Multiparametric magnetic resonance imaging (MRI) integrating amide proton transfer-weighted (APTw) imaging, synthetic MRI, and multiplexed sensitivity encoding diffusion-weighted imaging (MUSE DWI) in the diagnosis and grading of prostate cancer

Introduction

Prostate cancer (PCa) is one of the most common malignancies affecting men worldwide, ranking second in incidence and fifth in cancer-related mortality among male malignancies. With the global trend of population aging, the incidence of PCa continues to rise annually (1). Currently, prostate-specific antigen (PSA) level and magnetic resonance imaging (MRI) are primary examination methods used in clinical practice for PCa screening, diagnosis, and management (2,3). However, despite their widespread clinical use, the diagnostic performance of PSA and conventional MRI still have limitations in both sensitivity and specificity, particularly in distinguishing malignant from benign lesions, including benign prostatic hyperplasia (BPH) and prostatitis (4,5).

Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1) highlights diffusion-weighted imaging (DWI) as the principal sequence for evaluating suspicious prostate peripheral zone (PZ) lesions (6). DWI, commonly performed using single-shot echo planar imaging (ss-EPI), is a widely utilized technique for characterizing prostate lesions, as it reflects the diffusion restriction associated with increased cellular density (7). Nevertheless, ss-EPI-based DWI is susceptible to geometric distortions, susceptibility artifacts, and poor spatial resolution, especially in regions near air-tissue interfaces (8). To overcome these challenges, the multiplexed sensitivity encoding (MUSE) technique has been introduced. By correcting linear and nonlinear phase variations in multi-shot segmented EPI acquisitions, MUSE DWI improves spatial resolution, signal-to-noise ratio (SNR), and reduces distortion artifacts (9). Prior studies have demonstrated the utility of MUSE DWI in various organs, including the brain, breast, liver, pancreas, and prostate (10-14). However, high DWI value and low apparent diffusion coefficient (ADC) value can also be observed in chronic prostatitis, which complicates the differentiation from early-stage PCa (15).

Synthetic magnetic resonance imaging (SyMRI) is an advanced imaging technique that enables the simultaneous generation of multiple contrast-weighted images [e.g., T1-, T2-weighted, fluid-attenuated inversion recovery (FLAIR)] and quantitative maps of longitudinal relaxation time (T1), transverse relaxation time (T2), and proton density (PD) through a single acquisition (16,17). This quantitative approach facilitates the objective evaluation and visualization of tissue characteristics. The applicability of SyMRI has been demonstrated in a variety of medical conditions, such as breast cancer, rectal cancer, sinonasal lesions, and PCa (18-23). PI-RADS v2.1 considers T2-weighted imaging (T2WI) the most important sequence to detect prostate transitional zone (TZ) lesions (14). SyMRI-derived T2 maps allow for quantifiable assessment, but prior studies have suggested that individual quantitative parameters derived from SyMRI alone may not provide sufficient diagnostic accuracy for PCa (24).

Chemical exchange saturation transfer (CEST) imaging, particularly the amide proton transfer-weighted (APTw) imaging, is a molecular imaging technique that detects endogenous mobile proteins and peptides by exploiting the chemical exchange between amide protons and bulk water (25,26). CEST APTw imaging offers metabolic-level information without the use of exogenous contrast agents and has shown potential as a biomarker in various malignancies (27-31). In the context of PCa, elevated APTw signals have been associated with increased cellular proliferation and protein synthesis, suggesting its value in tumor characterization (29,32,33). However, most prior studies have focused on isolated APTw parameters, without exploring multiparametric integration.

To date, studies evaluating SyMRI, APTw imaging, or MUSE DWI have primarily examined these techniques in isolation. The potential diagnostic advantage of integrating these advanced MRI techniques has not been systematically investigated. Therefore, this exploratory study aimed to assess the performance of a multiparametric MRI approach that combines SyMRI, MUSE DWI, and APTw imaging for the diagnosis and grading of PCa, leveraging the complementary strengths of structural, diffusion, and metabolic imaging, to improve biopsy decision-making. We present this article in accordance with the STARD reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-aw-2307/rc).

Methods

Patient population

A total of 302 patients with clinically suspected PCa were consecutively enrolled in the First Affiliated Hospital of Soochow University between October 2023 and March 2025. All participants underwent SyMRI, APTw imaging, and MUSE DWI in addition to standard-of-care MRI. The inclusion criteria were as follows: (I) PSA levels above 4 ng/mL and clinical suspicion of PCa at digital rectal or ultrasound (US) examination; (II) complete sequence of SyMRI, APTw imaging, and MUSE DWI; (III) acceptable image quality of all three modalities for analysis; and (IV) MRI examinations performed within three months before transrectal US/MRI fusion targeted biopsy. The exclusion criteria were as follows: (I) imaging data unavailable or poor image quality (n=10); (II) prior prostate-related treatment before the MRI scan (n=5); and (III) absence of histopathological confirmation within three months following the MRI scan (n=92). Finally, 195 patients with pathologically confirmed prostate lesions were included in this study. The flow diagram of the patient selection is shown in Figure 1. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Board of the First Affiliated Hospital of Soochow University (No. 2025477). The requirement for informed consent was waived in this retrospective study.

Figure 1 The study flowchart of patient selection. APTw, amide proton transfer-weighted; BPH, benign prostatic hyperplasia;DWI, diffusion-weighted imaging; ISUP, international society of urological pathology; MRI, magnetic resonance imaging; MUSE, multiplexed sensitivity encoding; PCa, prostate cancer; PSA, prostate-specific antigen; PZ, peripheral zone; SyMRI, synthetic magnetic resonance imaging; TZ, transitional zone; US, ultrasound.

MRI acquisition

All MRI scans were conducted on a 3.0T MR scanner (SIGNA Premier, GE HealthCare, Chicago, IL, USA) with a 30-channel body phased-array coil. SyMRI images were acquired using the MAGnetic resonance image Compilation (MAGiC) sequence. Additional sequences included APTw imaging and MUSE DWI. Detailed scan parameters for each sequence are summarized in Table 1. The total acquisition time was approximately 9 minutes and 54 seconds.

Image analysis

All images were transferred to the dedicated post-processing workstation (AW 4.7 workstation, GE HealthCare). Quantitative maps of T1, T2, and PD were generated using the MAGiC software (version 100.3; GE HealthCare). APTw asymmetry maps were reconstructed from the APTw sequence automatically. ADC maps were calculated based on mono-exponential fitting of low- and intermediate-b-value MUSE DWI images.

Two experienced radiologists (Reader 1 with 5 years and Reader 2 with 15 years of experience in prostate MRI interpretation) independently performed region of interest (ROI) delineation. Both readers were blinded to clinical and histopathological results. Two radiologists reviewed and discussed all of the magnetic resonance images. Each patient selected the most suspicious lesion. One lesion per patient was analyzed to ensure statistical independence. Two radiologists scored the lesions according to the PI-RADS v2.1 standard. In case of any disagreement, they reached a consensus through consultation. ROIs were manually drawn on the slice demonstrating the maximum axial diameter of lesion, referencing T2-weighted images for anatomical guidance, and propagated onto the corresponding quantitative maps (T1, T2, PD, APTw, and ADC). The ROIs were drawn to encompass the lesion as fully as possible while avoiding areas of necrosis or artifacts. Discrepancies between readers were resolved by consensus, and quantitative values measured by the senior reader were used for subsequent analysis.

Statistical analysis

The software SPSS 27.0 (IBM Corp., Armonk, NY, USA) and MedCalc version 22.021 (MedCalc Software, Ostend, Belgium) were used for statistical analyses. The intraclass correlation coefficient (ICC) was used to evaluate the interobserver agreement of all metrics and classified as excellent (>0.80), good (0.60–0.79), fair (0.40–0.59), and poor (<0.40). The normality of the distribution for each parameter was assessed using the Shapiro-Wilk test. Continuous data were expressed as mean ± standard deviation (SD) or median (first quartile, third quartile), depending on whether the data followed a normal distribution. Differences between benign and malignant lesions in PZ and TZ were evaluated using either the independent sample t-test or the Mann-Whitney U test, depending on data distribution. Parametric variables were correlated with the International Society of Urological Pathology (ISUP) grades using the Spearman rank correlation coefficient. A one-way analysis of variance (ANOVA) test with Bonferroni correction or Kruskal-Wallis test was performed to determine the differences of each parameter among different ISUP grades. Univariate and multivariate logistic regression analyses were used to analyze the correlation between parameters.

For parameters with statistically significant differences in univariate analysis, logistic regression analysis was used to estimate the probability of the combined parameters. The parameters with statistically significant differences were modeled using binary logistic regression analysis, and the probability of the combined parameters was calculated. Receiver operating characteristic (ROC) curve analysis was conducted to assess diagnostic performance of individual and combined parameters. The optimal cut-off value was determined using the Youden index (sensitivity + specificity − 1). The areas under the curves (AUCs), 95% confidence interval (CI), along with sensitivity, specificity were calculated for each variable. Bootstrap CIs [2,000 resamples, bias-corrected and accelerated (BCa) method] were calculated for all AUCs. Comparisons between AUCs were conducted using DeLong’s test to evaluate statistical significance between diagnostic models. A P value <0.05 was considered statistically significant.

Results

Participant characteristics

A total of 195 patients (mean age ± SD: 67.72±7.61) with histopathologically confirmed prostate lesions were included in the final analysis, comprising 96 malignant and 99 benign cases. The baseline clinical characteristics are summarized in Table 2. No significant difference was observed in age and ROI size between the malignant and benign groups (P>0.05). However, total prostate-specific antigen (tPSA) levels were significantly higher in the malignant group compared to the benign group (P<0.05).

Interobserver agreement

ICC consistency was performed for all the measurement data by the two readers. As shown in Table S1, excellent interobserver agreement was achieved for all parameters, including T1 (ICC =0.980, 95% CI: 0.974–0.985), T2 (ICC =0.962, 95% CI: 0.950–0.972), PD (ICC =0.972, 95% CI: 0.962–0.979), ADC (ICC =0.996, 95% CI: 0.995–0.997), APT_min (ICC =0.965, 95% CI: 0.954–0.974), APT_max (ICC =0.976, 95% CI: 0.969–0.982), and APT_mean (ICC =0.984, 95% CI: 0.978–0.988), indicating high measurement reliability.

Comparison of quantitative parameters between benign and malignant groups

Quantitative parameters for benign and malignant lesions in different zones of prostate are demonstrated in Table 3. T1, T2, and ADC values of PCa were significantly lower, whereas the APTw parameters were significantly higher than those of BPH (P<0.05) in the TZ. PCa and BPH did not show significant differences in PD value (P>0.05). For the PZ, the T1, T2, and ADC values of PCa were significantly lower, whereas the APTw parameters were significantly higher than those of prostatitis (P<0.05). However, no statistically significant difference was observed in PD value between PCa and prostatitis (P>0.05).

Regression analyses

Univariate analyses displayed that APT_max, APT_min, APT_mean, T1, T2, and ADC values were all favorable for the discrimination between PCa and benign prostate lesions. The multivariate analyses revealed that APT_max and ADC value were independent predictors for evaluating TZ PCa, whereas ADC value was an independent predictor for evaluating PZ PCa. In addition, ADC value was an independent predictor for evaluating high-grade PCa (Table 4).

Diagnostic performance of quantitative parameters and combined diagnostic models for benign and malignant prostate lesions

For the diagnosis of TZ lesions, ROC analysis demonstrated that the AUCs of individual MRI parameters ranged from 0.655 to 0.768. Among these parameters, APT_min yielded the lowest AUC of 0.655 (95% CI: 0.538–0.756), whereas APT_max showed the highest AUC of 0.768 (95% CI: 0.663–0.853). The APT + SyMRI + ADC diagnostic model achieved an AUC of 0.846 (95% CI: 0.754–0.910). Compared to individual MRI parameters and PI-RADS, the model demonstrated a statistically significant improvement in diagnostic performance (DeLong test: P<0.05). ROC analysis demonstrated that the AUCs of individual MRI parameters for diagnosing PZ lesions ranged from 0.722 to 0.896. ADC value exhibited the highest diagnostic performance (AUC =0.896, 95% CI: 0.784–0.957), whereas APT_min showed the lowest diagnostic performance (AUC =0.722, 95% CI: 0.593–0.818). The APT + SyMRI + ADC diagnostic model achieved an AUC of 0.948 (95% CI: 0.878–0.979). Compared to APTw, SyMRI parameters, and PI-RADS, the model demonstrated significantly improved diagnostic performance (DeLong test: all P<0.05). However, there was no significant difference in the AUC value between the ADC value and the model in differentiating PCa from prostatitis (DeLong test: P>0.05) (Tables 5,6, Figure 2).

Figure 2 ROC curves of the parameters and combined models for distinguishing benign lesions from PCa. (A) ROC curves of the parameters for distinguishing BPH from PCa. (B) ROCs of combined models for distinguishing BPH from PCa. (C) ROCs of the parameters for distinguishing prostatitis from PCa. (D) ROC curves of combined models for distinguishing prostatitis from PCa. SyMRI, T1 + T2; APT, APT_min + APT_max + APT_mean. ADC, apparent diffusion coefficient; APT, amide proton transfer; BPH, benign prostatic hyperplasia; PCa, prostate cancer; PI-RADS, Prostate Imaging Reporting and Data System; ROC, receiver operating characteristic; SyMRI, synthetic magnetic resonance imaging; T1, T1 relaxation time; T2, T2 relaxation time.

Value of quantitative parameters in PCa grade

With increasing ISUP grade of PCa, APTw parameters showed an upward trend, whereas SyMRI parameters and ADC value exhibited a downward trend. Among APTw parameters, APT_max and APT_mean showed significant positive correlations with ISUP grade (r=0.419, P<0.001; r=0.311, P=0.002), whereas APT_min demonstrated no significant correlation with ISUP grade (r=0.138, P=0.181). For SyMRI parameters, a negative correlation was found between T2 value and ISUP grade (r=−0.285, P=0.005). However, no significant correlations were observed for T1 and PD values with ISUP grade (r=−0.136, −0.131; P=0.188, 0.202, respectively). Moreover, ADC value demonstrated a significant negative correlation with ISUP grade (r=−0.495, P<0.001) (Table 7).

The APT_max and APT_mean of PCa lesions with ISUP grade 4 were significantly higher than those of PCa with ISUP grade 1 (P<0.05). The T2 value of PCa with ISUP grade 4 was significantly lower than that of PCa with ISUP grade 1 (P=0.048). ADC value of PCa with ISUP grade 1 were significantly higher than that of PCa with ISUP grades 2–5 (P<0.05). There were no significant differences of T1 and PD values among PCa with different ISUP grades (P>0.05).

Furthermore, low-grade PCa (ISUP grade ≤2) showed significantly lower APT_max and APT_mean, while exhibiting significantly higher T2 and ADC values compared to high-grade PCa (ISUP grade ≥3) (Figures 3,4). The combined model, developed by integrating individual quantitative parameters, achieved an AUC of 0.785 (95% CI: 0.670–0.866). The diagnostic performance showed statistically significant improvement compared to APT_mean (AUC =0.668; 95% CI: 0.551–0.766) and T2 value (AUC =0.619; 95% CI: 0.501–0.727) (DeLong test: P<0.05). However, compared to APT_max (AUC =0.729; 95% CI: 0.616–0.821) and ADC value (AUC =0.728; 95% CI: 0.619–0.818), the model did not improve diagnostic performance in differentiating low-grade and high-grade PCa (DeLong test: P>0.05) (Table 6, Figure 5).

Figure 3 Box plots showing the comparisons (A-C) APT parameters (%), (D) T1 value (ms), (E) T2 value (ms), (F) PD value (%), and (G) ADC value (10⁻⁶ mm²/s) in low- and high-grade PCa. The differences between APT_min, T1, and PD values were analyzed using the independent sample t-test. The differences between APT_max, APT_mean, T2, and ADC values were analyzed using the Mann-Whitney U test. *, P<0.05; **, P<0.01; ***, P<0.001. ADC, apparent diffusion coefficient; APT, amide proton transfer; PCa, prostate cancer; PD, proton density; T1, T1 relaxation time; T2, T2 relaxation time.

Figure 4 Images of PCa. (A) PZ PCa with ISUP grade 1. (A1) T2WI; (A2) ADC map (ADC =0.93×10⁻³ mm²/s); (A3) APTw map (APT_min =14%, APT_max =34%, APT_mean =23.12%); (A4) T1 mapping (T1 =1,282 ms); (A5) T2 mapping (T2 =91 ms); (A6) PD mapping (PD =82.2%). (B) PZ PCa with ISUP grade 4. (B1) T2WI; (B2) ADC map (ADC =0.76×10^-3 mm²/s); (B3) APTw map (APT_min =20%, APT_max =34%, APT_mean =26.05%); (B4) T1 mapping (T1 =1,345 ms); (B5) T2 mapping (T2 =77 ms); (B6) PD mapping (PD =74.8%). ADC, apparent diffusion coefficient; APTw, amide proton transfer-weighted; ISUP, international society of urological pathology; PCa, prostate cancer; PD, proton density; PZ, peripheral zone; T1, T1 relaxation time; T2, T2 relaxation time; T2WI, T2-weighted imaging.

Figure 5 ROC curves of the parameters and combined model for distinguishing low- and high-grade PCa. Combined model: ADC + APT_max + APT_mean. ADC, apparent diffusion coefficient; APT, amide proton transfer; PCa, prostate cancer; ROC, receiver operating characteristic; T2, T2 relaxation time.

Discussion

This study evaluated the utility of multiparametric MRI incorporating SyMRI, APTw imaging, and MUSE DWI in differentiating PCa from benign prostate lesions and grading of PCa. Our findings demonstrated that T1, T2, and ADC values were significantly lower in PCa than in benign prostate lesions, whereas APTw-derived parameters (APT_min, APT_max, APT_mean) were significantly higher compared to benign lesions. In contrast, PD value did not show a significant difference. Among individual parameters, APT_max yielded the highest diagnostic performance for the diagnostic of TZ PCa, and ADC value yielded the highest diagnostic performance for the diagnosis of PZ PCa. In the TZ, the diagnostic performance of the APT + SyMRI + ADC model was significantly improved compared to each single parameter. For the diagnosis of PZ PCa, the diagnostic model was similar to the ADC value. With increasing ISUP grading of PCa, APT_max and APT_min showed a significant upward trend, whereas T2 and ADC values exhibited a significant downward trend.

SyMRI in the diagnosis and grading of PCa

T2 relaxation time is a well-established biomarker reflecting tissue water content. In malignant tumors, increased cellular density and reduced extracellular space typically lead to decreased water mobility and shorter T2 value (24,34). This phenomenon has been documented in brain, breast, rectal, sinonasal, and prostate malignancies (17-20,24,35-37). In our study, the significantly lower T2 value in PCa supports this hypothesis. The observed reduction may result from two key pathological features: (I) elevated tumor cell proliferation leading to decreased interstitial water, and (II) architectural disruption of prostatic glands, with loss of glandular lumina and reduced acinar fluid content, contributing to overall water reduction (38). We have noticed that previous research has demonstrated a significant correlation between T2 value and PCa grade (24). This phenomenon was similarly validated in our study. As the ISUP grade increases, the close packing of tumor cells leads to a reduction in tissue water content, resulting in a downward trend in T2 value (24). Additionally, T2 value was significantly lower in high-grade PCa. This phenomenon may be attributed to the cellular density of PCa tissue.

T1 and PD values are two additional quantitative parameters derived from SyMRI that reflect the characteristics of tissue relaxation. Although T1 mapping has primarily been applied to pharmacokinetic modeling in dynamic contrast-enhanced (DCE) MRI, its direct diagnostic role in PCa remains less explored (39). We found that T1 value in PCa was significantly lower than that of BPH and prostatitis. However, T1 value demonstrated certain limitations in the grading of PCa. Baur et al. (40) reported that T1 value in PCa was significantly lower than that in both PZ and TZ. Cao et al. (41) demonstrated that in the TZ, T1 value in PCa was significantly lower than that of non-cancerous tissue. However, in the PZ, there was no significant difference in T1 value between PCa and non-cancerous tissue. Due to the differences in T1 mapping techniques and the selected ROI applied in various studies, the application of T1 mapping in PCa still needs to be verified.

PD value has often been used to assess water content in neuroimaging (10). However, in our study, it did not significantly differ between PCa and benign lesions, though PD value in PCa was lower than those in benign prostate lesions, which is consistent with previous studies (17,42). This may be due to the relatively small magnitude of free water content change in PCa, which limits the sensitivity of PD mapping in this context.

APTw in the diagnosis and grading of PCa

CEST APTw imaging is a molecular imaging technique that non-invasively quantifies endogenous mobile proteins and peptides via amide proton exchange with bulk water (43). This method provides indirect information on tissue metabolism, cellular proliferation, and microenvironmental pH. Malignant tumors are often characterized by high protein turnover and structural atypia, making APTw a promising tool for tumor characterization (44). In previous studies, CEST APTw imaging has been shown to have potential value in the differential diagnosis of nervous system tumors, breast cancer, bladder cancer, and PCa (29-33,45-47). In our study, for the PZ and TZ, all APTw-derived parameters were significantly elevated in PCa, which may be attributed to increased protein synthesis and microvascular density (48). Importantly, combining APTw with SyMRI further enhanced diagnostic performance, reinforcing the complementary role of metabolic imaging in conjunction with structural mapping. This synergy highlights the potential clinical utility of CEST APTw imaging for PCa characterization. This utility is particularly valuable in equivocal cases where conventional parameters are inconclusive.

Theoretically, as the grade increases, tumor differentiation becomes progressively lower, with increasing proliferation rates, cellular density, and nuclear-to-cytoplasmic ratios (49,50). These pathological changes lead to elevated levels of proteins and polypeptides in high-grade PCa, which may consequently drive an upward trend in APTw parameters. In our study, as the ISUP grade increased, APT_max and APT_mean exhibited a significant upward trend. Moreover, both were significantly higher in high-grade PCa, which was similar to previous reports (29,32). This phenomenon may be related to the following factors: as the ISUP grade increases, the differentiation degree of PCa decreases, resulting in a significant increase in the maximum protein content, average protein content, and polypeptide levels in the cancer tissue (32). APT_max and APT_mean demonstrated significant value in detecting high-grade PCa, suggesting that both the APT_max and APT_mean within lesions can effectively predict aggressiveness of PCa. The restricted diagnostic utility of APT_min in PCa grade may be attributed to the selection of ROIs and the intratumoral heterogeneity. Two-dimensional (2D)-ROIs cannot include all parts of lesions, which leads to an inability to fully reflect the overall levels of mobile proteins and peptides in PCa. Furthermore, the limited elevation of APT_min across different PCa grades may consequently result in no statistically significant difference between high- and low-grade PCa.

However, Takayama et al. (33) revealed that as the degree of differentiation of PCa decreased, the APT value initially increased and then decreased. Moreover, the APT value in PCa with Gleason score 7 was significantly higher than that in other Gleason score groups. This might be related to the different application of APTw imaging techniques in various studies as well as the selection of the ROIs. Furthermore, APTw imaging is susceptible to motion and fat artifacts, especially in the application of abdominal organs, which may result in the APT value at the lesion site being not pure, thereby affecting its diagnostic efficacy (51).

MUSE DWI in the diagnosis and grading of PCa

DWI remains a cornerstone of PCa imaging, as it reflects the restricted diffusion of water molecules in hypercellular tumor environments (52). MUSE DWI, by correcting for phase inconsistencies and reducing susceptibility artifacts, enables high-resolution DWI acquisition with reduced distortion (53,54). In our study, ADC value derived from MUSE DWI achieved the highest AUC among all individual metrics. Furthermore, ADC value based on MUSE DWI demonstrated excellent performance in the diagnosis and grading of PCa, indicating significant clinical applicability. However, we also observed overlapping low ADC value in some cases of chronic prostatitis and early-stage PCa, underscoring the need for multiparametric approaches in challenging cases.

A previous study showed that although MUSE DWI offered better anatomical visualization and overall image quality, there was no significant difference in the imaging performance of lesions between MUSE DWI and conventional DWI scan (10). This might be because PCa lesions exhibit high contrast, making them clearly visible even when image quality and resolution are suboptimal. Nakamoto et al. (10) indicated that since rectal gas often caused image distortion, conventional DWI usually resulted in unavoidable artifacts. MUSE DWI had a unique advantage in delineating the posterior lesions of prostate.

Combined diagnostic models in PCa

We established integrated diagnostic models incorporating SyMRI-derived parameters, APTw parameters, and ADC value obtained from MUSE DWI post-processing to evaluate both the diagnosis and grading of PCa. A notable advantage of the proposed imaging protocol is the complete avoidance of exogenous contrast agents. All sequences—MAGiC (SyMRI), CEST APTw, and MUSE DWI—are non-contrast techniques, offering safer alternatives for patients with renal insufficiency or contrast allergies. Moreover, these sequences reflect distinct biological properties: SyMRI quantifies water content and tissue relaxation; APTw imaging captures protein metabolism; and DWI assesses water diffusion. Together, they provide a comprehensive characterization of PCa at a microstructural and molecular level. For the differential diagnosis of PCa, the APT + SyMRI + ADC diagnostic model demonstrated significantly superior diagnostic performance in detecting TZ PCa. In the PZ, the combined diagnostic model demonstrated significantly superior diagnostic performance compared to APTw parameters and SyMRI-derived parameters. However, compared to ADC value, the combined model did not show significant improvement in the ability to distinguish PCa from prostatitis. For the detection of high-grade PCa, the diagnostic performance improvement of the combined model was limited. This might be due to the mutual influence among different parameters. For instance, high cell density increases the protein level while reducing the extracellular fluid space, which limits the improvement of the diagnostic performance of the combined model.

PI-RADS is currently the commonly used standard for evaluating benign and malignant lesions of the prostate (6). However, PI-RADS is often influenced by the quality of MRI images and the subjective judgment and experience of radiologists, which usually leads to diagnostic deviations (5). At the same time, for some small lesions, PI-RADS may cause missed diagnoses and misdiagnoses (55). In our study, for the diagnosis for TZ and PZ PCa, the diagnostic performance of the SyMRI + APT + ADC combined diagnostic model was significantly superior to that of PI-RADS. This indicates that the multiparametric MRI based on SyMRI, APTw imaging, and MUSE DWI has great significance for the differential diagnosis of benign and malignant lesions of TZ and PZ of the prostate. For junior radiologists, this quantitative diagnostic scheme may reduce the occurrence of missed diagnoses and misdiagnoses.

Several limitations should be acknowledged. Firstly, this was a single-center study with a relatively modest sample size, and employed 3.0T magnetic resonance scanning, which may have introduced selection bias. Multicenter validation with larger cohorts is needed to confirm generalizability. Secondly, ROIs were manually delineated on a single 2D slice representing the largest cross-sectional area of the lesion, which may introduce sampling bias. Although efforts were made to include as much of the tumor area as possible, the inherent intratumoral heterogeneity could not be fully captured by this 2D ROI approach. As a result, the derived quantitative parameters primarily reflect overall trends rather than localized heterogeneity within the tumor. Addressing this limitation will require future studies incorporating three-dimensional ROI delineation and spatial heterogeneity analysis to better characterize the complex biological features of PCa. Last but not least, no external validation cohort was available. Model calibration and clinical decision curve analysis were not performed. Future prospective studies should assess whether this multiparametric approach changes clinical management decisions.

Conclusions

Our study demonstrates that multiparametric MRI combining SyMRI, APTw imaging, and MUSE DWI provides superior performance in diagnosis and grading of PCa. This non-contrast-enhanced, comprehensive imaging approach captures complementary tissue characteristics—relaxation properties, protein metabolism, and water diffusion—offering a promising diagnostic tool for improving the accuracy and safety of PCa evaluation.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STARD reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-aw-2307/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-aw-2307/dss

Funding: This work was supported by the National Natural Science Foundation of China (No. 82402227) and Suzhou Medical and Health Technology Innovation Project (No. SKY2022003).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-aw-2307/coif). J.S. is an employee of GE Healthcare. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the ethics board of the First Affiliated Hospital of Soochow University (No. 2025477). Informed consent was waived in this retrospective study.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2024;74:229-63. [Crossref] [PubMed]
2. Fazekas T, Shim SR, Basile G, Baboudjian M, Kói T, Przydacz M, Abufaraj M, Ploussard G, Kasivisvanathan V, Rivas JG, Gandaglia G, Szarvas T, Schoots IG, van den Bergh RCN, Leapman MS, Nyirády P, Shariat SF, Rajwa P. Magnetic Resonance Imaging in Prostate Cancer Screening: A Systematic Review and Meta-Analysis. JAMA Oncol 2024;10:745-54. [Crossref] [PubMed]
3. Pezaro C, Woo HH, Davis ID. Prostate cancer: measuring PSA. Intern Med J 2014;44:433-40. [Crossref] [PubMed]
4. Ilic D, Djulbegovic M, Jung JH, Hwang EC, Zhou Q, Cleves A, Agoritsas T, Dahm P. Prostate cancer screening with prostate-specific antigen (PSA) test: a systematic review and meta-analysis. BMJ 2018;362:k3519. [Crossref] [PubMed]
5. Zhen L, Liu X, Yegang C, Yongjiao Y, Yawei X, Jiaqi K, Xianhao W, Yuxuan S, Rui H, Wei Z, Ningjing O. Accuracy of multiparametric magnetic resonance imaging for diagnosing prostate Cancer: a systematic review and meta-analysis. BMC Cancer 2019;19:1244. [Crossref] [PubMed]
6. Turkbey B, Rosenkrantz AB, Haider MA, Padhani AR, Villeirs G, Macura KJ, Tempany CM, Choyke PL, Cornud F, Margolis DJ, Thoeny HC, Verma S, Barentsz J, Weinreb JC. Prostate Imaging Reporting and Data System Version 2.1: 2019 Update of Prostate Imaging Reporting and Data System Version 2. Eur Urol 2019;76:340-51. [Crossref] [PubMed]
7. Fukunaga T, Tamada T, Kanomata N, Takeuchi M, Ueda Y, Miyaji Y, Kido A, Yamamoto A, Sone T. Quantitative diffusion-weighted imaging and dynamic contrast-enhanced MR imaging for assessment of tumor aggressiveness in prostate cancer at 3T. Magn Reson Imaging 2021;83:152-9. [Crossref] [PubMed]
8. Reischauer C, Cancelli T, Malekzadeh S, Froehlich JM, Thoeny HC. How to improve image quality of DWI of the prostate-enema or catheter preparation? Eur Radiol 2021;31:6708-16. [Crossref] [PubMed]
9. Chen NK, Guidon A, Chang HC, Song AW. A robust multi-shot scan strategy for high-resolution diffusion weighted MRI enabled by multiplexed sensitivity-encoding (MUSE). Neuroimage 2013;72:41-7. [Crossref] [PubMed]
10. Nakamoto A, Onishi H, Tsuboyama T, Fukui H, Ota T, Yano K, Kiso K, Honda T, Tarewaki H, Koyama Y, Tatsumi M, Tomiyama N. High-resolution Diffusion-weighted Imaging of the Prostate Using Multiplexed Sensitivity-encoding: Comparison with the Conventional and Reduced Field-of-view Techniques. Magn Reson Med Sci 2025;24:58-65. [Crossref] [PubMed]
11. Wang W, Xu W, Hu S, Hu J. Comparative study of image quality, ADC, and IVIM data between multi-b value MUSE-DWI and SS-EPI-DWI in brain imaging. BMC Med Imaging 2025;25:431. [Crossref] [PubMed]
12. Daimiel Naranjo I, Lo Gullo R, Morris EA, Larowin T, Fung MM, Guidon A, Pinker K, Thakur SB. High-Spatial-Resolution Multishot Multiplexed Sensitivity-encoding Diffusion-weighted Imaging for Improved Quality of Breast Images and Differentiation of Breast Lesions: A Feasibility Study. Radiol Imaging Cancer 2020;2:e190076. [Crossref] [PubMed]
13. Kim YY, Kim MJ, Gho SM, Seo N. Comparison of multiplexed sensitivity encoding and single-shot echo-planar imaging for diffusion-weighted imaging of the liver. Eur J Radiol 2020;132:109292. [Crossref] [PubMed]
14. Bai Y, Pei Y, Liu WV, Liu W, Xie S, Wang X, Zhong L, Chen J, Zhang L, Masokano IB, Li W. MRI: Evaluating the Application of FOCUS-MUSE Diffusion-Weighted Imaging in the Pancreas in Comparison With FOCUS, MUSE, and Single-Shot DWIs. J Magn Reson Imaging 2023;57:1156-71. [Crossref] [PubMed]
15. Chatterjee A, Thomas S, Oto A. Prostate MR: pitfalls and benign lesions. Abdom Radiol (NY) 2020;45:2154-64. [Crossref] [PubMed]
16. Ji S, Yang D, Lee J, Choi SH, Kim H, Kang KM. Synthetic MRI: Technologies and Applications in Neuroradiology. J Magn Reson Imaging 2022;55:1013-25. [Crossref] [PubMed]
17. Cui Y, Han S, Liu M, Wu PY, Zhang W, Zhang J, Li C, Chen M. Diagnosis and Grading of Prostate Cancer by Relaxation Maps From Synthetic MRI. J Magn Reson Imaging 2020;52:552-64. [Crossref] [PubMed]
18. Li X, Fan Z, Jiang H, Niu J, Bian W, Wang C, Wang Y, Zhang R, Zhang H. Synthetic MRI in breast cancer: differentiating benign from malignant lesions and predicting immunohistochemical expression status. Sci Rep 2023;13:17978. [Crossref] [PubMed]
19. Zhu K, Chen Z, Cui L, Zhao J, Liu Y, Cao J. The Preoperative Diagnostic Performance of Multi-Parametric Quantitative Assessment in Rectal Carcinoma: A Preliminary Study Using Synthetic Magnetic Resonance Imaging. Front Oncol 2022;12:682003. [Crossref] [PubMed]
20. Xiang Y, Zhang Q, Chen X, Sun H, Li X, Wei X, Zhong J, Gao B, Huang W, Liang W, Sun H, Yang Q, Ren X. Synthetic MRI and amide proton transfer-weighted MRI for differentiating between benign and malignant sinonasal lesions. Eur Radiol 2024;34:6820-30. [Crossref] [PubMed]
21. Arita Y, Takahara T, Yoshida S, Kwee TC, Yajima S, Ishii C, Ishii R, Okuda S, Jinzaki M, Fujii Y. Quantitative Assessment of Bone Metastasis in Prostate Cancer Using Synthetic Magnetic Resonance Imaging. Invest Radiol 2019;54:638-44. [Crossref] [PubMed]
22. Li M, Fu W, Ouyang L, Cai Q, Huang Y, Yang X, Pan W, Qian L, Guo Y, Wang H. Potential clinical feasibility of synthetic MRI in bladder tumors: a comparative study with conventional MRI. Quant Imaging Med Surg 2023;13:5109-18. [Crossref] [PubMed]
23. Gao Z, Xu X, Sun H, Li T, Ding W, Duan Y, Tang L, Gu Y. The value of synthetic magnetic resonance imaging in the diagnosis and assessment of prostate cancer aggressiveness. Quant Imaging Med Surg 2024;14:5473-89. [Crossref] [PubMed]
24. Mai J, Abubrig M, Lehmann T, Hilbert T, Weiland E, Grimm MO, Teichgräber U, Franiel T. T2 Mapping in Prostate Cancer. Invest Radiol 2019;54:146-52. [Crossref] [PubMed]
25. van Zijl PC, Yadav NN. Chemical exchange saturation transfer (CEST): what is in a name and what isn’t? Magn Reson Med 2011;65:927-48. [Crossref] [PubMed]
26. Zhou J, Hong X, Zhao X, Gao JH, Yuan J. APT-weighted and NOE-weighted image contrasts in glioma with different RF saturation powers based on magnetization transfer ratio asymmetry analyses. Magn Reson Med 2013;70:320-7. [Crossref] [PubMed]
27. Mamoune KE, Barantin L, Adriaensen H, Tillet Y. Application of Chemical Exchange Saturation Transfer (CEST) in neuroimaging. J Chem Neuroanat 2021;114:101944. [Crossref] [PubMed]
28. Ju Y, Liu A, Wang Y, Chen L, Wang N, Bu X, Du C, Jiang H, Wang J, Lin L. Amide proton transfer magnetic resonance imaging to evaluate renal impairment in patients with chronic kidney disease. Magn Reson Imaging 2022;87:177-82. [Crossref] [PubMed]
29. Yin H, Wang D, Yan R, Jin X, Hu Y, Zhai Z, Duan J, Zhang J, Wang K, Han D. Comparison of Diffusion Kurtosis Imaging and Amide Proton Transfer Imaging in the Diagnosis and Risk Assessment of Prostate Cancer. Front Oncol 2021;11:640906. [Crossref] [PubMed]
30. Guo Z, Qin X, Mu R, Lv J, Meng Z, Zheng W, Zhuang Z, Zhu X. Amide Proton Transfer Could Provide More Accurate Lesion Characterization in the Transition Zone of the Prostate. J Magn Reson Imaging 2022;56:1311-9. [Crossref] [PubMed]
31. Kamitani T, Sagiyama K, Yamasaki Y, Hino T, Wada T, Kubo M, Akiyoshi S, Yamamoto H, Yabuuchi H, Ishigami K. Amide proton transfer (APT) imaging of breast cancers and its correlation with biological status. Clin Imaging 2023;96:38-43. [Crossref] [PubMed]
32. Yang L, Wang L, Tan Y, Dan H, Xian P, Zhang Y, Tan Y, Lin M, Zhang J. 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. [Crossref] [PubMed]
33. Takayama Y, Nishie A, Sugimoto M, Togao O, Asayama Y, Ishigami K, Ushijima Y, Okamoto D, Fujita N, Yokomizo A, Keupp J, Honda H. Amide proton transfer (APT) magnetic resonance imaging of prostate cancer: comparison with Gleason scores. MAGMA 2016;29:671-9. [Crossref] [PubMed]
34. Wang P, Hu S, Wang X, Ge Y, Zhao J, Qiao H, Chang J, Dou W, Zhang H. Synthetic MRI in differentiating benign from metastatic retropharyngeal lymph node: combination with diffusion-weighted imaging. Eur Radiol 2023;33:152-61. [Crossref] [PubMed]
35. Moya-Sáez E, Navarro-González R, Cepeda S, Pérez-Núñez Á, de Luis-García R, Aja-Fernández S, Alberola-López C. Synthetic MRI improves radiomics-based glioblastoma survival prediction. NMR Biomed 2022;35:e4754. [Crossref] [PubMed]
36. Onishi S, Yamasaki F, Akiyama Y, Kawahara D, Amatya VJ, Yonezawa U, Taguchi A, Ozono I, Khairunnisa NI, Takeshima Y, Horie N. Usefulness of synthetic MRI for differentiation of IDH-mutant diffuse gliomas and its comparison with the T2-FLAIR mismatch sign. J Neurooncol 2024;170:429-36. [Crossref] [PubMed]
37. Lin D, Liu J, Ke C, Chen H, Li J, Xie Y, Ma J, Lv X, Feng Y. Radiomics Analysis of Quantitative Maps from Synthetic MRI for Predicting Grades and Molecular Subtypes of Diffuse Gliomas. Clin Neuroradiol 2024;34:817-26. [Crossref] [PubMed]
38. Sabouri S, Fazli L, Chang SD, Savdie R, Jones EC, Goldenberg SL, Black PC, Kozlowski P. MR measurement of luminal water in prostate gland: Quantitative correlation between MRI and histology. J Magn Reson Imaging 2017;46:861-9. [Crossref] [PubMed]
39. Sanz-Requena R, Martí-Bonmatí L, Pérez-Martínez R, García-Martí G. Dynamic contrast-enhanced case-control analysis in 3T MRI of prostate cancer can help to characterize tumor aggressiveness. Eur J Radiol 2016;85:2119-26. [Crossref] [PubMed]
40. Baur ADJ, Hansen CM, Rogasch J, Posch H, Elezkurtaj S, Maxeiner A, Erb-Eigner K, Makowski MR. Evaluation of T1 relaxation time in prostate cancer and benign prostate tissue using a Modified Look-Locker inversion recovery sequence. Sci Rep 2020;10:3121. [Crossref] [PubMed]
41. Cao H, Xu W, Xu Y, Rong X, Xiao X, Feng H, Wang X, Wang L, Qi T, Zhang L. Value of synthetic MRI quantitative parameters in preprocedural evaluation for TRUS/MRI fusion-guided biopsy of the prostate. Prostate 2023;83:1089-98. [Crossref] [PubMed]
42. Arita Y, Akita H, Fujiwara H, Hashimoto M, Shigeta K, Kwee TC, Yoshida S, Kosaka T, Okuda S, Oya M, Jinzaki M. Synthetic magnetic resonance imaging for primary prostate cancer evaluation: Diagnostic potential of a non-contrast-enhanced bi-parametric approach enhanced with relaxometry measurements. Eur J Radiol Open 2022;9:100403. [Crossref] [PubMed]
43. Sheng L, Yuan E, Yuan F, Song B. Amide proton transfer-weighted imaging of the abdomen: Current progress and future directions. Magn Reson Imaging 2024;107:88-99. [Crossref] [PubMed]
44. Milot L. Amide Proton Transfer-weighted MRI: Insight into Cancer Cell Biology. Radiology 2022;305:135-6. [Crossref] [PubMed]
45. Zhuang L, Lian C, Wang Z, Zhang X, Wu Z, Huang R. Breast-lesion assessment using amide proton transfer-weighted imaging and dynamic contrast-enhanced MR imaging. Radiol Oncol 2023;57:446-54. [Crossref] [PubMed]
46. Wang HJ, Cai Q, Huang YP, Li MQ, Wen ZH, Lin YY, Ouyang LY, Qian L, Guo Y. Amide Proton Transfer-weighted MRI in Predicting Histologic Grade of Bladder Cancer. Radiology 2022;305:127-34. [Crossref] [PubMed]
47. Ye Y, Gong Z, Song Y, Yv L, Liu Z, Ying H, Qiu J, Dai J, Peng Y, Gong L. Added value of amide proton transfer-weighted magnetic resonance imaging to Prostate Imaging Reporting and Data System version 2.1 in differentiating clinically significant prostate cancer. Quant Imaging Med Surg 2024;14:9036-48. [Crossref] [PubMed]
48. Hou H, Chen W, Diao Y, Wang Y, Zhang L, Wang L, Xu M, Yu J, Song T, Liu Y, Yuan Z. 3D Amide Proton Transfer-Weighted Imaging for Grading Glioma and Correlating IDH Mutation Status: Added Value to 3D Pseudocontinuous Arterial Spin Labelling Perfusion. Mol Imaging Biol 2023;25:343-52. [Crossref] [PubMed]
49. Choi YS, Ahn SS, Lee SK, Chang JH, Kang SG, Kim SH, Zhou J. Amide proton transfer imaging to discriminate between low- and high-grade gliomas: added value to apparent diffusion coefficient and relative cerebral blood volume. Eur Radiol 2017;27:3181-9. [Crossref] [PubMed]
50. Gupta J, Tayyib NA, Jalil AT, Hlail SH, Zabibah RS, Vokhidov UN, Alsaikhan F, Ramaiah P, Chinnasamy L, Kadhim MM. Angiogenesis and prostate cancer: MicroRNAs comes into view. Pathol Res Pract 2023;248:154591. [Crossref] [PubMed]
51. Zhou J, Heo HY, Knutsson L, van Zijl PCM, Jiang S. APT-weighted MRI: Techniques, current neuro applications, and challenging issues. J Magn Reson Imaging 2019;50:347-64. [Crossref] [PubMed]
52. Jendoubi S, Wagner M, Montagne S, Ezziane M, Mespoulet J, Comperat E, Estellat C, Baptiste A, Renard-Penna R. MRI for prostate cancer: can computed high b-value DWI replace native acquisitions? Eur Radiol 2019;29:5197-204. [Crossref] [PubMed]
53. Tamada T, Kido A, Ueda Y, Takeuchi M, Kanki A, Neelavalli J, Yamamoto A. Comparison of single-shot EPI and multi-shot EPI in prostate DWI at 3.0 T. Sci Rep 2022;12:16070. [Crossref] [PubMed]
54. Zhao W, Ju S, Yang H, Wang Q, Fang L, Pylypenko D, Wang W. Improved Value of Multiplexed Sensitivity Encoding DWI with Reversed Polarity Gradients in Diagnosing Prostate Cancer: A Comparison Study with Single-Shot DWI and MUSE DWI. Acad Radiol 2024;31:909-20. [Crossref] [PubMed]
55. Westphalen AC, McCulloch CE, Anaokar JM, Arora S, Barashi NS, Barentsz JO, et al. Variability of the Positive Predictive Value of PI-RADS for Prostate MRI across 26 Centers: Experience of the Society of Abdominal Radiology Prostate Cancer Disease-focused Panel. Radiology 2020;296:76-84. [Crossref] [PubMed]