Diagnosis and Risk Stratification of PI-RADS v2.1 Category 3–5 Lesions Using Amide Proton Transfer Imaging

doi:10.21203/rs.3.rs-4845281/v1

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Diagnosis and Risk Stratification of PI-RADS v2.1 Category 3–5 Lesions Using Amide Proton Transfer Imaging

https://doi.org/10.21203/rs.3.rs-4845281/v1

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Objectives: To explore the value of amide proton transfer-weighted imaging (APTWI) combined with apparent diffusion coefficient (ADC) and prostate-specific antigen density (PSAD) in evaluating the risk of aggressive PI-RADS v2.1 category 3–5 lesions.

Materials and Methods: We retrospectively analyzed clinical and MRI features of 69 patients with prostate cancer (PCa) and 32 patients with benign prostatic lesion (BL). The PCa group was classified into Gleason Grade Groups (GG) 1 to 4 based on Gleason Score (GS). APTWI parameters, ADC, and PSAD were compared among the different groups. The receiver operating characteristic (ROC) curve was used to assess the diagnostic accuracy of each parameter, and Spearman rank correlation was used to analyze the relationship between APTWI parameters and GS.

Results: APTWI parameters, ADC, and PSAD were significantly higher in the PCa group than in the BL group, whereas ADC was significantly lower in the PCa group than in the BL group. ADC showed the highest AUC in the diagnosis of PCa, followed by PSAD and APT_min. Combined analysis showed that APT_min+ADC+PSAD exhibited the highest AUC (0.997). In the PCa group, significant differences in APTWI parameters were found among GG1 to GG4 (P<0.001), with intra-group comparisons showing significant differences between GG1 and GG3, GG1 and GG4, GG2 and GG3, and GG2 and GG4 subgroups. The AUC of APT_mean was greatest in evaluating the risk of aggressive PCa (0.843), which further increased when APT_mean was combined with ADC and PSAD (0.859).

Conclusion: In PI-RADS v2.1 category 3–5 lesions, APT can serve as an important biomarker for the risk stratification of PCa, and combining APT with PSAD and ADC achieves the highest diagnostic efficacy.

Amide proton transfer

Apparent diffusion coefficient

Gleason score

PI-RADS v2.1

Prostate cancer

Prostate-specific antigen density

As one of the most common malignant tumors in men, prostate cancer (PCa) is a growing concern, with increasing incidence rates of both new cases and deaths^[1–3]. To diagnose PCa, clinicians rely on the Gleason Score (GS), which examines and scores prostate tissue under a microscope, with higher scores indicating aggressive cancer. Based on the National Comprehensive Cancer Network (NCCN) guidelines, men with low GS (≤ 6) can be monitored regularly, while those with high GS (≥ 3 + 4) are considered to have clinically significant prostate cancer (csPC) and may need aggressive treatment such as surgery or androgen deprivation therapy^[4–5].

Multiparametric magnetic resonance imaging (MRI) offers a non-invasive way to assess the prostate for abnormalities. Multiparametric MRI is particularly useful because it has superior soft tissue resolution^[6–7]. The Prostate Imaging Reporting and Data System (PI-RADS) v2.1 assigns a score from 1 to 5 based on these scans to estimate the risk of aggressive PCa, which include T₁-weighted imaging (T₁WI), T₂-weighted imaging (T₂WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE)^[8–10]. However, previous biopsy results proved that there was only 2%-22.9% of PI-RADS category 3 lesions and 40%-80% of PI-RADS category 4–5 lesions were csPC, indicating that the current multiparametric MRI sequences has a high false positive rate^[11–13]. Therefore, improving risk assessment for PI-RADS category 3–5 lesions to minimize unnecessary procedures and therapies remains a challenge in PCa diagnosis.

Studies have explored various ways to improve the risk stratification of PI-RADS v2.1 category 3–5 lesions. For instance, DWI generates several quantitative parameters, including apparent diffusion coefficient (ADC), prostate volume, prostate serum antigen (PSA), and PSA-density (PSAD); however, the results are not conclusive^[14–15]. Amide proton transfer weighted imaging (APTWI) is an MRI technique that provides information about the metabolic state of cells by assessing intracellular protein content and pH level by measuring the exchange rate of amine protons on free proteins and hydrogen protons of water at a specific resonance frequency. Approximately 66% of amine proton transfer alterations are attributed to increased protein content, and these changes are often linked to the development and progression of cancer^[16–19]. APTWI has been used in the differential diagnosis of prostate lesions and in the prediction of bone metastasis. However, there is limited research on APTWI in predicting PCa risk and its correlation with GS^[20–22].

The objective of this study is to explore the value of APTWI combined with ADC and PSAD in evaluating the risk of aggressive PI-RADS v2.1 category 3–5 lesions and the correlation between APTWI parameters and GS to provide a foundation for the MRI metabolic sequence in the early diagnosis and treatment of PCa.

Patients

This study was approved by Anhui Medical University of Technology Human Research Ethics Committee, and all subjects provided written informed consent. In total, 212 patients with suspected PCa and benign lesion (BL) were retrospectively enrolled between June 2023 and April 2024. The inclusion criteria were as follows: (1) patients who underwent complete multiparametric MRI, including T₁WI, T₂WI, DWI, DCE, and APTWI sequences; (2) patients with PI-RADS v2.1 category 3–5 lesions; (3) patients who underwent serum examination of PSA indicators 2 weeks before MRI; and (4) patients with complete pathological results, including GS determination through biopsy or surgery 2 weeks after MRI.

Among these patients, 111 were excluded due to the following reasons: (1) patients received local or systematic treatments such as surgery, endocrine therapy, radiotherapy, or chemotherapy before MRI (n = 15); (2) patients had poor quality images, which affected diagnostic and post-processing analysis (n = 11 ); (3) patients’ PI-RADS scores were not 3–5 (n = 24); (4) patients did not undergo serum examination of PSA indicators 2 weeks before MRI (n = 8); and (5) patients did not undergo biopsy or surgery 2 weeks after MRI (n = 53), leaving 101 patients for analysis. The patient selection process is shown in Fig. 1.

MRI protocol

The 3.0T MRI scanner (Philip, Ingenia CX), with a 16-channel phased array body coil, was used for multiparametric MRI of the prostate. Preparation involved emptying the bowels and keeping the bladder moderately full. The scanning range was from the anterior superior iliac spine to the upper margin of the pubic symphysis. Conventional MRI included axial T₁WI without fat suppression, axial/coronal/sagittal T₂WI without fat suppression, axial fat-suppressed T₂WI, axial DWI (b = 0,1000 and 2000 mm²/s), and axial DCE with 6 phases.

In the 3D-APTWI model, a dual-transmission system with patient-adaptive RF shimming (MultiTransmit 4D) was used to allow enough time for proton exchange. Nine image volumes were acquired at 7 different frequency offsets to ensure complete volumetric coverage. Seven acquisitions (− 4.3, − 3.5, − 2.7, + 2.7, + 3.5, + 4.3, and − 1560 ppm) were performed to obtain the APT signal intensity. The parameters of the APT scan are provided in Table 1.

Table 1

Scanning parameters of multiparametric MRI
Sequences	TR (ms)	TE(ms)	Scan time(s)	Layer thickness (mm)	Interlayer spacing (mm)	FOV (mm²)	NAS	Matrix
T₁WI	3000	105	54	3	0.3	220×220	1	320×315
T₂WI	3000	105	104	3	0.3	220×220	1	340×307
DWI	2900	80	191	3.5	0.4	220×220	1	88×86
APT	4500	7.3	329	6	0	300×300	1	100×100
APT:amide proton transfer,T₁WI:T₁-weighted imaging,T₂WI:T₂-weighted imaging,DWI:diffusion-weighted imaging, TR:repetition time, TE:echo time,FOV:field of view,NSA:number of signals averaged.

Image analysis

All image data were transferred to the workstation (IntelliSpace Portal, ISP, Philips Healthcare). Two radiologists (WS.H and X.W with 12 and 25 years of diagnostic experience in the urogenital system, respectively) measured the MRI parameters and scored prostate lesions according to PI-RADS v2.1 in a double-blinded manner without knowing the clinical and pathological data. A test of consistency was conducted using the results of the two radiologists.

The region of interest (ROI) in the maximum slice of the lesion was selected by fusing T₂WI, DWI, and APTWI images, and the largest lesion was chosen for analysis in cases of multiple lesions in one patient. The ROI avoided sites of necrosis, calcification, and hemorrhage and maintained a certain distance from the edge of the lesion to avoid volume effects. Three measurements were performed for each lesion, and the average value was calculated. For APTWI, including APT_max, APT_mean, and APT_min, the asymmetric magnetization transfer rate at 3.5 ppm (MTRasym (3.5 ppm)) was calculated using the formula (S_sat(-3.5 ppm) − S_sat(+ 3.5 ppm))/S₀×100 (%), where S₀ is the signal strength before the application of saturation pulse and S_sat is the signal strength after the application of saturation pulse at a certain chemical acquisition. The APT_max, APT_mean, and APT_min were then calculated^[23–24]. Finally, the ADC was calculated by diffusion software according to the ROI from the DWI image.

Biopsy and pathologic evaluation

Each patient underwent two ultrasound-guided core biopsies based on MRI-identified suspicious lesions and routine 12-core systematic biopsies (six cores in bilateral peripheral zone, four cores in bilateral transitional zone, and two cores in apex). All specimens were stained with hematoxylin-eosin. A pathologist with 15 years of experience in the genitourinary system (HQ.Z) recorded the diagnosis of PCa and the GS for all lesions. The GS was evaluated according to the 2014 International Society of Urological Pathology Modified Gleason Grading System, and it was classified as Gleason group 1 (GG1: GS ≤ 6), Gleason group 2 (GG2: GS = 3 + 4), Gleason group 3 (GG3: GS = 4 + 3), and Gleason group 4 (GG4: GS = 8, 9, and 10)^[4].

Prostate volume and serum examination

The diameter of the prostate was measured on T₂WI, and the prostate volume was calculated using the ellipsoid formula volume = transverse diameter×anterior-posterior diameter×superior-inferior diameter×0.52. PSA-related metrics were gathered from clinical records, and the PSAD was calculated by dividing total PSA by prostate volume.

Statistical analysis

Data were analyzed using SPSS 26.0 software (SPSS, Chicago, IL, USA). The measurement consistency between the two radiologists was evaluated using intraclass correlation coefficient(ICC). Kolmogorov-Smirnov test was used to evaluate the normal distribution of data, and the Levene test was used to evaluate the homogeneity and variance of data. Normally distributed and homoscedastic data were expressed as mean ± standard deviation\(\:\:(\overline{x}\)±s), while other data were presented as medians with minimum-maximum ranges. The correlation between APT and GS was determined using Spearman’s correlation analysis. The differences in APT, ADC, and PSAD between PCa and BL groups were analyzed using two independent-sample t-test or Mann-Whitney U test. The differences in APT, ADC, and PSAD between GG lesions were analyzed using ANOVA or Kruskal-Wallis test, and the diagnostic performance of parameters was evaluated by the ROC curve. Parameters with higher diagnostic efficacy were selected to construct a combined model to predict prostate risk through logistic regression analysis. P < 0.05 indicated statistical significance.

General characteristics

Of 101 patients, 23 were scored using PI-RADS v2.1 3, 31 were scored using PI-RADS v2.1 4, and 47 were scored using PI-RADS v2.1 5. For pathological evaluation, there were 32 cases of BL, including normal prostate tissue (n = 6), hyperplasia (n = 15), and prostatitis (n = 11). There were no differences in patients’ age and maximum diameter between the PCa and BL groups (Table 2). Among 69 cases of prostate cancer (PCa), 17 were GG1,14 were GG2, 14 were GG3, and 24 were GG4 (6 cases were GS = 8, 16 cases were GS = 9, 2 cases were GS = 10). There were no differences in patients’ age and maximum diameter among the different GG subgroups (Table 3).

Table 2

Comparation of general characteristics,APTWI parameters,ADC and PASD between BL and PCa group
Characteristics	BL(n = 32)	PCa(n = 69)	P-value
Age(years)	72.16 ± 7.185	70.13 ± 8.887	0.179
Maximum diameter(cm)	6.194 ± 1.155	5.491 ± 0.966	0.000
APT_max (%)	1.977 ± 0.496	2.600 ± 0.781	0.000
APT_mean (%)	1.400(0.300-2.133)	2.024 ± 0.650	0.000
APT_min (%)	0.600(-0.600-1.467)	1.300(-0.133-2.800)	0.000
ADC (×10^− 3mm²/s)	1.224 ± 0.234	0.639 ± 0.147	0.000
PSAD (ng/ml/cm³)	0.120(0.008–1.226)	0.674(0.063–11.468)	0.001
APT:amide proton transfer,ADC: apparent diffusion coefficient,PSAD: prostate-specific antigen density,BL: benign prostatic lesions,PCa: prostate cancer.

Table 3

Comparation of generaleneral characteristics,APTWI parameters,ADC and PASD among GG1-GG4 lesions
Characteristics	GG1(n = 17)	GG2(n = 14)	GG3(n = 14)	GG4(n = 24)	P-value
Age(years)	66.89 ± 6.990	67.670 ± 11.011	71.400 ± 8.072	71.750 ± 8.975	0.358
Maximum diameter(cm)	5.610 ± 0.709	5.534 ± 0.653	5.266 ± 0.720	5.512 ± 1.353	0.468
APT_max (%)	1.971 ± 0.513	2.033 ± 0.558	2.860 ± 0.290	3.226 ± 0.677	0.000
APT_mean (%)	1.471 ± 0.420	1.588 ± 0.509	2.250 ± 0.296	2.537 ± 0.532	0.000
APT_min (%)	1.000(0.233–1.567)	1.050(-0.133-1.667)	1.383 ± 0.363	1.617 (0.667-2.800)	0.000
ADC (×10^− 3mm²/s)	0.702 ± 0.180	0.702 ± 0.139	0.625 ± 0.124	0.550(0.377–0.743)	0.007
PSAD (ng/ml/cm³)	0.288(0.063–3.230)	0.325(0.071–3.230)	0.720(0.088–11.468)	0.900(0.106–11.468)	0.004
APT:amide proton transfer,ADC: apparent diffusion coefficient,PSAD: prostate-specific antigen density,GG: gleason grade group.

Consistency test

The ICC of APT_max, APT_mean, APT_min, and ADC were 0.842, 0.873, 0.796, and 0.858, respectively, indicating substantial agreement between the two radiologists regarding APT and ADC measurements.

Comparative analysis of APTWI parameters in PI-RADS 3–5 lesions

APT_max, APT_mean, and APT_min were all significantly higher in the PCa group than in the BL group (P < 0.001) (Table 2, Fig. 2). In the PCa group, statistically significant differences in APT_max, APT_mean, and APT_min were found in GG1 to GG4 (P < 0.001), and intra-group comparisons showed significant differences in APTWI parameters between GG1 and GG3 lesions (APT_max: P < 0.001; APT_mean: P < 0.001; APT_min: P < 0.05) and between GG1 and GG4 lesions (APT_max: P < 0.001; APT_mean: P < 0.001; APT_min: P < 0.001). Similarly, there were significant differences between GG2 and GG3 lesions (APT_max: P < 0.001; APT_mean: P < 0.001; APT_min: P < 0.05) and between GG2 and GG4 lesions (APT_max: P < 0.001; APT_mean: P < 0.001; APT_min: P < 0.001). No significant differences were found between the remaining subgroups (Table 3, Figs. 3 and 4).

Correlation between APT parameters and Gleason score in PI-RADS 3–5 lesions

Spearman’s rank correlation analysis revealed a moderate positive correlation of APT_max and APT_mean with GS (r = 0.652, r = 0.665, P < 0.05, respectively). However, APT_min showed a weak positive correlation with GS (r = 0.464, P < 0.05). These relationships are depicted in Fig. 5.

Comparative analysis of ADC and PASD in PI-RADS 3–5 lesions

ADC was significantly lower in the PCa group than in the BL group (P < 0.001). In the PCa group, there were significant differences in ADC among the four subgroups (P = 0.007), and intra-group comparisons showed that ADC was significantly lower in GG4 than in GG1 and GG2 (P < 0.01 and P < 0.05, respectively) (Tables 2 and 3, Fig. 4).

PSAD was significantly higher in the PCa group than in the BL group (P = 0.001). In the PCa group, there were significant differences in PSAD among the four subgroups (P = 0.004), but PASD was higher in GG4 than in GG1 (P < 0.01), as shown by intra-group comparisons (Tables 2 and 3, Fig. 4).

ROC analysis in evaluating the risk of aggressive PI-RADS 3–5 lesions

For the diagnosis of PCa, ADC exhibited the highest area under the curve (AUC = 0.994), followed by PSAD (AUC = 0.845) and APT_min (AUC = 0.841). ADC combined with PSAD also exhibited a high AUC (AUC = 0.997), followed by APT_min+ADC + PSAD (Table 4, Fig. 6).

Table 4

Parameters of ROC curve with APTWI parameters and combined parameters for the diagnosis of PCa
Characteristics	AUC	95%CI	Sensitivity	Specificity	Youden index J	Best cut-off value	P value
APT_max (%)	0.748	0.654–0.841	81.3%	65.217%	0.465	2.383	0.000
APT_mean (%)	0.823	0.744–0.902	93.8%	63.768%	0.575	1.850	0.000
APT_min (%)	0.841	0.776–0.919	75%	78.261%	0.533	0.867	0.000
ADC (×10^− 3mm²/s)	0.994	0.984-1.000	100%	95.652%	0.957	1	0.000
PSAD (ng/ml/cm³)	0.845	0.768–0.922	81.3%	79.710%	0.610	0.231	0.000
APT_max+ADC + PSAD	0.995	0.986-1.000	100%	95.652%	0.957	0.796	0.000
APT_mean+ADC + PSAD	0.994	0.984-1.000	100%	95.652%	0.957	0.804	0.000
APT_min+ADC + PSAD	0.997	0.990-1.000	100%	97.101%	0.971	0.710	0.000
APT:amide proton transfer,ADC: apparent diffusion coefficient,PSAD: prostate-specific antigen density.AUC:area under curve,CI:confidence interval.

To evaluate the risk of aggressive PCa, ROC analysis of APTWI parameters was used to distinguish GG1 from GG ≥ 2 lesions based on the definition of csPCa (GS ≥ 3 + 4). The AUC for separating PCa of GG1 from PCa of GG ≥ 2 was 0.839 for APT_max (sensitivity = 100%, specificity = 61.538%) and 0.843 for APT_mean (sensitivity = 82.4%, specificity = 80.770%), respectively. The AUC for ADC and PASD for separating PCa of GG1 from PCa of GG ≥ 2 was 0.665 (sensitivity = 88.2%, specificity = 44.231%) and 0.724 (sensitivity = 76.5%, specificity = 61.538%), respectively (Fig. 6, Table 5).

Table 5

Parameters of ROC curve with APTWI parameter evaluating aggressive risk of PCa
Characteristics	AUC	95%CI	Sensitivity	Specificity	Youden index J	Best cut-off value	P value
APT_max (%)	0.839	0.746–0.931	100%	61.538%	0.615	2.667	0.000
APT_mean (%)	0.843	0.748–0.938	82.4%	80.770%	0.631	1.767	0.000
ADC (×10^− 3mm²/s)	0.665	0.508–0.823	88.2%	44.231%	0.325	0.572	0.042
PSAD (ng/ml/cm³)	0.724	0.584–0.864	76.5%	61.538%	0.380	0.633	0.006
APT_max+ADC + PSAD	0.852	0.763–0.940	76.516%	76.923%	0.652	0.765	0.000
APT_mean+ADC + PSAD	0.859	0.772–0.946	100%	63.462%	0.635	0.864	0.000
APT:amide proton transfer,ADC: apparent diffusion coefficient,PSAD: prostate-specific antigen density, AUC:area under curve,CI:confidence interval.

The diagnostic efficiency of combined parameters was highest between GG1 and GG ≥ 2 (APT_max+ADC + PSAD: AUC = 0.852, sensitivity = 76.516%, specificity = 76.923%; APT_mean+ADC + PSAD: AUC = 0.859, sensitivity = 100%, specificity = 63.462%). The ROC curves of APTWI and combined parameters in evaluating the risk of aggressive PCa are shown in Fig. 6, and the diagnostic efficacy is shown in Table 5.

This study investigated APTWI and its correlation with GS in PI-RADS v2.1 category 3–5 lesions and concluded that APTWI is valuable in diagnosing PCa and evaluating different GG types of PCa. APT_mean combined with ADC and PSAD had optimal diagnostic performance in the risk assessment of PCa.

APTWI can noninvasively detect the expression of soluble proteins and polypeptides, and it can be used in the discrimination of malignant and benign tissue. Our study showed that all APTWI parameters were higher in PCa than in BL, with excellent diagnostic efficacy in diagnosing PCa. The results are consistent with previous research and suggest that the pathological changes associate with tight cellular arrangement and high polypeptide concentration, which correlate with high MTRasym values (3.5 ppm)^[25–28]. On the contrary, normal and hypoplastic prostate tissue is composed of loose or dense cellular arrangement, smooth muscle, and fibrous tissue, with large extracellular spaces and gland cavities, so MTRasym values (3.5 ppm) were lower compared to its counterpart^[29–31].

We also found moderate positive correlations between APT_max, APT_mean, and GS, indicating that tumor cell density and protein expression increased gradually with elevated GS. However, Takayama et al.^[32] found no correlation between APT and GS. The growth rate of PCa increases rapidly with the degree of malignancy, at which time gland function and soluble proteins may be destroyed, resulting in low MTRasym values (3.5ppm)^[33]. However, three-dimensional acquisition with a higher SNR and a smaller ROI was used to obtain complete tumor information; therefore, it is unlikely that poor gland function affected our measurements. Furthermore, a weaker correlation was found for APT_min and GS, which may be due to the fact that the minimal proton exchange rate did not reflect the protein and peptide content of the whole tumor tissue^[27].

There were significant differences in APT parameters among the different GG lesions, and protein and polypeptide levels increased gradually in PCa with increasing aggressiveness (reflected in the different GG subgroups), suggesting that APT_max and APT_mean may be valuable biomarkers in predicting patient prognosis. Further intra-group analyses found that differences existed between GG1 and GG3, GG1 and GG4, GG2 and GG3, GG2 and GG4 lesions. The were no differences in APT parameters between GG1 and GG2 lesions because glands of two subgroups have similar differentiation degree, along with similar protein and polypeptide content. However, differences were not found between GG3 and GG4 lesions, perhaps because most of the GG4 lesions were complicated by necrosis and damaged proteins, which reduced MTRasym (3.5 ppm) values and required larger sample sizes for intra-group comparisons.

Based on the definition of csPCa (GS ≥ 3 + 4), ROC analysis of APTWI parameters was used to distinguish GG1 from GG ≥ 2 lesions to evaluate the risk of aggressive PCa, and APT_mean showed the highest diagnostic efficacy (AUC = 0.843). Meanwhile, ADC and PSAD were used separately or in combination with other parameters in the ROC analysis, as they perform well in the diagnosis and risk assessment of PCa. Combined analysis showed that the diagnostic efficacy of APT_mean+ADC + PSAD was higher than APT_mean in evaluating the risk of aggressive PCa, indicating that the metabolic APT parameter can serve as an important biomarker for risk stratification, and along with laboratory indicators and ADC, it is an important criterion for PI-RADS v2.1 classification.

Limitations

This study had some limitations. Firstly, this was a retrospective study at a single center with a limited sample size. Future prospective multicenter trials with larger cohorts should be performed. Secondly, multiple factors, such as GS, PSA level, and TNM staging, determine the risk of aggressive PCa; however, this study only focused on the relationship between GS and APTWI as this was a preliminary study exploring the relationship between APTWI parameters and PCa aggressiveness. Thirdly, although visible necrosis or cystic areas were excluded during ROI selection, we could not eliminate the influence of tumor heterogeneity. Therefore, radiomics may be more objective and accurate in the interpretation of APTWI research in the future.

In PI-RADS v2.1 category 3–5 lesions, APTWI shows promise in diagnosing PCa and evaluating aggressiveness based on GG stratification. APTWI has the potential to serve as a valuable biomarker for PCa risk stratification, and its combined use with PSAD and ADC can achieve the most accurate diagnosis.

Author Contribution

Hongkun Fang: Data management、Data analysis、Manuscript writing.Weishu Hou: Protocol development、Manuscript editing.Qun Wang: Data management、Data analysis.Xiaoyu Zhang: Data collection.Xiao Wang: Project development.Shuhai Zhang: Data collection.Shoubin Li: Data collection.Xiaohu Li: Data collection or management.Yongqiang Yu: Data management、Manuscript editing.

Xia C, Dong X, Li H et al (2022) Cancer statistics in China and United States, 2022: profiles, trends, and determinants. Chin Med J (Engl) 135(5):584–590. 10.1097/CM9.0000000000002108PMID: 35143424; PMCID: PMC8920425
Dyba T, Randi G, Bray F et al (2021) The European cancer burden in 2020: Incidence and mortality estimates for 40 countries and 25 major cancers. Eur J Cancer 157:308–347. 10.1016/j.ejca.2021.07.039Epub 2021 Sep 21. PMID: 34560371; PMCID: PMC8568058
Bergengren O, Pekala KR, Matsoukas K et al (2023) 2022 Update on Prostate Cancer Epidemiology and Risk Factors-A Systematic Review. Eur Urol 84(2):191–206. 10.1016/j.eururo.2023.04.021Epub 2023 May 16. PMID: 37202314; PMCID: PMC10851915
Epstein JI, Egevad L, Amin MB, Delahunt B, Srigley JR, Humphrey PA, Grading Committee (2016) The 2014 International Society of Urological Pathology (ISUP) Consensus Conference on Gleason Grading of Prostatic Carcinoma: Definition of Grading Patterns and Proposal for a New Grading System. Am J Surg Pathol. ;40(2):244 – 52. 10.1097/PAS.0000000000000530. PMID: 26492179
Brönimann S, Pradere B, Karakiewicz P, Abufaraj M, Briganti A, Shariat SF (2020) An overview of current and emerging diagnostic, staging and prognostic markers for prostate cancer. Expert Rev Mol Diagn 20(8):841–850 Epub 2020 Jun 25. PMID: 32552088
Deniffel D, Healy GM, Dong X et al (2021) Avoiding Unnecessary Biopsy: MRI-based Risk Models versus a PI-RADS and PSA Density Strategy for Clinically Significant Prostate Cancer. Radiology 300(2):369–379. 10.1148/radiol.2021204112Epub 2021 May 25. PMID: 34032510
Stempel CV, Dickinson L, Pendsé D (2020) MRI in the Management of Prostate Cancer. Semin Ultrasound CT MR 41(4):366–372. 10.1053/j.sult.2020.04.003Epub 2020 May 7. PMID: 32620227
Barrett T, Rajesh A, Rosenkrantz AB, Choyke PL, Turkbey B (2019) PI-RADS version 2.1: one small step for prostate MRI. Clin Radiol 74(11):841–852. 10.1016/j.crad.2019.05.019Epub 2019 Jun 22. PMID: 31239107
Lo GC, Margolis DJA (2020) Prostate MRI with PI-RADS v2.1: initial detection and active surveillance. Abdom Radiol (NY). ;45(7):2133–2142. 10.1007/s00261-019-02346-z. PMID: 31797025
O'Shea A, Harisinghani M (2022) PI-RADS: multiparametric MRI in prostate cancer. MAGMA 35(4):523–532. 10.1007/s10334-022-01019-1Epub 2022 May 21. PMID: 35596009
Sathianathen NJ, Omer A, Harriss E et al (2020) Negative Predictive Value of Multiparametric Magnetic Resonance Imaging in the Detection of Clinically Significant Prostate Cancer in the Prostate Imaging Reporting and Data System Era: A Systematic Review and Meta-analysis. Eur Urol 78(3):402–414 Epub 2020 May 20. PMID: 32444265
Hectors SJ, Chen C, Chen J et al (2021) Magnetic Resonance Imaging Radiomics-Based Machine Learning Prediction of Clinically Significant Prostate Cancer in Equivocal PI-RADS 3 Lesions. J Magn Reson Imaging 54(5):1466–1473. 10.1002/jmri.27692Epub 2021 May 10. PMID: 33970516
Wadera A, Alabousi M, Pozdnyakov A et al (2021) Impact of PI-RADS Category 3 lesions on the diagnostic accuracy of MRI for detecting prostate cancer and the prevalence of prostate cancer within each PI-RADS category: A systematic review and meta-analysis. Br J Radiol 94(1118):20191050. 10.1259/bjr.20191050Epub 2020 Oct 22. PMID: 33002371; PMCID: PMC7934301
Falagario UG, Martini A, Wajswol E et al (2020) Avoiding Unnecessary Magnetic Resonance Imaging (MRI) and Biopsies: Negative and Positive Predictive Value of MRI According to Prostate-specific Antigen Density, 4Kscore and Risk Calculators. Eur Urol Oncol 3(5):700–704 Epub 2019 Sep 20. PMID: 31548130
Hu C, Sun J, Xu Z et al (2023) Development and external validation of a novel nomogram to predict prostate cancer in biopsy-naïve patients with PSA < 10 ng/ml and PI-RADS v2.1 = 3 lesions. Cancer Med 12(3):2560–2571. 10.1002/cam4.5100Epub 2022 Aug 3. PMID: 35920264; PMCID: PMC9939143
Meng N, Wang X, Sun J et al (2020) Application of the amide proton transfer-weighted imaging and diffusion kurtosis imaging in the study of cervical cancer. Eur Radiol 30(10):5758–5767. 10.1007/s00330-020-06884-9Epub 2020 May 18. PMID: 32424593
Koike H, Morikawa M, Ishimaru H, Ideguchi R, Uetani M, Miyoshi M (2023) Diagnostics (Basel) 13(5):914. 10.3390/diagnostics13050914PMID: 36900058; PMCID: PMC10000843 Amide Proton Transfer-Chemical Exchange Saturation Transfer Imaging of Intracranial Brain Tumors and Tumor-like Lesions: Our Experience and a Review
Ray KJ, Simard MA, Larkin JR et al (2019) umor pH and Protein Concentration Contribute to the Signal of Amide Proton Transfer Magnetic Resonance Imaging. Cancer Res 79(7):1343–1352. 10.1158/0008-5472.CAN-18-2168Epub 2019 Jan 24. PMID: 30679178; PMCID: PMC6462213
Su C, Liu C, Zhao L et al (2017) Amide Proton Transfer Imaging Allows Detection of Glioma Grades and Tumor Proliferation: Comparison with Ki-67 Expression and Proton MR Spectroscopy Imaging. AJNR Am J Neuroradiol 38(9):1702–1709. 10.3174/ajnr.A5301Epub 2017 Jul 20. PMID: 28729292; PMCID: PMC7963688
Guo Z, Qin X, Mu R et al (2022) Amide Proton Transfer Could Provide More Accurate Lesion Characterization in the Transition Zone of the Prostate. J Magn Reson Imaging 56(5):1311–1319. 10.1002/jmri.28204Epub 2022 Apr 16. PMID: 35429190
Kido A, Tamada T, Ueda Y, Takeuchi M, Kanki A, Yamamoto A Comparison Between Amide Proton Transfer Magnetic Resonance Imaging Using 3-Dimensional Acquisition and Diffusion-Weighted Imaging for Characterization of Prostate Cancer: A Preliminary Study. J Comput Assist Tomogr 2023 Mar-Apr 01;47(2):178–185. doi: 10.1097/RCT.0000000000001398. Epub 2022 Dec 13. PMID: 36729617.
Hu W, Chen L, Lin L, Wang J, Wang N, Liu A (2023) Three-dimensional amide proton transfer-weighted and intravoxel incoherent motion imaging for predicting bone metastasis in patients with prostate cancer: A pilot study. Magn Reson Imaging 96:8–16 Epub 2022 Nov 12. PMID: 36375760
Zhou J, Zaiss M, Knutsson L et al (2022) Review and consensus recommendations on clinical APT-weighted imaging approaches at 3T: Application to brain tumors. Magn Reson Med 88(2):546–574. 10.1002/mrm.29241Epub 2022 Apr 22. PMID: 35452155; PMCID: PMC9321891
Gao T, Zou C, Li Y, Jiang Z, Tang X, Song X (2021) A Brief History and Future Prospects of CEST MRI in Clinical Non-Brain Tumor Imaging. Int J Mol Sci 22(21):11559. 10.3390/ijms222111559PMID: 34768990; PMCID: PMC8584005
Jia G, Abaza R, Williams JD et al (2011) Amide proton transfer MR imaging of prostate cancer: a preliminary study. J Magn Reson Imaging 33(3):647–654. 10.1002/jmri.22480PMID: 21563248; PMCID: PMC4287206
Yin H, Wang D, Yan R et al (2021) Comparison of Diffusion Kurtosis Imaging and Amide Proton Transfer Imaging in the Diagnosis and Risk Assessment of Prostate Cancer. Front Oncol 11:640906. 10.3389/fonc.2021.640906PMID: 33937041; PMCID: PMC8082407
Yang L, Wang L, Tan Y et al (2023) 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 23(1):3. 10.1186/s40644-022-00515-wPMID: 36611191; PMCID: PMC9826590
Wu B, Jia F, Li X, Li L, Wang K, Han D (2020) Comparative Study of Amide Proton Transfer Imaging and Intravoxel Incoherent Motion Imaging for Predicting Histologic Grade of Hepatocellular Carcinoma. Front Oncol 10:562049. 10.3389/fonc.2020.562049PMID: 33194630; PMCID: PMC7659984
Wang XZ, Wang B, Gao ZQ et al (2009) Diffusion-weighted imaging of prostate cancer: correlation between apparent diffusion coefficient values and tumor proliferation. J Magn Reson Imaging. ;29(6):1360-6. 10.1002/jmri.21797. PMID: 19472393
Nguyen K, Sarkar A, Jain AK (2014) Prostate cancer grading: use of graph cut and spatial arrangement of nuclei. IEEE Trans Med Imaging 33(12):2254–2270. 10.1109/TMI.2014.2336883Epub 2014 Jul 10. PMID: 25029379
El-Serougy L, Abdel Razek AA, Ezzat A, Eldawoody H, El-Morsy A (2016) Assessment of diffusion tensor imaging metrics in differentiating low-grade from high-grade gliomas. Neuroradiol J 29(5):400–407. 10.1177/1971400916665382Epub 2016 Aug 25. PMID: 27562582; PMCID: PMC5033100
Takayama Y, Nishie A, Sugimoto M et al (2016) Amide proton transfer (APT) magnetic resonance imaging of prostate cancer: comparison with Gleason scores. MAGMA 29(4):671–679. 10.1007/s10334-016-0537-4Epub 2016 Mar 10. PMID: 26965511
Togao O, Yoshiura T, Keupp J et al (2014) Amide proton transfer imaging of adult diffuse gliomas: correlation with histopathological grades. Neuro Oncol 16(3):441–448. 10.1093/neuonc/not158Epub 2013 Dec 4. PMID: 24305718; PMCID: PMC3922507

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Diagnosis and Risk Stratification of PI-RADS v2.1 Category 3–5 Lesions Using Amide Proton Transfer Imaging

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Version 1

Abstract

Figures

Introduction

Materials and Methods

Patients

MRI protocol

Image analysis

Biopsy and pathologic evaluation

Prostate volume and serum examination

Statistical analysis

Results

General characteristics

Consistency test

Comparative analysis of APTWI parameters in PI-RADS 3–5 lesions

Correlation between APT parameters and Gleason score in PI-RADS 3–5 lesions

Comparative analysis of ADC and PASD in PI-RADS 3–5 lesions

ROC analysis in evaluating the risk of aggressive PI-RADS 3–5 lesions

Discussion

Limitations

Conclusion

Declarations

Author Contribution

References

Additional Declarations

Status:

Version 1