospital approved this study in accordance with the Declaration of Helsinki. And all subjects signed an informed consent form before the examination.\u003c/p\u003e \u003cp\u003eFrom July 2022 to August 2023, consecutive patients underwent preoperative prostate mpMRI, and either prostate biopsy or radical individuals who had undergone a prostatectomy were enlisted.\u003c/p\u003e \u003cp\u003eThe inclusion criteria were as follows:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePatients who underwent mpMRI scans, which included T2WI, APT-weighted imaging, DWI, and DCE.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePatients who underwent a biopsy and/or radical prostatectomy within one month following mpMRI.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe exclusion criteria for patients were as follows: First, patients had previously undergone prostate surgery or had previously received endocrine therapy. Second, participants were found to have poor MRI quality, as noted by two radiologists. Additionally, individuals with acute prostatitis or prostatic abscess were excluded from the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMRI Parameters\u003c/h2\u003e \u003cp\u003eAn INGENIA 3.0 T MRI scanner (Philips Healthcare Co., Ltd., Best, The Netherlands) equipped with a 16-channel phased-array body coil was utilized for prostate MRI scans of all patients. The scan sequences can be found in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e displays the sequences obtained from multiparametric MRI scans.\u003c/p\u003e \u003cp\u003eAxial DWI was performed using b values of 50, 1000, and 2000 sec/mm\u003csup\u003e2\u003c/sup\u003e. The scanner automatically generated apparent diffusion coefficient (ADC) maps from the DWI data.\u003c/p\u003e \u003cp\u003eTo reduce or eliminate motion artifacts, four regional saturation technique slabs were used during APT scanning to suppress the MRI signal from moving tissues beyond the scanned region. Two slabs were placed over the rectum and bladder to mitigate motion artifacts, while the remaining two were positioned over the left and right iliac crests to improve the uniformity of the B1 field. Adjustments to the slab angulation, center, and width were based on the patient's size(Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e Indication of REST slabs that were used during the amide proton transfer sequence scanning. (a) REST slabs in the sagittal section. (b) REST slabs in the coronal section. (c) REST slabs in the transversal section. REST, regional saturation technique; APT, amide proton transfer.\u003c/p\u003e \u003cp\u003eThe only parameter in the APT model is the asymmetric magnetization transfer rate at 3.5 ppm [MTRasym (3.5 ppm)], which is determined using the following equation:\u003c/p\u003e \u003cp\u003eMTRasym (3.5 ppm) is expressed as the difference between the signal intensity at +\u0026thinsp;3.5 ppm and \u0026minus;\u0026thinsp;3.5 ppm divided by the signal intensity without the saturation pulse (S0). Here, S0 represents the baseline signal intensity, while Ssat denotes the signal intensity following the application of the saturation pulse. MTRasym (3.5 ppm) refers to the asymmetry in the magnetization transfer ratio at 3.5 ppm.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eImage analysis\u003c/h2\u003e \u003cp\u003eThe APT-weighted image was aligned with the T2WI using the postprocessing workstation \"IntelliSpace Portal\" version 8 developed by Philips Healthcare in the Netherlands.\u003c/p\u003e \u003cp\u003eTwo radiologists specializing in fellowship training (Z.L., 8 years of experience; L.L.C., 3 years of experience) independently evaluated all prostate MRI, including APT-weighted signal value results. The patients were kept unaware of clinical and pathological information to prevent bias. The assessments of PI-RADS V2.1 and APT data were conducted separately with a one-month gap to minimize recall bias.\u003c/p\u003e \u003cp\u003eEvery lesion was evaluated according to the PI-RADS V2.1 criteria using DWI, DCE, and T2WI sequences, and the highest total PI-RADS score from each mpMRI scan was selected.\u003c/p\u003e \u003cp\u003eThe highest lesion level was identified using T2WI as a reference. APT-weighted signal values were subsequently assessed by choosing a region of interest (ROI) at the corresponding level on the APT images. To prevent any volumetric bias, the ROIs were positioned away from the periphery of the lesion's solid components. It is important to steer clear of placing ROIs close to areas showing cystic changes, necrosis, calcification, or the urethra. In cases where multiple suspected csPCa lesions were observed, all assessors unanimously agreed that the largest lesion visible on T2WI and DWI was the primary lesion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eHistopathological analysis\u003c/h2\u003e \u003cp\u003eTwelve tissue samples plus X (targeted biopsy) was performed with transrectal ultrasound guidance via a transperineal approach. Prostate biopsy was performed, and the operator reviewed and visually confirmed the findings. A total of 195 patients underwent rebiopsy with MRI and real-time US images (known as cognitive fusion biopsy), while 94 patients underwent radical prostatectomy[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA uropathologist with 18 years of experience examined all surgical and biopsy specimens in our institution using the updated Gleason score grading system from the ISUP 2014. The pathologist was not provided with any clinical or imaging data during the examination[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed with software (SPSS Version 18.0, SPSS; and R version 3.4.3, R Foundation for Statistical Computing). \u003cem\u003eP\u003c/em\u003e values less than .05 were considered to indicate a statistically significant difference. The interreader agreement for the PI-RADS V2.1 and APT-weighted signal values was assessed using the k statistic and the intraclass correlation coefficient (ICC), respectively. For the k value, agreement levels were categorized as follows: \u0026lt;0.20 indicated slight agreement, 0.21\u0026ndash;0.40 suggested fair agreement, 0.41\u0026ndash;0.60 reflected moderate agreement, 0.61\u0026ndash;0.80 represented substantial agreement, and 0.81-1.00 signified almost perfect agreement. The ICCs were defined as follows: 0.80-1.00 indicated excellent agreement, 0.60\u0026ndash;0.79 indicated good agreement, 0.40\u0026ndash;0.59 indicated moderate agreement, 0.20\u0026ndash;0.39 indicated fair agreement, and 0.00-0.19 represented poor agreement[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe patients' APT max, APT mean, APT min, Prostate-specific antigen(PSA), and age are presented as the means and standard deviations. The normality of the distribution of the data was determined using the Kolmogorov‒Smirnov test. Subsequently, either the independent samples t test or the Wilcoxon rank sum test was employed to investigate the statistical variance in the demographic, clinical variables and APT parameters.\u003c/p\u003e \u003cp\u003eFor the parameters that displayed statistically significant differences, logistic regression analysis was conducted to predict the probability of the combined models. The diagnostic performance of PI-RADS V2.1 and the combined models was evaluated through receiver operating characteristic (ROC) curve analysis. The threshold, sensitivity, and specificity were determined utilizing the maximum Youden's index, while the area under the curve (AUC) was compared using the Delong method.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePatient characteristics\u003c/h2\u003e \u003cp\u003eA total of 332 patients were recruited, 43 of whom were excluded based on the exclusion criteria. Ultimately, 289 individuals were enrolled; 102 had csPCa verified by histopathology, 187 had benign prostatic lesions or cisPCa, 102 had benign prostatic hyperplasia (BPH), 65 had chronic prostatitis, and 20 had cisPCa (Gleason\u0026thinsp;=\u0026thinsp;6). Figure\u0026nbsp;1 shows a flow chart of participant recruitment. The demographic and clinical characteristics of the participants are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eFigure 1 Flow diagram of the patient selection process\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e The demographic and clinical characteristics of the participants\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eObserver Consistency\u003c/h2\u003e \u003cp\u003eA weighted k value of 0.65 (95%CI 0.58\u0026ndash;0.75) indicated substantial interobserver agreement with PI-RADS V2.1.\u003c/p\u003e \u003cp\u003eThe ICC results showed that the APT max, APT mean, and APT min values for csPCa and benign or cisPCa lesions measured by the two observers were in good agreement. The ICCs for csPCa were 0.889 for APTmax, 0.853 for APTmean, and 0.816 for APTmin. The ICCs for benign lesions or cisPCa were 0.910 for APTmax, 0.887 for APTmean, and 0.841 for APTmin.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDifferences in Parameters\u003c/h2\u003e \u003cp\u003eThe mean and standard deviation of the APTmax, APTmean, and APTmin were 3.936\u0026thinsp;\u0026plusmn;\u0026thinsp;0.922%, 3.019\u0026thinsp;\u0026plusmn;\u0026thinsp;0.786%, and 2.094\u0026thinsp;\u0026plusmn;\u0026thinsp;0.836%, respectively, in PZ csPCa; 2.995\u0026thinsp;\u0026plusmn;\u0026thinsp;0.833%, 2.221\u0026thinsp;\u0026plusmn;\u0026thinsp;0.772%, and 1.315\u0026thinsp;\u0026plusmn;\u0026thinsp;0.837%, respectively, in PZ benign lesions and cisPCa; 4.006\u0026thinsp;\u0026plusmn;\u0026thinsp;1.155%, 3.119\u0026thinsp;\u0026plusmn;\u0026thinsp;1.006%, and 2.097\u0026thinsp;\u0026plusmn;\u0026thinsp;0.866%, respectively, in TZ csPCa; and 2.806\u0026thinsp;\u0026plusmn;\u0026thinsp;0.625%, 1.973\u0026thinsp;\u0026plusmn;\u0026thinsp;0.691%, and 1.076\u0026thinsp;\u0026plusmn;\u0026thinsp;0.887%, respectively, in TZ BPH or cisPCa.The details are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Figs.\u0026nbsp;2 and 3\u003c/p\u003e \u003cp\u003eFigure 2 Case examples. 1 80-year-old man was hospitalized, and the physical examination revealed that PSA level increased. (1a) T2WI. (1b) APT, APT max was 3.6%. (1c) DWI. (1d) Pathological images (original magnification, \u0026times;40) with PZ csPCa. 2 A 73-year-old man. (2a) T2WI. (2b) APT, APT max was 4.6%. (2c) DWI. (2d) Pathological images (original magnification, \u0026times;40) with TZ csPCa.\u003c/p\u003e \u003cp\u003eFigure 3 Independent sample t tests of APT max, APT mean, and APT min between patients with csPCa and patients with benign lesions or cisPCa in the whole gland, PZ, and TZ. ****P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e Ability of the quantitative parameters in differentiating csPCa from benign prostate diseases or cisPCa\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDiagnostic Performance of PI-RADS V2.1\u003c/h2\u003e \u003cp\u003eThe diagnostic accuracy of PI-RADS V2.1 was evaluated. For detecting csPCa in the whole gland, PI-RADS category 4 had a sensitivity of 73% and specificity of 87%. In TZ, a threshold of 4 had a sensitivity of 83% and specificity of 75% for 131 lesions. Moreover, for 158 lesions in PZ, the sensitivity was 89%, with a specificity of 71%, using the same threshold.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eROC analysis\u003c/h2\u003e \u003cp\u003eROC analyses were used to evaluate the diagnostic accuracy of PI-RADS V2.1 and the combined models for distinguishing between csPCa and benign or cisPCa lesions in the whole gland, TZ, and PZ. The results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;4.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e Analysis of the ROC curves of the combinations of different quantitative parameters\u003c/p\u003e \u003cp\u003eFigure 4 ROC curves analyses for assessing the diagnostic efficacy of apt parameters, PI-RADS V2.1 and combined models in the csPCa. a ROC curves of whole gland lesions. b ROC curves of the TZ lesions. c ROC curves of the PZ lesions. d ROC curves of the PI-RADS V2.1\u0026thinsp;=\u0026thinsp;3 lesions\u003c/p\u003e \u003cp\u003eThe combination of PI-RADS V2.1 with APTmax [AUC 0.883, 95% CI (0.842\u0026ndash;0.923)], APTmean [0.877, 95% CI (0.835\u0026ndash;0.919)], and APTmin [0.874, 95% CI (0.833\u0026ndash;0.916)] demonstrated significantly better predictive performance than the PI-RADS V2.1 model alone [AUC 0.803, 95% CI (0.757\u0026ndash;0.848), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01, 0.019, 0.023, respectively] for the whole gland.\u003c/p\u003e \u003cp\u003eWe conducted an analysis based on the locations of the lesions. In terms of the PZ, PI-RADS V2.1 in conjunction with the APTmax produced the highest AUC (0.885; 95% CI\u0026thinsp;=\u0026thinsp;0.832\u0026ndash;0.938) among the models tested. However, for the TZ, the improvements in the combination models [AUC\u0026thinsp;=\u0026thinsp;0.854, 95% CI\u0026thinsp;=\u0026thinsp;0.766\u0026ndash;0.942; 0.865, 95% CI\u0026thinsp;=\u0026thinsp;0.786\u0026ndash;0.944; and 0.853, 95% CI\u0026thinsp;=\u0026thinsp;0.770\u0026ndash;0.935] were not significant compared to those in the PI-RADS V2.1 [AUC\u0026thinsp;=\u0026thinsp;0.791, 95% CI\u0026thinsp;=\u0026thinsp;0.710\u0026ndash;0.873], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.309, 0.203, 0.298].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePI-RADS V2.1\u0026thinsp;=\u0026thinsp;3 lesions\u003c/h2\u003e \u003cp\u003eAnalysis of the ROC curve demonstrated that the PI-RADS 3 subgroup yielded the highest AUC for APTmax, which was 0.6 [95% CI (0.356\u0026ndash;0.844)] for distinguishing csPCa from benign lesions or cisPCa (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;4).\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study delves into the significance of this innovative functional MRI technique. When used in conjunction with PI-RADS V2.1, APT-weighted MRI helps to distinguish between csPCa and benign prostate lesions or cisPCa. It means APT-weighted imaging could provide additional value to PI-RADS V2.1. The combination of PI-RADS V2.1 and the APT max value yielded the most effective results in distinguishing csPCa from benign lesions or cisPCa in the whole prostate gland, with an AUC of 0.883. Moreover, the combined approach was successful at distinguishing csPCa from benign lesions or cisPCa in PZ (AUC\u0026thinsp;=\u0026thinsp;0.885 vs 0.798, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.036), indicating a significant improvement in performance. However, in TZ, the addition of APT-weighted signal values to PI-RADS V2.1 did not lead to a noteworthy improvement in diagnostic accuracy, with the AUC increasing from 0.791 to 0.853\u0026ndash;0.865 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.202).\u003c/p\u003e \u003cp\u003eWhile PI-RADS V2.1 showed good overall performance in diagnosing csPCa, its specificity for PZ csPCa was low, potentially leading to unnecessary biopsies[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In our evaluation, the highest Youden's index was obtained at a PI-RADS V2.1 specificity of 0.706. Currently, APT-weighted MRI has shown promise as a good predictor for detecting PCa in PZ[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Our study revealed that combining APTmax with the PI-RADS V2.1 score improved the specificity of diagnosing PZ csPCa and successfully enhanced the diagnostic efficiency. Moreover, APT signal values may help address the limitations of PI-RADS V2.1 and contribute positively to the specificity of identifying different csPCa lesions in PZ.\u003c/p\u003e \u003cp\u003eFor detecting csPCa in TZ, APT-weighted signals values were included in the multivariate model. It was found to be beneficial, albeit with a minor additional impact when combined with PI-RADS V2.1, in predicting csPCa in the TZ. According to the PI-RADS V2.1, T2WI is considered the most crucial sequence for identifying and characterizing prostate lesions in TZ, followed by DWI. However, the morphological features of TZ csPCa on T2WI in the context of PI-RADS V2.1 are subjective. Therefore, other quantitative parameters should be further analyzed to improve the diagnostic performance of PI-RADS V2.1 for the detection of TZ csPCa.\u003c/p\u003e \u003cp\u003eOur results indicated that APT max, APT mean, and APT min were independent predictors of csPCa, the APT-weighted signal values in patients with csPCa was generally greater than that in patients with benign prostate lesions or cisPCa. However, these conclusions are not consistent with the findings of Yang et al. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], who demonstrated that APTmax and APTmean could accurately distinguish between malignant and benign prostate lesions and that the differences in APTmin were not statistically significant. The reason could be that the samples were different. In the present study, it was diagnostic for csPCa but not for all PCa cases.\u003c/p\u003e \u003cp\u003eGuo et al[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] reported that APT-weighted signal value can provide more accurate lesion characterization for distinguishing TZ PCa from BPH, with an AUC of 0.812. According to the findings of Yin et al[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], APT imaging can be used to discriminate PCa from BPH, with an AUC of 0.8. Qin et al[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] reported that APT imaging performed well in PCa risk classification and had reproducible cutoff values in TZ and PZ. In line with our findings, these early results suggest that APT-weighted imaging is a promising valuable imaging marker for detecting PCa.\u003c/p\u003e \u003cp\u003eTheoretically, increased APT-weighted signal values indicate enhanced concentrations of proteins and peptides due to abnormal protein synthesis by rapidly dividing tumor cells and altered cellular metabolism in high-grade malignancies. Malignant prostate lesions exhibit more active metabolism than benign lesions, leading to a denser cell arrangement, reduced intercellular space, and greater secretion of macromolecules and peptides in csPCa, all of which correspond to elevated APT-weighted signal values[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe sub-analysis demonstrated moderate diagnostic accuracy, with APTmax values of 0.6 indicating the potential to differentiate csPCa in patients with PI-RADS 3 lesions. Although PI-RADS 3 lesions are commonly observed and exhibit a moderate to high risk of malignancy, their optimal treatment remains under investigation[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Stratifying these lesions using the PI-RADS V2.1 algorithm continues to be challenging. Our results indicated that APTmax exhibited high sensitivity and low specificity for PI-RADS 3 lesions, suggesting that APTmax has moderate diagnostic accuracy but a relatively high false-negative rate. This finding implies that functional MRI sequences such as APT imaging may not serve as effective stand-alone predictive markers for distinguishing benign and malignant prostate lesions within PI-RADS V2.1 3 lesions. Our preliminary findings provide initial support for utilizing APT-weighted imaging for stratifying PI-RADS 3 lesions.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eTo the best of our knowledge, this study is the first to assess the effectiveness of APT-weighted imaging in diagnosing csPCa compared to that of combination model (PI-RADS V2.1 and the 3D APT approach). The 3D APT sequence offers a comprehensive scan of the entire prostate area, a higher signal-to-noise ratio, and less image distortion than does the 2D APT sequence[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Notably, we also conducted an initial investigation into the diagnostic accuracy of APT-weighted signal values for detecting csPCa in patients with PI-RADS 3 lesions.\u003c/p\u003e \u003cp\u003eThere are a few potential limitations to consider. First, this study was retrospectively conducted at a single center, potentially leading to patient selection bias that may restrict generalizability. Hence, the current findings may require further validation in prospective multicenter studies involving a larger patient cohort. Second, using freehand ROI analysis may introduce artificial errors that could impact accuracy. Additionally, it is not possible to completely eliminate the risk of undetected necrosis or cystic changes, potentially contaminating the results. Utilizing methods such as histograms and iconography may offer more objectivity and enhance accuracy. Third, in some cases, the reference standard was MRI-based targeted biopsy via transrectal ultrasound, which could overlook potential lesions that were negative on MRI but positive on pathology.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eA combination model incorporating APT-weighted signal values and the PI-RADS V2.1 score may enhance the diagnostic efficacy for csPCa in the whole gland and PZ compared to PI-RADS V2.1 alone. However, no significant improvement in accuracy was noted for csPCa in the TZ. For lesions classified as PI-RADS V2.1, quantitative APT values may not be an effective parameter for diagnosing csPCa.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePCa Prostate cancer\u003c/p\u003e\n\u003cp\u003eAPT Amide proton transfer\u003c/p\u003e\n\u003cp\u003ePI-RADS V2.1 Prostate Imaging Reporting and Data System scoring system version 2.1\u003c/p\u003e\n\u003cp\u003ecsPCa Clinically significant prostate cancer\u003c/p\u003e\n\u003cp\u003eT2WI T2-weighted imaging\u003c/p\u003e\n\u003cp\u003eDWI Diffusion-weighted imaging\u003c/p\u003e\n\u003cp\u003eDCE Dynamic contrast-enhanced\u003c/p\u003e\n\u003cp\u003eROC Receiver operating characteristic\u003c/p\u003e\n\u003cp\u003eAUC Area under the curve\u003c/p\u003e\n\u003cp\u003eCisPCa Clinically insignificant prostate cancer\u003c/p\u003e\n\u003cp\u003ePZ Peripheral zone\u003c/p\u003e\n\u003cp\u003eTZ Transition zone\u003c/p\u003e\n\u003cp\u003eISUP International Society of Urological Pathology\u003c/p\u003e\n\u003cp\u003eMpMRI Multiparametric magnetic resonance imaging\u003c/p\u003e\n\u003cp\u003eICC Intraclass correlation coefficient\u003c/p\u003e\n\u003cp\u003eADC Apparent diffusion coefficient\u003c/p\u003e\n\u003cp\u003eREST Regional saturation technique\u003c/p\u003e\n\u003cp\u003eROI Region of interest\u003c/p\u003e\n\u003cp\u003eBPH Benign prostatic hyperplasia\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eL.Z, LC.L study concepts and design: X.Z literature research: J.Z, M.T clinical studies: J.Z, LC.L data analysis: L.Z manuscript preparation: XY.L, XL. D manuscript editing. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Shaanxi Provincial People\u0026apos;s Hospital Science and technology talent support plan (2021JY-43), Shaanxi Provincial People\u0026apos;s Hospital incubation Fund (2022YJY-13).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the ethics committee of the Shaanxi Provincial People\u0026apos;s Hospital, and all subjects signed an informed consent form before the examination, and all methods were carried out in accordance with relevant guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel RL, Miller KD, Fuchs HE, Jemal A. 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Eur J Radiol. 2020;129:109047. https://doi.org/10.1016/j.ejrad.2020.109047.\u003c/li\u003e\n\u003cli\u003eYang L, Wang L, Tan Y, Dan H, Xian P, Zhang Y, et al. Amide Proton Transfer-weighted MRI combined with serum prostate-specific antigen levels for differentiating malignant prostate lesions from benign prostate lesions: a retrospective cohort study. Cancer Imaging. 2023;23:3. https://doi.org/10.1186/s40644-022-00515-w.\u003c/li\u003e\n\u003cli\u003eGuo Z, Qin X, Mu R, Lv J, Meng Z, Zheng W, et al. Amide proton transfer could provide more accurate lesion characterization in the transition zone of the prostate. J Magn Reson Imaging. 2022 Nov;56:1311-1319. https://doi.org/10.1002/jmri.28204.\u003c/li\u003e\n\u003cli\u003eYin H, Wang D, Yan R, Jin X, Hu Y, Zhai Z, et al. Comparison of Diffusion Kurtosis Imaging and Amide Proton Transfer Imaging in the Diagnosis and Risk Assessment of Prostate Cancer. 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Korean J Radiol. 2021;22:1100-1109. https://doi.org/10.3348/kjr.2020.0837.\u003c/li\u003e\n\u003cli\u003eRudolph MM, Baur ADJ, Cash H, Haas M, Mahjoub S, Hartenstein A, et al. Diagnostic performance of PI-RADS version 2.1 compared to version 2.0 for detection of peripheral and transition zone prostate cancer. Sci Rep. 2020;10:15982. https://doi.org/10.1038/s41598-020-72544-z.\u003c/li\u003e\n\u003cli\u003eYang L, Wang L, Tan Y, Dan H, Xian P, Zhang Y, et al. Amide Proton Transfer-weighted MRI combined with serum prostate-specific antigen levels for differentiating malignant prostate lesions from benign prostate lesions: a retrospective cohort study. Cancer Imaging. 2023 Jan 7;23:3. https://doi.org/10.1186/s40644-022-00515-w. \u003c/li\u003e\n\u003cli\u003eGuo Z, Qin X, Mu R, Lv J, Meng Z, Zheng W, et al. Amide Proton Transfer Could Provide More Accurate Lesion Characterization in the Transition Zone of the Prostate. J Magn Reson Imaging. 2022;56:1311-1319. https://doi.org/10.1002/jmri.28204.\u003c/li\u003e\n\u003cli\u003eYin H, Wang D, Yan R, Jin X, Hu Y, Zhai Z, et al. Comparison of Diffusion Kurtosis Imaging and Amide Proton Transfer Imaging in the Diagnosis and Risk Assessment of Prostate Cancer. Front Oncol. 2021;11:640906. https://doi.org/10.3389/fonc.2021.640906.\u003c/li\u003e\n\u003cli\u003eQin X, Mu R, Zheng W, Li X, Liu F, Zhuang Z, et al. Comparison and combination of amide proton transfer magnetic resonance imaging and the apparent diffusion coefficient in differentiating the grades of prostate cancer. Quantitative Imaging In Medicine And Surgery.2022;13, 812-824.https://doi.org/10.21037/qims-22-721.\u003c/li\u003e\n\u003cli\u003eJia G, Abaza R, Williams JD, Zynger DL, Zhou J, Shah ZK, et al. Amide proton transfer MR imaging of prostate cancer: a preliminary study. 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Amide Proton Transfer-weighted MRI in Predicting Histologic Grade of Bladder Cancer [published correction appears in Radiology. Radiology. 2022;305:127-134. https://doi.org/10.1148/radiol.211804.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eImaging protocol parameters\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSacn protocol\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eScan sequences\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTR(ms)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTE(ms)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFOV(mm2)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSlice thickness(mm)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNumber of slices\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMatrix\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eScan time(min)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTSE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e582\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026times;\"\u003e\n\u003cp\u003e260\u0026times;260\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026times;\"\u003e\n\u003cp\u003e288\u0026times;228\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1:24\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eT2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTSE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2757\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e110\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026times;\"\u003e\n\u003cp\u003e260\u0026times;260\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026times;\"\u003e\n\u003cp\u003e432\u0026times;366\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2:12\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAPT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTSE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7280\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026times;\"\u003e\n\u003cp\u003e230\u0026times;180\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026times;\"\u003e\n\u003cp\u003e128\u0026times;100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4:29\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDWI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEPI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2982\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026times;\"\u003e\n\u003cp\u003e260\u0026times;260\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026times;\"\u003e\n\u003cp\u003e88\u0026times;86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3:38\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDCE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTFE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026times;\"\u003e\n\u003cp\u003e260\u0026times;260\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026times;\"\u003e\n\u003cp\u003e216\u0026times;217\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4:07\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eThe demographic and clinical characteristics of the participants\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCharacteristic\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ecsPCa(n\u0026thinsp;=\u0026thinsp;102)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eBenign or cisPCa(n\u0026thinsp;=\u0026thinsp;187)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge (year, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e73.11\u0026thinsp;\u0026plusmn;\u0026thinsp;9.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69.41\u0026thinsp;\u0026plusmn;\u0026thinsp;9.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003etPSA (ng/ml, IQR)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17.7(8.67,69.98)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.4(6.62,16.87)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLocation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePZ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTZ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e102\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGleason score\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u0026thinsp;+\u0026thinsp;3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u0026thinsp;+\u0026thinsp;4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u0026thinsp;+\u0026thinsp;5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u0026thinsp;+\u0026thinsp;3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u0026thinsp;+\u0026thinsp;4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u0026thinsp;+\u0026thinsp;5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u0026thinsp;+\u0026thinsp;3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u0026thinsp;+\u0026thinsp;4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u0026thinsp;+\u0026thinsp;5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTissue acquisition method\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCognitive fusion biopsy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e113\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRadical prostatectomy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePI-RADS V2.1 score\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eAbility of the quantitative parameters in differentiating csPCa from benign prostate diseases or cisPCa\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMalignant(%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eBenign or cisPCa(%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003et\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eParameters for PZ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAPT max\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e3.936\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9217\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e2.995\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8327\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-12.997\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAPTmean\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e3.019\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7856\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e2.221\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7715\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-12.534\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAPTmin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e2.094\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8355\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e1.315\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8365\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-11.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eParameters for TZ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAPT max\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e4.006\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1549\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e2.806\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6248\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-10.911\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAPTmean\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e3.119\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0055\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e1.973\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6912\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-11.593\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAPTmin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e2.097\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8660\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e1.076\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8867\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-10.835\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eParameters for Whole Gland\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAPT max\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e3.953\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9865\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e2.895\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7348\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-19.718\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAPTmean\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e3.045\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8480\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e2.089\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7396\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-19.363\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAPTmin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e2.095\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8426\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e1.188\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8711\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-16.985\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePI-RADS V2.1\u0026thinsp;=\u0026thinsp;3 lesions\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAPT max\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e2.940\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4925\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e2.795\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5402\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-1.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.316\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAPTmean\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e2.060\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5462\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e1.943\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5637\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.776\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.438\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAPTmin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e1.380\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7589\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e1.125\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7667\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-1.239\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.217\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eAnalysis of the ROC curves of the combinations of different quantitative parameters\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eWhole Gland\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAUC\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e95%CI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSensitivity\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSpecificity\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eYouden index J\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eThreshold\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePI-RADS V2.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.803\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e(0.757\u0026ndash;0.848)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.733\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.873\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.605\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePI-RADS V2.1\u0026thinsp;+\u0026thinsp;APT max\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.883\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e(0.842\u0026ndash;0.923)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.861\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.784\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.645\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.484\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.011\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePI-RADS V2.1\u0026thinsp;+\u0026thinsp;APT mean\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.877\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e(0.835\u0026ndash;0.919)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.843\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.813\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.656\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.389\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.019\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePI-RADS V2.1\u0026thinsp;+\u0026thinsp;APT min\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.874\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e(0.833\u0026ndash;0.916)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.824\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.797\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.621\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.409\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.023\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePZ\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePI-RADS V2.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.798\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e(0.738\u0026ndash;0.859)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.890\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.706\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.596\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePI-RADS V2.1\u0026thinsp;+\u0026thinsp;APT max\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" 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align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.835\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.629\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.578\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.062\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTZ\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePI-RADS V2.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.791\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e(0.710\u0026ndash;0.873)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.828\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.755\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.582\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePI-RADS V2.1\u0026thinsp;+\u0026thinsp;APT max\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.854\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e(0.766\u0026ndash;0.942)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.759\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.892\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.651\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.328\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.309\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePI-RADS V2.1\u0026thinsp;+\u0026thinsp;APT mean\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.865\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e(0.786\u0026ndash;0.944)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.828\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.833\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.661\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.240\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.203\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePI-RADS V2.1\u0026thinsp;+\u0026thinsp;APT min\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.853\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e(0.770\u0026ndash;0.935)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.724\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.902\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.626\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.426\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.298\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePI-RADS V2.1\u0026thinsp;=\u0026thinsp;3 lesions\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAPT max\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e(0.356\u0026ndash;0.844)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.302\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.302\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAPT mean\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.537\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e(0.299\u0026ndash;0.774)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.302\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.302\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAPT min\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.563\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e(0.321\u0026ndash;0.806)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.238\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.238\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Amide proton transfer-weighted MR image, Prostate-specific antigen, Clinically significant prostate cancer, PI-RADS V2.1, Benign lesions","lastPublishedDoi":"10.21203/rs.3.rs-4168033/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4168033/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe goal of this study was to assess whether combining amide proton transfer (APT)-weighted MRI with the Prostate Imaging Reporting and Data System scoring system version 2.1 (PI-RADS V2.1) could increase diagnostic accuracy compared to PI-RADS V2.1 alone in predicting clinically significant prostate cancer (csPCa).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study retrospectively analyzed data from patients who underwent prostate magnetic resonance imaging(MRI) examinations from July 2022 to August 2023. All patients underwent T2-weighted imaging (T2WI), amide proton transfer (APT), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) MRI. Two radiologists independently examined the images. The independent samples t test or the Wilcoxon rank sum test was employed to investigate the statistical variance in the demographic and APT parameters of the two groups. We utilized receiver operating characteristic (ROC) curve analysis to assess the diagnostic accuracy of PI-RADS V2.1 and the combination model (APT-weighted signal values and PI-RADS V2.1). The comparison of the area under the curve (AUC)s were conducted using the Delong method.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 289 patients were eventually included in this study; 102 had csPCa, and 187 had either benign lesions or clinically insignificant prostate cancer (cisPCa). The APTmean, APTmax, and APTmin values were significantly different between the two groups in both the peripheral zone (PZ) and transition zone (TZ). The combined models were significantly more effective than the use of PI-RADS V2.1 alone for the whole gland and PZ, with areas under the curve (AUC)s of 0.874–0.883 compared to 0.803 and 0.885 compared to 0.798, respectively (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). However, there was no substantial improvement in diagnostic accuracy when APT-weighted signal values were incorporated into PI-RADS V2.1 for the TZ, as the AUC increased from 0.791 to 0.865, with a \u003cem\u003eP\u003c/em\u003e value of 0.202.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBy incorporating APT-weighted signal values with PI-RADS V2.1, there was a notable improvement in the diagnostic accuracy of csPCa detection in both the whole gland and the PZ compared to PI-RADS V2.1 alone. However, there was no significant enhancement in terms of csPCa in TZ.\u003c/p\u003e","manuscriptTitle":"Combination of PI-RADS version 2.1 and amide proton transfer values for the detection of clinically significant prostate cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-04 17:22:18","doi":"10.21203/rs.3.rs-4168033/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-11T02:27:23+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-05T11:01:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"156056932150006115935715763033257388058","date":"2024-11-26T09:23:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-23T17:43:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"213617620425312615246370616783581706786","date":"2024-10-19T08:55:52+00:00","index":"hide","fulltext":""},{"type":"editorInvited","content":"","date":"2024-09-25T09:05:50+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"228161934567367831087981597101136552508","date":"2024-06-22T21:48:54+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-17T17:28:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-30T02:38:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-29T13:58:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2024-03-26T07:58:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"70e1b392-e4e8-44df-9c55-4ade73dff171","owner":[],"postedDate":"April 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-04T16:40:56+00:00","versionOfRecord":{"articleIdentity":"rs-4168033","link":"https://doi.org/10.1186/s12885-025-14610-1","journal":{"identity":"bmc-cancer","isVorOnly":false,"title":"BMC Cancer"},"publishedOn":"2025-07-30 16:13:02","publishedOnDateReadable":"July 30th, 2025"},"versionCreatedAt":"2024-04-04 17:22:18","video":"","vorDoi":"10.1186/s12885-025-14610-1","vorDoiUrl":"https://doi.org/10.1186/s12885-025-14610-1","workflowStages":[]},"version":"v1","identity":"rs-4168033","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4168033","identity":"rs-4168033","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}