Transition Zone Prostate Cancer Research Articles

Diffusion weighted image-guided transitional zone scoring in the detection of transitional zone prostate cancer: comparison with current PI-RADS v2.1 scoring.

To compare the performance of diffusion-weighted imaging-guided transitional zone (TZ) lesion scoring on T2-weighted imaging (DWI-guided TZ scoring) to conventional PI-RADS TZ scoring. Forty patients carried transition zone prostate cancer (TZPCa), and 40 patients had benign prostatic hyperplasia without TZPCa. A lesion-base, one-to-one correlation between the pathologic mapping sheet and the corresponding MR imaging was conducted by consensus between the genitourinary-specialized radiologist and pathologist. DWI-guided TZ scoring was defined as evaluating the DWI/apparent diffusion coefficient (ADC) images first, identifying the suspicious foci, then correlating the foci with the T2-weighted imaging, and finally assigning the PI-RADS score based on PI-RADS v2.1. Three other radiologists independently recorded the PI-RADS v2.1 scoring for TZ and the DWI-guided TZ scoring, with a time interval of 4weeks. When a PI-RADS score of ≥ 3 was considered a positive lesion, the specificity, PPV, NPV and sensitivity between the DWI-guided TZ scoring and conventional PI-RADS TZ scoring were 0.896 vs. 0.542 (p < .001), 0.764 vs. 0.439 (p < .001), 0.853 vs. 0.759 (p = .001), and 0.687 vs. 0.676 (p = .836), respectively. When PI-RADS scores ≥ 4 was considered cancer-positive, the specificity and PPV were also higher when applying DWI-guided TZ scoring (0.986 vs. 0.944, p = .007; 0.943 vs. 0.810, p = .009, respectively); however, the sensitivity and NPV were not statistically different (0.468 vs. 0.468, p = .998; 0.785 vs. 0.776, p = .537, respectively). The interobserver agreement presented as κ-value was higher in DWI-guided TZ scoring (0.584) than in conventional PI-RADS TZ scoring (0.155) (p = .003). DWI-guided TZ scoring improves the interobserver agreement, specificity, and predictive value without impairing the sensitivity.

Tale of two zones: investigating the clinical outcomes and research gaps in peripheral and transition zone prostate cancer through a systematic review and meta-analysis

ObjectiveTo assess pathological characteristics, clinical features and outcomes of patients diagnosed with peripheral zone (PZ) and transition zone (TZ) prostate cancer after prostatectomy.Methods and analysisWe systematically reviewed PubMed, EMBASE and...

Comparison of the Utility of PI-RADS 2.1, ADC Values, and Combined Use of Both, for the Diagnosis of Transition Zone Prostate Cancers.

To assess the performance of apparent diffusion coefficient (ADC; values or category) alone, Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1) scoring alone, and the two in combination, to diagnose transition zone prostate cancers (PCas). This retrospective study included 222 patients who underwent multiparametric magnetic resonance imaging of the prostate between May 2020 and December 2022 and who had pathologically confirmed PCa or benign prostatic hyperplasia (BPH). Prostate Imaging Reporting and Data System version 2.1 and ADC (values or category) were used in the assessment of suspicious findings identified in the transition zone. The interobserver agreements for region-of-interest measurements were calculated by intraclass correlation coefficients. Logistic regression analyses were used to determine the performance of PI-RADS v2.1 alone and in combination with ADC (values or category) to diagnose PCa. Receiver operating characteristic curve and DeLong test were used to evaluate the diagnostic performance of the quantitative parameters. A total of 152 patients had BPH, and 70 patients had PCa. For BPH versus PCa, the ADC values of PCa (0.64 × 10 -3 ± 0.16 × 10 -3 mm 2 /s) were significantly lower than BPH (1.06 ± 0.18 × 10 -3 mm 2 /s; P < 0.001). The PI-RADS scores for PCa (5 [interquartile range, 5-5]) were significantly higher than BPH (2 [interquartile range, 2-3]; P < 0.001). For all patients who had PI-RADS 1-5, the combined use of ADC (values or category) together with PI-RADS v2.1 did not perform significantly better than the use of PI-RADS v2.1 alone. The receiver operating characteristic of ADC category in combination with PI-RADS v2.1 score, 0.756 (95% confidence interval, 0.646-0.846), was significantly higher than that for PI-RADS 2.1 alone, 0.631 (95% confidence interval, 0.514-0.738), in PI-RADS 3-4 lesions ( P = 0.047). The ADC category can help to improve the diagnostic performance of PI-RADS v2.1 category 3-4 lesions in diagnosing PCa.

Transitional zone prostate cancer: Performance of texture-based machine learning and image-based deep learning.

This study is aimed to explore the performance of texture-based machine learning and image-based deep-learning for enhancing detection of Transitional-zone prostate cancer (TZPCa) in the background of benign prostatic hyperplasia (BPH), using a one-to-one correlation between prostatectomy-based pathologically proven lesion and MRI. Seventy patients confirmed as TZPCa and twenty-nine patients confirmed as BPH without TZPCa by radical prostatectomy. For texture analysis, a radiologist drew the region of interest (ROI) for the pathologically correlated TZPCa and the surrounding BPH on T2WI. Significant features were selected using Least Absolute Shrinkage and Selection Operator (LASSO), trained by 3 types of machine learning algorithms (logistic regression [LR], support vector machine [SVM], and random forest [RF]) and validated by the leave-one-out method. For image-based machine learning, both TZPCa and BPH without TZPCa images were trained using convolutional neural network (CNN) and underwent 10-fold cross validation. Sensitivity, specificity, positive and negative predictive values were presented for each method. The diagnostic performances presented and compared using an ROC curve and AUC value. All the 3 Texture-based machine learning algorithms showed similar AUC (0.854-0.861)among them with generally high specificity (0.710-0.775). The Image-based deep learning showed high sensitivity (0.946) with good AUC (0.802) and moderate specificity (0.643). Texture -based machine learning can be expected to serve as a support tool for diagnosis of human-suspected TZ lesions with high AUC values. Image-based deep learning could serve as a screening tool for detecting suspicious TZ lesions in the context of clinically suspected TZPCa, on the basis of the high sensitivity.

Morphologic perfusion patterns and PI-RADSv2.1 in transition zone prostate cancer

PurposeTo evaluate morphologic perfusion patterns in transition zone prostate cancer in multiparametric MRI controlled by in-bore MRI-guided prostate biopsy.MethodsTwo experienced radiologists evaluated MRI perfusion patterns in consensus from 321 biopsy cores from the transition zone in 141 patients. Transition zone cancer was present in 77 cores in 36 patients. Single early-phase perfusion images were evaluated separately for the presence of a transition zone prostate cancer (consensus tumor early perfusion). The proposed criteria for the perfusion pattern (asymmetry, signal strength, and homogeneity) were rated in consensus for each biopsy position in the presence of the T2w images including the markers of the biopsy trace. We analyzed receiver operating characteristic curves for the PI-RADSv2.1 score and the proposed perfusion pattern.ResultsA logistic regression model with PI-RADSv2.1 and perfusion patterns in early perfusion imaging improved the model fit significantly compared to a model containing only PI-RADSv2.1 (Likelihood Ratio Test, LR = 14.5, p < .001). The AUC was 0.96 for the multiple regression model compared to 0.92 for the PI-RADSv2.1 alone. The evaluation of homogeneity in single early-enhancement images is not inferior compared to the conventional DCE parameter of PI-RADSv2.1 (AUC 0.84 versus 0.83).ConclusionMorphologic perfusion patterns significantly improve the diagnostic performance of PI-RADSv2.1 in TZ prostate cancer.Graphical abstract

Extracapsular extension of transitional zone prostate cancer miss-detected by multiparametric magnetic resonance imaging.

To demonstrate the importance of extracapsular extension (ECE) of transitional zone (TZ) prostate cancer (PCa), examine the causes of its missed detection by Mp-MRI, and develop a new predictive model by integrating multi-level clinical variables. This retrospective study included 304 patients who underwent laparoscopic radical prostatectomy after 12 + X needle transperineal transrectal ultrasound (TRUS)-MRI-guided targeted prostate biopsy from 2018 to 2021 in our center was performed. In this study, the incidence rates of ECE were similar in patients with MRI lesions in the peripheral zone (PZ) and TZ (P = 0.66). However, the missed detection rate was higher in patients with TZ lesions than in those with PZ lesions (P < 0.05). These missed detections result in a higher positive surgical margin rate (P < 0.05). In patients with TZ lesions, detected MP-MRI ECE may have grey areas: the longest diameters of the MRI lesions were 16.5-23.5mm; MRI lesion volumes were 0.63-2.51ml; MRI lesion volume ratios were 2.75-8.86%; PSA were 13.85-23.05ng/ml. LASSO regression was used to construct a clinical prediction model for predicting the risk of ECE in TZ lesions from the perspective of MRI and clinical features, including four variables: the longest diameter of MRI lesions, TZ pseudocapsule invasion, ISUP grading of biopsy pathology, and number of positive biopsy needles. Patients with MRI lesions in the TZ have the same incidence of ECE as those with lesions in the PZ, but a higher missed detection rate.

Comparison and combination of amide proton transfer magnetic resonance imaging and the apparent diffusion coefficient in differentiating the grades of prostate cancer.

More effective risk stratification of prostate cancer (PCa) than that possible with current methods can reduce undertreatment and guard against overtreatment. The aim of this study is to validate the differences and combined effects of amide proton transfer (APT) imaging and apparent diffusion coefficient (ADC) in discriminating the PCa grade group (GG) ≤2 from GG ≥3 PCa. This is an ongoing prospective study conducted in the radiology department of Nanxishan Hospital of Guangxi Zhuang Autonomous Region. Patients pathologically diagnosed with PCa were enrolled consecutively according to the eligibility criteria. A total of 180 patients (age range, 42-92 years) were included in this study. Using histopathology as the reference standard, we placed 71 cases in GG ≤2 (mean age 67.03±8.696 years) and 109 cases in GG ≥3 (age 69.60±9.638 years). Magnetic resonance imaging (MRI) parameters, including APT and ADC values, were analyzed using an independent samples t-test and binary logistic regression analysis stratified with GG. Receiver operating characteristic curve was used to analyze the diagnostic performance for different parameters distinguishing GG ≤2 and GG ≥3. APT [odds ratio (OR) for the transitional zone (TZ) PCa: 3.20, 95% CI: 1.14-8.98, P=0.02; OR for the peripheral zone (PZ) PCa: 86.32, 95% CI: 13.24-562.88, P=0.003] and ADC values (OR for TZ PCa: 89.79; 95% CI: 2.85-2,827.99, P=0.01; OR for PZ PCa: 39.92; 95% CI: 3.22-494.18, P=0.004) were independent predictors that differentiated the GG of patients. The sensitivity and specificity of the APT values were 61.1% and 81.0%, respectively, while the sensitivity and specificity of the ADC values were 83.3% and 61.9%, respectively. The optimal cutoff value of APT was 3.35% and which of ADC was 1.25×10-3 mm2/s in TZ origin PCa. At the optimal cutoff values of 3.31% (APT) and 0.79×10-3 mm2/s (ADC) in PZ PCa, the sensitivity and specificity of the APT values were 74.0% and 83.6%, respectively, while the sensitivity and specificity of the ADC values were 94.0% and 53.4%, respectively. The area under the curve of the combination of APT and ADC was significantly higher than either of APT or ADC alone in Delong test (TZ: P=0.002 and P=0.020; PZ: P=0.033 and P<0.001). APT and ADC have complementary effects on the sensitivity and specificity for identifying different PCa GGs. A combination model of APT and ADC could improve the diagnostic efficacy of PCa differentiation.

Effects of dynamic contrast enhancement on transition zone prostate cancer in Prostate Imaging Reporting and Data System Version 2.1.

The aim of the study was to analyse the effects of dynamic contrast enhanced (DCE)-MRI on transitional-zone prostate cancer (tzPCa) and clinically significant transitional-zone prostate cancer (cs-tzPCa) in Prostate Imaging Reporting and Data System (PI-RADS) Version 2.1. The diagnostic efficiencies of T2-weighted imaging (T2WI) + diffusion-weighted imaging (DWI), T2WI + dynamic contrast-enhancement (DCE), and T2WI + DWI + DCE in tzPCa and cs-tzPCa were compared using the score of ≥ 4 as the positive threshold and prostate biopsy as the reference standard. A total of 425 prostate cases were included in the study: 203 cases in the tzPCa group, and 146 in the cs-tzPCa group. The three sequence combinations had the similar areas under the curves in diagnosing tzPCa and cs-tzPCa (all P < 0.05). The sensitivity of T2WI + DCE and T2WI + DWI + DCE (84.7% and 85.7% for tzPCa; 88.4% and 89.7% for cs-tzPCa, respectively) in diagnosing tzPCa and cs-tzPCa was significantly greater than that of T2WI + DWI (79.3% for tzPCa; 82.9% for cs-tzPCa). The specificity of T2WI + DWI (86.5% for tzPCa; 74.9% for cs-tzPCa) were significantly greater than those of T2WI + DCE and T2WI + DWI + DCE (68.0% and 68.5% for tzPCa; 59.1% and 59.5% for cs-tzPCa, respectively) (all P > 0.05). The diagnostic efficacies of T2WI + DCE and T2WI + DWI + DCE had no significant differences (all P < 0.05). DCE can improve the sensitivity of diagnosis for tzPCa and cs-tzPCa, and it is useful for small PCa lesion diagnosis.

Arterial input function for quantitative dynamic contrast-enhanced MRI to diagnose prostate cancer.

PURPOSE This study aims to analyze the ability of quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to distinguish between prostate cancer (PCa) and benign lesions in transition zone (TZ) and peripheral zone (PZ) using different methods for arterial input function (AIF) determination. Study endpoints are identification of a standard AIF method and optimal quantitative perfusion parameters for PCa detection. METHODS DCE image data of 50 consecutive patients with PCa who underwent multiparametric MRI were analyzed retrospectively with three different methods of AIF acquisition. First, a region of interest was manually defined in an artery (AIFm); second, an automated algorithm was used (AIFa); and third, a population-based AIF (AIFp) was applied. Values of quantitative parameters after Tofts (Ktrans, ve, and kep) in PCa, PZ, and TZ in the three different AIFs were analyzed. RESULTS Ktrans and kep were significantly higher in PCa than in benign tissue independent from the AIF method. Whereas in PZ, Ktrans and kep could differentiate PCa (P < .001), in TZ only kep using AIFpdemonstrated a significant difference (P = .039). The correlations of the perfusion parameters that resulted from AIFm and AIFa were higher than those that resulted from AIFp, and the absolute values of Ktrans, kep, and ve were significantly lower when using AIFp. The values of quantitative perfusion parameters for PCa were similar regardless of whether PCa was located in PZ or TZ. CONCLUSION Ktrans and kep were able to differentiate PCa from benign PZ independent of the AIF method. AIFaseems to be the most feasible method of AIF determination in clinical routine. For TZ, none of the quantitative perfusion parameters provided satisfying results.

T1a/T1b transitional zone prostate cancer detection rates in patients who had TURP for clinically diagnosed benign prostatic enlargement in Southern Nigeria

Background: Transurethral resection of the prostate (TURP) removes the obstructing adenoma that grows from the transitional zone of the prostate. Resected chips are routinely sent for histologic examination. Incidental findings of prostate cancer in the TURP specimen for the clinically diagnosed benign disease may occur. The American Joint Committee on Cancer (AJCC) TNM staging of prostate cancer allocates T1a if &lt;5% of the resected chips are malignant and T1b if &gt;5% are malignant.&#x0D; Aim: To determine the frequency of incidental prostate cancer rate in TURP specimens of patients who had the procedure for clinically diagnosed benign prostatic enlargement.&#x0D; Methodology: This was a retrospective study carried out over a period of 8years from January 2013 to December 2020 on patients who had TURP for clinically diagnosed BPE or biopsy diagnosed BPH. All the patients had TURP for benign enlargements of the prostate. The resected prostate chips were sent for histopathological analysis. Patients with incomplete data and patients who had transrectal peripheral zone prostate biopsy confirmed cancers before TURP were excluded from this study. The data was collated using Microsoft Excel version 2020 and was analyzed using SPSS version 20.&#x0D; Results: There were 220 male patients with a mean age was 65.89 ±9.95years, and median age was 65years. The 60-69years group had the highest incidence of PCa. The T1a/T1b adenocarcinoma detection rate was 13.6% (30). 54.6% (120) had BPH only, and 31.8% (70) had BPH with prostatitis. All incidentally diagnosed T1a/T1b cancers were adenocarcinomas. The majority of the T1a/T1b adenocarcinomas, 43.3% (13), were poorly differentiated, while 36.7% (11) and 20% (6) had well and moderate differentiation, respectively.&#x0D; Conclusion: The Incidental T1a/T1b transitional zone adenocarcinoma detection rate in TURP specimens in the study was 13.6%. The high Gleason’s grade adenocarcinoma was the most frequent. Even when perceived as clinically benign, communication of the risk of cancer detection to the patient is pertinent and should be a routine part of the informed consenting process before TURP.

Amide Proton Transfer Could Provide More Accurate Lesion Characterization in the Transition Zone of the Prostate.

There is an overlap comparing transition zone prostate cancer (TZ PCa) and benign prostatic hyperplasia (BPH) on T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI), creating additional challenges for assessment of TZ tumors on MRI. To evaluate whether amide proton transfer-weighted (APTw) imaging provides new diagnostic ideas for TZ PCa. Prospective. A total of 51 TZ PCa patients (age, 49-89), 44 stromal BPH (age, 57-92), and 45 glandular BPH patients (age, 56-92). A 3 T; T2WI turbo spin echo (TSE), quantitative T2*-weighted imaging, DWI echo planar imaging, 3D APTw TSE. Differences in APTw, apparent diffusion coefficient (ADC), and T2* among three lesions were compared by one-way analysis of variance (ANOVA). Regions of interest were drawn by two radiologists (X.Q.Z. and X.Y.Q., with 21 and 15 years of experience, respectively). Multivariable logistic regression analyses; ANOVA with post hoc testing; receiver operator characteristic curve analysis; Delong test. Significance level: P < 0.05. APTw among TZ PCa, stromal BPH, and glandular BPH (3.48% ± 0.83% vs. 2.76% ± 0.49% vs. 2.72% ± 0.45%, respectively) were significantly different except between stromal BPH and glandular BPH (P >0.99). Significant differences were found in ADC (TZ PCa 0.76 ± 0.16 × 10-3 mm2 /sec vs. stromal BPH 0.91 ± 0.14 × 10-3 mm2 /sec vs. glandular BPH 1.08 ± 0.18 × 10-3 mm2 /sec) among three lesions. APTw (OR=12.18, 11.80, respectively) and 1/ADC (OR=703.87, 181.11, respectively) were independent predictors of TZ PCa from BPH and stromal BPH. The combination of APTw and ADC had better diagnostic performance in the identification of TZ PCa from BPH and stromal BPH. APTw imaging has the potential to be of added value to ADC in differentiating TZ PCa from BPH and stromal BPH. 2 TECHNICAL EFFICACY: Stage 2.

PI-RADS v2.1 Combined With Prostate-Specific Antigen Density for Detection of Prostate Cancer in Peripheral Zone.

PurposeTo evaluate the diagnostic performance of combining the Prostate Imaging Reporting and Data System (PI-RADS) scoring system v2.1 with prostate-specific antigen density (PSAD) to detect prostate cancer (PCa).MethodsA total of 266 participants with suspicion of PCa underwent multiparametric magnetic resonance imaging (mpMRI) in our hospital, after at least 4 weeks all patients underwent subsequent systematic transrectal ultrasound (TRUS)-guided biopsy or MRI-TRUS fusion targeted biopsy. All mpMRI images were scored in accordance with the PI-RADS v2.1, and univariate and multivariate logistic regression analyses were performed to determine significant predictors of PCa.ResultsA total of 119 patients were diagnosed with PCa in the biopsy, of them 101 patients were diagnosed with clinically significant PCa. The multivariate analysis revealed that PI-RADS v2.1 and PSAD were independent predictors for PCa. For peripheral zone (PZ), the area under the ROC curve (AUC) for the combination of PI-RADS score and PSAD was 0.90 (95% CI 0.83-0.96), which is significantly superior to using PI-RADS score (0.85, 95% CI 0.78-0.93, P=0.031) and PSAD alone (0.83, 95% CI 0.75-0.90, P=0.037). For transition zone (TZ), however, the combination model was not significantly superior to PI-RADS alone, with AUC of 0.94 (95% CI 0.89-0.99) vs. 0.93 (95% CI 0.88-0.97, P=0.186).ConclusionThe combination of PI-RADS v2.1 with PSAD could significantly improve the diagnostic performance of PCa in PZ. Nevertheless, no significant improvement was observed regarding PCa in TZ.

The role of spatial transcriptomic profiling to determine androgen receptor signaling and immune infiltration in prostate cancer

272 Background: Prostate cancer (PCa) in the transition zone (TZ) accounts for approximately 30% of disease and tends to present with higher PSAs with a lower risk of seminal vesicle invasion, extra-capsular extension, and risk of biochemical recurrence compared with peripheral zone (PZ) tumors. The underlying biological mechanism for these differences is poorly understood. Here, we performed spatial transcriptomic profiling to elucidate the molecular differences between TZ and PZ PCa. Methods: We identified three patients who underwent radical prostatectomy for PCa (one each with PZ only, TZ only, and both PZ and TZ tumors) and used the NanoString’s Digital Spatial Profiling (DSP) platform to quantify whole transcriptomic gene expression data (17,128 genes) in multiple regions of interest (ROI) per patient (42 cancer and 8 normal samples). Four morphology markers to facilitate ROI selection in both cancer and normal areas (SYTO13 for nucleus, PanCK for epithelium, SMA for stroma and CD45 for immune cells) were selected. Raw counts for 17,128 genes were imported to R v.4.1.0 for downstream data analyses. Counts were Q3 normalized and scaled (Z-score) to enable plotting of all genes on the same axes. Differential gene expression analysis using a linear model fit by empirical Bayes moderation, gene set enrichment analysis (GSEA) by cancer hallmarks, XCell gene sets for pathway enrichment and immune cell deconvolution using CIBERSORT was performed. Results: There were grade group (GG) 4 (n=10) and 5 (n=10) tumors in PZ and GG 3 (n=10), 4 (n=11) and 5 (n=1) cancers in TZ regions. We observed distinct gene expression profiles between PZ (n = 20) and TZ (n=22) tumors. Interestingly, androgen receptor (AR) signaling was significantly higher in TZ PCa ROIs compared to PZ ROIs in both GSEA (false discovery rate &lt; 5%) and the androgen subcomponent of the genomic prostate score (p&lt;0.001), regardless of grade, epithelial, stromal or immune component of the region. To standardize the comparison, CIBERSORT’s absolute immune signature scores were only computed for GG4 tumors and found to be significantly higher in PZ GG4 tumors compared to TZ GG4 tumors. Notably, CD4+ memory T cells were significantly higher in PZ GG4 regions compared to TZ GG4 tumor regions (p&lt;0.05). Conclusions: Here, we demonstrate distinct gene expression profiles of PZ and TZ PCa. Specifically, we observed higher AR signaling in TZ cancers and higher levels of immune infiltration on PZ cancers. This is in concordance with prior knowledge that TZ tumors may be associated with higher serum PSA and PZ tumors may be associated with inflammation. Further studies are needed to discern the biological and clinical significance of the different molecular features of PZ and TZ PCa.

PI-RADS version 2.1 for the evaluation of transition zone lesions: a practical guide for radiologists.

Multiparametric MRI has been established as the most accurate non-invasive diagnostic imaging tool for detecting prostate cancer (PCa) in both the peripheral zone and the transition zone (TZ) using the PI-RADS (Prostate Imaging Reporting and Data System) v. 2.1 released in 2019 as a guideline to reporting. TZ PCa remains the most difficult to diagnose due to a markedly heterogeneous background and a wide variety of atypical imaging presentations as well as other anatomical and pathological processes mimicking PCa. The aim of this paper was to present a spectrum of PCa in the TZ, as a guide for radiologists.

Fully automated detection of prostate transition zone tumors on T2‐weighted and apparent diffusion coefficient (ADC) map MR images using U‐Net ensemble

Accurate detection of transition zone (TZ) prostate cancer (PCa) on magnetic resonance imaging (MRI) remains challenging using clinical subjective assessment due to overlap between PCa and benign prostatic hyperplasia (BPH). The objective of this paper is to describe a deep-learning-based framework for fully automated detection of PCa in the TZ using T2-weighted (T2W) and apparent diffusion coefficient (ADC) map MR images. This was a single-center IRB-approved cross-sectional study of men undergoing 3T MRI on two systems. The dataset consisted of 196 patients (103 with and 93 without clinically significant [Grade Group 2 or higher] TZ PCa) to train and test our proposed methodology, with an additional 168 patients with peripheral zone PCa used only for training. We proposed an ensemble of classifiers in which multiple U-Net-based models are designed for prediction of TZ PCa location on ADC map MR images, with initial automated segmentation of the prostate to guide detection. We compared accuracy of ADC alone to T2W and combined ADC+T2W MRI for input images, and investigated improvements using ensembles over their constituent models with different methods of diversity in individual models by hyperparameter configuration, loss function and model architecture. Our developed algorithm reported sensitivity and precision of 0.829 and 0.617 in 56 test cases containing 31 instances of TZ PCa and in 25 patients without clinically significant TZ tumors. Patient-wise classification accuracy had an area under receiver operator characteristic curve (AUROC) of 0.974. Single U-Net models using ADC alone (sensitivity 0.829, precision 0.534) outperformed assessment using T2W (sensitivity 0.086, precision 0.081) and assessment using combined ADC+T2W (sensitivity 0.687, precision 0.489). While the ensemble of U-Nets with varying hyperparameters demonstrated the highest performance, all ensembles improved PCa detection compared to individual models, with sensitivities and precisions close to the collective best of constituent models. We describe a deep-learning-based method for fully automated TZ PCa detection using ADC map MR images that outperformed assessment by T2W and ADC+T2W.

Diagnosis of transition zone prostate cancer by multiparametric MRI: added value of MR spectroscopic imaging with sLASER volume selection

BackgroundCurrent multiparametric MRI (mp-MRI) in routine clinical practice has poor-to-moderate diagnostic performance for transition zone prostate cancer. The aim of this study was to evaluate the potential diagnostic performance of novel 1H magnetic resonance spectroscopic imaging (MRSI) using a semi-localized adiabatic selective refocusing (sLASER) sequence with gradient offset independent adiabaticity (GOIA) pulses in addition to the routine mp-MRI, including T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI) and quantitative dynamic contrast enhancement (DCE) for transition zone prostate cancer detection, localization and grading.MethodsForty-one transition zone prostate cancer patients underwent mp-MRI with an external phased-array coil. Normal and cancer regions were delineated by two radiologists and divided into low-risk, intermediate-risk, and high-risk categories based on TRUS guided biopsy results. Support vector machine models were built using different clinically applicable combinations of T2WI, DWI, DCE, and MRSI. The diagnostic performance of each model in cancer detection was evaluated using the area under curve (AUC) of the receiver operating characteristic diagram. Then accuracy, sensitivity and specificity of each model were calculated. Furthermore, the correlation of mp-MRI parameters with low-risk, intermediate-risk and high-risk cancers were calculated using the Spearman correlation coefficient.ResultsThe addition of MRSI to T2WI + DWI and T2WI + DWI + DCE improved the accuracy, sensitivity and specificity for cancer detection. The best performance was achieved with T2WI + DWI + MRSI where the addition of MRSI improved the AUC, accuracy, sensitivity and specificity from 0.86 to 0.99, 0.83 to 0.96, 0.80 to 0.95, and 0.85 to 0.97 respectively. The (choline + spermine + creatine)/citrate ratio of MRSI showed the highest correlation with cancer risk groups (r = 0.64, p < 0.01).ConclusionThe inclusion of GOIA-sLASER MRSI into conventional mp-MRI significantly improves the diagnostic accuracy of the detection and aggressiveness assessment of transition zone prostate cancer.

Arterial spin labelling as a gadolinium-free alternative in the detection of prostate cancer

PurposeTo determine the capability of Gadolinium-free arterial spin labelling (ASL) sequences as novel, contrast-free, non-invasive alternative perfusion imaging method to differentiate prostate cancer (PCA) from benign prostate tissue compared to conventional DCE MRI. MethodsThirty men with histologically confirmed PCA were included in this prospectively enrolled single center cohort study. All patients received multiparametric MRI (T2, DWI, DCE) at 3 T with additional ASL of the PCA lesion. Primary endpoint was differentiability of PCA versus benign prostate tissue by signal intensities (SI) and contrast ratios (CR) in ASL in comparison to DCE. For DCE also Signal-Enhancement-Ratio (SER) of native and early contrast enhancement SI was assessed. Secondary objectives were differences regarding PCA localisation in peripheral (PZ) or transition zone (TZ) and PCA detection. ResultsIn both, ASL and DCE, average SI of PCA differed significantly from SI in benign tissue in the TZ and PZ (p < 0,01, respectively). ASL had significantly higher CR discerning PCA and benign tissue in PZ and TZ (PZ = 5.19; TZ = 6.45) compared to DCE SI (PZ = 1.61; TZ = 1.43) and DCE SER (PZ = 1.59; TZ = 1.43) (p < 0.01, respectively). In subjective evaluation, PCA could be detected in ASL in 28 patients, compared to 29 in DCE. ConclusionASL had significantly higher CR differentiating PCA from benign tissue in PZ and TZ compared to DCE. Visual detection of PCA does not differ significantly between the two sequences. As perfusion gadolinium-based contrast media is seen more critical in the last few years, ASL seems to be a promising alternative to DCE in PCA detection.

Diagnostic Accuracy and Interobserver Agreement of PI-RADS Version 2 and Version 2.1 for the Detection of Transition Zone Prostate Cancers

BACKGROUND. PI-RADS version 2.1 (v2.1) introduced a number of key changes to the assessment of transition zone (TZ) lesions. OBJECTIVE. The purpose of this study was to evaluate interobserver agreement and diagnostic accuracy for detecting TZ prostate cancer (PCa) and clinically significant PCa (csPCa) by use of PI-RADS v2 and PI-RADS v2.1 among radiologists with different levels of experience. METHODS. This retrospective study included 355 biopsy-naïve patients who from January 2017 to March 2020 underwent prostate MRI that showed a TZ lesion and underwent subsequent biopsy. PCa was diagnosed in 93 patients (International Society of Urological Pathology [ISUP] grade group 1, n = 34; ISUP grade group ≥ 2, n = 59) and non-cancerous lesions in 262 patients. Five radiologists with varying experience in prostate MRI scored lesions using PI-RADS v2 and PI-RADS v2.1 in sessions separated by at least 4 weeks. Interobserver agreement was evaluated with kappa and Kendall W statistics. ROC curve analysis was used to evaluate performance in detection of TZ PCa and csPCa. RESULTS. Interobserver agreement among all readers was higher for PI-RADS v2.1 than for PI-RADS v2 (mean weighted κ = 0.700 vs 0.622; Kendall W = 0.805 vs 0.728; p = .03). The pooled AUC values for detecting TZ PCa and csPCa were higher among all readers using PI-RADS v2.1 (0.866 vs 0.827 for TZ PCa; 0.929 vs 0.899 for TZ csPCa; p < .001). For detecting TZ PCa, the pooled sensitivity, specificity, and accuracy were 86.9%, 79.4%, and 75.4% among all readers for PI-RADS v2.1 compared with 79.4%, 71.8%, and 73.8% for PI-RADS v2. For detecting TZ csPCa, the pooled sensitivity, specificity, and accuracy were 84.8%, 90.9%, and 89.9% among all readers for PI-RADS v2.1 compared with 81.4%, 89.9%, and 88.5% for PI-RADS v2. Reader 1, who had the least experience, had the lowest sensitivity, specificity, and accuracy (78.0%, 89.2%, and 87.3%). Reader 5, who had the most experience, had the highest sensitivity, specificity, and accuracy (88.1%, 92.9%, and 92.1%) in detecting csPCa. CONCLUSION. PI-RADS v2.1 had better interobserver agreement and diagnostic accuracy than PI-RADS v2 for evaluating TZ lesions. Reader experience continues to affect the performance of prostate MRI interpretation with PI-RADS v2.1. CLINICAL IMPACT. PI-RADS v2.1 is more accurate and reproducible than PI-RADS v2 for the diagnosis of TZ PCa.

Comparison of mono-exponential, bi-exponential, kurtosis, and fractional-order calculus models of diffusion-weighted imaging in characterizing prostate lesions in transition zone.

To compare various models of diffusion-weighted imaging including mono-exponential, bi-exponential, diffusion kurtosis (DK) and fractional-order calculus (FROC) models in diagnosing prostate cancer (PCa) in transition zone (TZ) and distinguish the high-grade PCa [Gleason score (GS) ≥ 7] lesions from the total of low-grade PCa (GS ≤ 6) lesions and benign prostatic hyperplasia (BPH) in TZ. 80 Patients with 103 lesions were included in this study. Nine metrics [including apparent diffusion coefficient (ADC) derived from mono-exponential model, slow diffusion coefficient (Ds), fast diffusion coefficient (Df),, and f (the fraction of fast diffusion) from bi-exponential model; mean diffusivity (MD) and mean kurtosis (MK) from DK model; diffusion coefficient (D), fractional-order derivative in space (β), and spatial metric (μ) from FROC model] were calculated. Comparisons between BPH and PCa lesions as well as between clinically significant PCa (CsPCa) (GS ≥ 7, n = 31) and clinically insignificant lesions (Cins) (GS ≤ 6 and BPH, n = 72) of these metrics were conducted. Mann-Whitney U-test and receiver operating characteristic (ROC) analysis were used for statistical evaluations. The areas under the ROC curve (AUC) values of β derived from FROC model were 0.778 and 0.853 in differentiating PCa from BPH and in differentiating CS (GS ≥ 7) from Cins (GS ≤ 6 and BPH), both were the highest compared to other metrics. The AUC value of β was significantly higher than that of ADC (P = 0.009) in differentiating CS from Cins, while the differentiation between BPH and PCa did not reach the statistical significance when comparing with ADC (P = 0.089). Although no significant difference was found in distinguishing PCa from BPH, the metric β derived from FROC model was superior to other diffusion metrics in differentiation between CS and Cins in TZ.

P054 - Transition zone prostate cancer: Detection by second-look micro-ultrasonography after biparametric MRI

P054 - Transition zone prostate cancer: Detection by second-look micro-ultrasonography after biparametric MRI