Malignant Prostatic Lesions Research Articles

To study ERG expression in benign, pre-neoplastic, and neoplastic lesions of prostate

Background: Prostate cancer (PCa) is the second most common malignancy in men and the sixth leading cause of death. Clinical course of PCa is highly variable and current clinico pathological parameters are unable to predict accurately the course of the disease because of genetic heterogeneity seen in this tumor. Aims and Objectives: The aim of the study was to study the prevalence of E26 transformation-specific-related gene (ERG) expression in benign and malignant lesions of prostate in Northern Indian patients. Whether ERG expression using immunohistochemical (IHC) methods can also be used for differentiating various lesions of the prostate gland is also evaluated. Materials and Methods: This is an observational study conducted on 50 biopsy specimens encompassing different lesions of prostate. The study was carried out in the Department of Pathology at Integral Institute of Medical Sciences and Research, Lucknow, India, during the period January 2020–September 2022. Results: Amongst 50 cases of prostatic lesions included in the study, there were 20 cases of benign prostatic hyperplasia (BPH), 17 cases of benign mimicker lesions and 13 cases of PCa. ERG expression was absent in all cases of BPH and benign mimicker lesions. ERG expression was seen in 38.5% of cases of PCa. Conclusion: In our study, none of the patients of BPH or benign mimicker lesion expressed ERG on IHC, while 38.5% of patients of PCa showed positive staining. This figure differs from reports from other parts of the world. ERG expressions can be a potential therapeutic target for personalized treatment in a subset of patients of PCa. However, it was not found to be a sensitive marker for the diagnosis of PCa.

Arterial embolization in the treatment of benign and malignant prostate lesions

Arterial embolization in the treatment of benign and malignant prostate lesions

Quantitative Evaluation of Apparent Diffusion Coefficient Values, ISUP Grades and Prostate-Specific Antigen Density Values of Potentially Malignant PI-RADS Lesions

The aim of this study was to demonstrate the correlation between ADC values and the ADC/PSAD ratio for potentially malignant prostate lesions classified into ISUP grades and to determine threshold values to differentiate benign lesions (noPCa), clinically insignificant (nsPCa) and clinically significant prostate cancer (csPCa). We enrolled a total of 403 patients with 468 prostate lesions, of which 46 patients with 50 lesions were excluded for different reasons. Therefore, 357 patients with a total of 418 prostate lesions remained for the final evaluation. For all lesions, ADC values were measured; they demonstrated a negative correlation with ISUP grades (p < 0.001), with a significant difference between csPCa and a combined group of nsPCa and noPCa (ns-noPCa, p < 0.001). The same was true for the ADC/PSAD ratio, but only the ADC/PSAD ratio proved to be a significant discriminator between nsPCa and noPCa (p = 0.0051). Using the calculated threshold values, up to 31.6% of biopsies could have been avoided. Furthermore, the ADC/PSAD ratio, with the ability to distinguish between nsPCa and noPCa, offers possible active surveillance without prior biopsy.

Improving diagnostic efficacy of primary prostate cancer with combined 99mTc-PSMA SPECT/CT and multiparametric-MRI and quantitative parameters.

This prospective study aimed to evaluate the difference between 99mTc-PSMA single-photon emission computed tomography (SPECT)/CT and multiparametric magnetic resonance imaging (mpMRI) in the detection of primary prostate cancer (PCa). Fifty-six men with suspected PCa between October 2019 and November 2022 were prospectively enrolled in this study. The median age of the patients was 70 years (range, 29-87 years). Patients were divided into high-(Gleason score>7, n=31), medium- (Gleason score=7, n=6) and low-risk groups (Gleason score < 7, n=6). All patients underwent 99mTc-PSMA SPECT/CT and mpMRI at an average interval of 3 days (range, 1-7 days). The maximum standardized uptake value (SUVmax), the minimum apparent diffusion coefficient (ADCmin), and their ratio (SUVmax/ADCmin) were used as imaging parameters to distinguish benign from malignant prostatic lesions. Of the 56 patients, 12 were pathologically diagnosed with a benign disease, and 44 were diagnosed with PCa. 99mTc-PSMA SPECT/CT and mpMRI showed no significant difference in the detection of primary PCa (kappa =0.401, P=0.002), with sensitivities of 97.7% (43/44) and 90.9% (40/44), specificities of 75.0% (9/12) and 75.0% (9/12), and AUC of 97.4% and 95.1%, respectively. The AUC of SUVmax/ADCmin was better than those of SUVmax or ADCmin alone. When SUVmax/ADCmin in the prostatic lesion was >7.0×103, the lesion was more likely to be malignant. When SUVmax/ADCmin in the prostatic lesion is >27.0×103, the PCa patient may have lymph node and bone metastases. SUVmax was positively correlated with the Gleason score (r=0.61, P=0.008), whereas ADCmin was negatively correlated with the Gleason score (r=-0.35, P=0.023). SUVmax/ADCmin was positively correlated with the Gleason score (r=0.59, P=0.023). SUVmax/ADCmin was the main predictor of the high-risk group, with an optimal cut-off value of 15.0×103. The combination of 99mTc-PSMA SPECT/CT and mpMRI can improve the diagnostic efficacy for PCa compared with either modality alone; SUVmax/ADCmin is a valuable differential diagnostic imaging parameter.

Direct Patlak Reconstruction of [68Ga]Ga-PSMA PET for the Evaluation of Primary Prostate Cancer Prior Total Prostatectomy: Results of a Pilot Study.

To investigate the use of kinetic parameters derived from direct Patlak reconstructions of [68Ga]Ga-PSMA-11 positron emission tomography/computed tomography (PET/CT) to predict the histological grade of malignancy of the primary tumor of patients with prostate cancer (PCa). Thirteen patients (mean age 66 ± 10 years) with a primary, therapy-naïve PCa (median PSA 9.3 [range: 6.3-130 µg/L]) prior radical prostatectomy, were recruited in this exploratory prospective study. A dynamic whole-body [68Ga]Ga-PSMA-11 PET/CT scan was performed for all patients. Measured quantification parameters included Patlak slope (Ki: absolute rate of tracer consumption) and Patlak intercept (Vb: degree of tracer perfusion in the tumor). Additionally, the mean and maximum standardized uptake values (SUVmean and SUVmax) of the tumor were determined from a static PET 60 min post tracer injection. In every patient, initial PSA (iPSA) values that were also the PSA level at the time of the examination and final histology results with Gleason score (GS) grading were correlated with the quantitative readouts. Collectively, 20 individual malignant prostate lesions were ascertained and histologically graded for GS with ISUP classification. Six lesions were classified as ISUP 5, two as ISUP 4, eight as ISUP 3, and four as ISUP 2. In both static and dynamic PET/CT imaging, the prostate lesions could be visually distinguished from the background. The average values of the SUVmean, slope, and intercept of the background were 2.4 (±0.4), 0.015 1/min (±0.006), and 52% (±12), respectively. These were significantly lower than the corresponding parameters extracted from the prostate lesions (all p < 0.01). No significant differences were found between these values and the various GS and ISUP (all p > 0.05). Spearman correlation coefficient analysis demonstrated a strong correlation between static and dynamic PET/CT parameters (all r ≥ 0.70, p < 0.01). Both GS and ISUP grading revealed only weak correlations with the mean and maximum SUV and tumor-to-background ratio derived from static images and dynamic Patlak slope. The iPSA demonstrated no significant correlation with GS and ISUP grading or with dynamic and static PET parameter values. In this cohort of mainly high-risk PCa, no significant correlation between [68Ga]Ga-PSMA-11 perfusion and consumption and the aggressiveness of the primary tumor was observed. This suggests that the association between SUV values and GS may be more distinctive when distinguishing clinically relevant from clinically non-relevant PCa.

The value of DISCO and MUSE-DWI combined with prostate specific antigen density in the diagnosis and risk stratification of prostate cancer

Objective: To explore the value of differential subsampling with cartesian ordering (DISCO) and multiplexed sensitivity-encoding diffusion weighted-imaging (MUSE-DWI) combined with prostate specific antigen density (PSAD) in the diagnosis and risk stratification of prostate cancer (PCa). Methods: The data of 183 patients [aged from 48 to 86 (68±8) years] with prostate diseases in the General Hospital of Ningxia Medical University from July 2020 to August 2021 were retrospectively collected. Those patients were divided into non-PCa group (n=115) and PCa group (n=68) based on the disease condition. According to the risk degree, PCa group was subdivided into low risk PCa group (n=14) and medium-to-high risk PCa group (n=54). The differences of volume transfer constant (Ktrans), rate constant (Kep), extracellular volume fraction (Ve), apparent diffusion coefficient (ADC) and PSAD between groups were analyzed. Receiver operating characteristic (ROC) curves analysis were conducted for evaluating the diagnostic efficacy of quantitative parameters and PSAD in distinguishing non-PCa and PCa, low-risk PCa and medium-high risk PCa. Multivariate logistic regression model was used for screening out the predictors, which was statistically significant differences between non-PCa group and PCa group, for PCa prediction. Results: Ktrans, Kep, Ve and PSAD of PCa group all were higher than those of non-PCa group, and ADC value was lower than that of non-PCa group, and the differences all were statistically significant (all P<0.001). Ktrans, Kep and PSAD of medium-to-high risk PCa group all were higher than those of low risk PCa group, and ADC value was lower than that of low risk PCa group, and the differences were all statistically significant (all P<0.001). When distinguishing non-PCa from PCa, the area under ROC curve (AUC) of the combined model (Ktrans+Kep+Ve+ADC+PSAD) was higher than that of any single index [0.958 (95%CI: 0.918-0.982) vs 0.881 (95%CI: 0.825-0.924), 0.836 (95%CI: 0.775-0.887), 0.672 (95%CI: 0.599-0.740), 0.940(95%CI: 0.895-0.969), 0.816(95%CI:0.752-0.869), all P<0.05]. When distinguishing low-risk PCa and medium-to-high risk PCa, the AUC of the combined model (Ktrans+Kep+ADC+PSAD) were higher than those of Ktrans, Kep and PSAD[0.933 (95%CI: 0.845-0.979) vs 0.846 (95%CI:0.738-0.922), 0.782 (95%CI:0.665-0.873), 0.84 8(95%CI: 0.740-0.923), all P<0.05]. The multivariate logistic regression analysis showed that Ktrans (OR=1.005, 95%CI:1.001-1.010) and ADC values (OR=0.992, 95%CI:0.989-0.995) were predictors of PCa (P<0.05). Conclusions: DISCO and MUSE-DWI combined with PSAD can distinguish benign and malignant prostate lesions. Ktrans and ADC values were predictors of PCa; Ktrans, Kep, ADC values and PSAD are helpful in predicting the biological behavior of PCa.

Value of synthetic MRI quantitative parameters in preprocedural evaluation for TRUS/MRI fusion-guided biopsy of the prostate.

Transrectal ultrasonography (TRUS)/magnetic resonance imaging (MRI) fusion-guided biopsy has a high clinical application value. However, this technique has some limitations, which limit its use in routine clinical practice. Therefore, the selection of suitable proatate lesions for this technique is worthy of our attention. Synthetic MRI (SyMRI) is capable of quantifying multiple relaxation parameters, which might have potential value in preprocedural evaluation for TRUS/MRI fusion-guided biopsy of the prostate. The aim of our study is to examine the value of SyMRI quantitative parameters in preprocedural evaluation for TRUS/MRI fusion-guided biopsy of the prostate. We prospectively selected 148 lesions in 137 patients who underwent prostate biopsy in our hospital. Next, 2-4 needles of TRUS/MRI fusion-guided biopsy combined with 10 needles of system biopsy(SB) were used as the protocol for prostate biopsy. Before biopsy, the MAGiC sequences of the MRI images of the enrolled patients underwent post-processing, and the longitudinal relaxation time (T1), transverse relaxation time (T2), and proton density (PD) were extracted. The biopsy pathology results were used as a gold standard to compare the differences in SyMRI quantitative parameters between benign and malignant prostate lesions in the peripheral and transitional zones. The receiver operating characteristic (ROC) curves were plotted to confirm the optimal SyMRI quantitative parameter for prostate lesion benignancy/malignancy performance, and the cutoff values of these parameters were used for grouping the lesions. The single-needle biopsy prostate cancer (PCa)-positivity rates (number of positive biopsy needles/total biopsy needles) and PCa overall detection rates by TRUS/MRI fusion-guided biopsy and SB were compared in different subgroups. The T1 and T2 values can determine the benignancy/malignancy of prostate transition lesions(p < 0.01), and the T2 value has a greater diagnostic performance (p = 0.0376). The T2 value can determine the benignancy/malignancy of prostate peripheral lesions. The optimal diagnostic cutoff values for T2 were 77 and 81 ms, respectively. The single-needle PCa positivity rate of TRUS/MRI fusion-guided biopsy was higher than SB for any prostate lesions in different subgroups (p < 0.01). However, only in the subgroup of transition zone lesions with T2 ≤ 77 ms, the PCa overall detection rate of TRUS/MRI fusion-guided biopsy was significantly higher than that of SB (p = 0.031). SyMRI-T2 value can provide a theoretical basis for the selection of suitable lesions for TRUS/MRI fusion-guided biopsy.

Quantification of Multi-Parametric Magnetic Resonance Imaging Based on Radiomics Analysis for Differentiation of Benign and Malignant Lesions of Prostate.

The most common cancer (non-cutaneous) malignancy among men is prostate cancer. Management of prostate cancer, including staging and treatment, playing an important role in decreasing mortality rates. Among all current diagnostic tools, multiparametric MRI (mp-MRI) has shown high potential in localizing and staging prostate cancer. Quantification of mp-MRI helps to decrease the dependency of diagnosis on readers' opinions. The aim of this research is to set a method based on quantification of mp-MRI images for discrimination between benign and malignant prostatic lesions with fusion-guided MR imaging/transrectal ultrasonography biopsy as a pathology validation reference. It is an analytical research that 27 patients underwent the mp-MRI examination, including T1- and T2- weighted and diffusion weighted imaging (DWI). Quantification was done by calculating radiomic features from mp-MRI images. Receiver-operating-characteristic curve was done for each feature to evaluate the discriminatory capacity and linear discriminant analysis (LDA) and leave-one-out cross-validation for feature filtering to estimate the sensitivity, specificity and accuracy of the benign and malignant lesion differentiation process is used. An accuracy, sensitivity and specificity of 92.6%, 95.2% and 83.3%, respectively, were achieved from a subset of radiomics features obtained from T2-weighted images and apparent diffusion coefficient (ADC) maps for distinguishing benign and malignant prostate lesions. Quantification of mp-MRI (T2-weighted images and ADC-maps) based on radiomics feature has potential to distinguish benign with appropriate accuracy from malignant prostate lesions. This technique is helpful in preventing needless biopsies in patients and provides an assisted diagnosis for classifications of prostate lesions.

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

BackgroundEarly diagnosis of prostate cancer improves its prognosis, while it is essential to upgrade screening tools. This study aimed to explore the value of a novel functional magnetic resonance imaging (MRI) technique, namely amide proton transfer (APT)-weighted MRI, combined with serum prostate-specific antigen (PSA) levels to differentiate malignant prostate lesions from benign prostate lesions.MethodsData of patients who underwent prostate examinations at Chongqing University Cancer Hospital between July 2019 and March 2022 were retrospectively analyzed. All patients underwent T2-weighted imaging (T2WI), APT, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) MRI. Two radiologists analyzed the images independently. The ability of the quantitative parameters alone or in different combinations in differentiating malignant prostate lesions from benign prostate lesions were compared by using receiver operating characteristic (ROC) curves. According to the DeLong test, the combined parameters were significantly different from the corresponding single parameter (P < 0.05).ResultsA total of 79 patients were finally enrolled, including 52 patients in the malignant group and 27 patients in the benign group. The separate assessment of indexes revealed that APTmax, APTmean, mean apparent diffusion coefficient (ADCmean), ADCmax, ADCmin, tPAD, free prostate-specific antigen (FPSA), FPSA/total prostate-specific antigen (tPSA), and PSA density (PSAD) were significantly different between the two groups (P < 0.05), while APTmin was not significantly different between the two groups (P > 0.05). APTmax and APTmean had the high values of area under the ROC curve (AUC), which were 0.780 and 0.710, respectively. APTmax had a high sensitivity, and APTmean had a high specificity. The combination of APTmax, APTmean, ADCmean, and PSAD had the highest AUC value (AUC: 0.880, sensitivity: 86.540, specificity: 78.260).ConclusionAPTmax, APTmean, ADCmean, ADCmin, tPAD, FPSA, and PSAD showed to have a high value in differentiating malignant prostate lesions from benign prostate lesions in the separate assessment of indexes. The combination of APTmax, APTmean, ADCmean, and PSAD had the highest diagnostic value.

Clinicopathological correlation of various prostatic lesions with serum prostate-specific antigen level – A hospital-based cross-sectional study

Background: The prostate is a male reproductive accessory gland which gives rise to several pathological conditions. The present study aims to study the correlation of histopathology of prostatic diseases with the clinical profile of the patient coming to VSS Medical College (VSSMC), Burla for histopathological reporting of prostatic biopsies. Aim and Objectives: The objectives of this study were as follows: (1) To correlate serum prostate-specific antigen (PSA) level with various clinical and pathological lesions of the prostrate gland, in a Hospital-based sample of Western Odisha population. (2) As the male gender represents almost half of the entire population, to find out different clinicopathological lesions, percentage-wise, so as to know the frequency of occurrence of such lesions. Materials and Methods: Fifty-seven cases of prostatic diseases were recorded in the Department of Pathology, VSSMC, Burla for histopathological examination of prostatic tissue for 2 years from September 2017 to August 2019. All the samples were fixed in 5 μ sections with 10% neutral buffered formalin. Then Hematoxylin and Eosin stain (H and E stain) was used for staining. Appropriate clinical data comprising signs and symptoms, laboratory findings, and serum PSA levels were recorded. Results: The present study, in the beginning, consisted of 64 patients, out of which seven patients were excluded by exclusion criteria, and finally, 57 cases were analyzed. The most prevalent age group for all prostatic diseases was 61–70 years with a mean of 67 years. The majority of the cases (73.67%) were benign mostly benign prostatic hyperplasia (BPH). Prostatic intraepithelial neoplasia (PIN) was 12.29% and malignant cases (all adenocarcinoma) were also 12.29%. All patients showed prostatomegaly. About 50% of cases suggestive of malignancy by digital rectal examination (DRE) were confirmed to be carcinoma by histopathological study. Serum creatinine level was raised in 16.27% of benign and 21.42% of premalignant and malignant conditions. Serum PSA level below 4 ng/mL were all benign and above 10 ng/mL were majority malignant conditions. However, at level 4–10 ng/mL, BPH and PIN cases were overlapped with cases of carcinoma. Conclusion: Benign prostatic hyperplasia is the most commonly encountered prostatic lesion. Serum PSA level &lt;4 ng/mL is associated with benign conditions and more than 10 ng/mL is associated more with carcinomas. Although, investigations such as DRE and serum PSA estimation aid in diagnosis, a definitive diagnosis of benign and malignant lesions of prostate can be made by histopathological study of prostatic biopsies.

Correlation of multiparametric MRI with histopathological grade of peripheral zone prostate carcinoma.

Prostatic cancer is the second most common malignant tumor in men. Preoperative grading of prostate cancer is important for its management. Our objective is to compare individual and combined detection rates of T2-weighted imaging (T2WI), diffusion weighted imaging (DWI), dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI), and magnetic resonance spectroscopy (MRS) for prostate cancer with histopathological diagnosis as its golden standard. Forty-four patients with positive digital rectal examination (DRE) findings and elevated prostate specific antigen (PSA), underwent multiparametric MRI (Mp-MRI). T2WI, DWI, DCE-MRI and MRS were done in all the patients. Cognitive magnetic resonance-transrectal ultrasound (MR-TRUS) fusion biopsy was done in all the patients. Sensitivity and specificity of T2WI, DWI, DCE-MRI, and Prostate Imaging - Reporting and Data System PIRADS version 2 was obtained. Apparent diffusion coefficient (ADC) value and choline/citrate ratio were obtained for each lesion and correlated with histopathological grade. The mean age of the patients was 68.7 ± 10.1 years, and the mean serum PSA level was 58.1 ± 22.4 ng/dL. Of the 38 lesions in peripheral zone, 33 (87%) had histopathologically proven prostate cancer. T2WI had a sensitivity and specificity of 75.8% and 80% and DWI had a sensitivity and specificity of 90.9% and 80%, respectively, for detection of malignant prostatic lesion. The mean ADC values for prostate cancer, prostatitis, and normal prostatic parenchyma were 0.702 ± 0.094 × 10-3 mm2/sec, 0.959 ± 0.171 × 10-3 mm2/sec, and 1.31 ± 0.223 × 10-3 mm2/sec, respectively. Type 3 curve has lower sensitivity (45.5%) but high specificity (80%) for diagnosing prostate cancer. DWI can be useful to differentiate benign from malignant prostatic lesions, and low-grade from high-grade prostate carcinoma. ADC value has a positive correlation with histopathological grade of prostate cancer.

Retracted: Magnetic Resonance Image Compilation Was Used in Conjunction with Prostate PI-RADS v2.1 Score Has Diagnostic Relevance for Benign and Malignant Prostate Lesions.

[This retracts the article DOI: 10.1155/2022/3613540.].

Utility of p63 and Alpha Methyl Acyl CoA Racemase in the Lesions of Prostatic Mimickers: An Observational Study

Introduction: Prostate adenocarcinoma is the most commonly occurring cancer in men. Differentiation of prostatic adenocarcinoma from prostatic mimickers sometimes difficult on the sole basis of histologic findings. Diagnosis of carcinoma is based on Immunohistochemistry (IHC) by negative immune reaction with p63 and positive staining with Alpha Methyl Acyl CoA Racemase (AMACR). Aim: To evaluate the utility of p63 and AMACR in the prostatic mimickers. Materials and Methods: In this analytical observational study, all prospective prostatic mimickers with transrectal ultrasound (TRUS) guided biopsies received in the pathology department of Bharati Vidyapeeth Medical College, Pune, India in a tertiary care hospital from July 2020 to July 2022 were studied. 70 cases were collected, studied and analysed which included 38 Transurethral Resection of the Prostate (TURP) specimens and 32 TRUS guided tissue biopsies. The Haematoxylin &amp; Eosin (H&amp;E) stained slides of all the cases were examined and divided into three categories-malignant (08 cases), prostatic mimickers (53 cases) and suspicious for malignancy (09 cases). Furthur, these cases were subjected to IHC for p63 and AMACR. Statistical Package for Social Sciences (SPSS) version 25.0 software was used for data analysis using chi square test along with p-value. Results: Out of the 53 cases of prostatic mimickers and 09 cases were suspicious for malignancy, 56 were positive for p63 and 06 were negative for p63. There was a statistically significant association between p63 IHC staining and the differentiation of benign cases from malignant prostatic lesions (p&lt;0.001). The sensitivity and specificity of p63 were 93.3% and 100% respectively. There were 08 cases of prostate adenocarcinoma stained with AMACR, all were positive. The sensitivity and specificity being 100%. Results revealed that there was a significant statistical association between the positive IHC staining of AMACR and prostatic adenocarcinoma (p&lt;0.001). Conclusion: The diagnostic performance of p63 and AMACR proves to be significant in differentiating between the mimickers of prostatic lesions and adenocarcinoma. IHC is recommended to reduce diagnostic error in suspected cases.

Suppression of prostate cancer and amelioration of the immunosuppressive tumor microenvironment through selective immunoproteasome inhibition

ABSTRACT New treatment options to battle hormone-refractory prostate carcinoma (PC) are a pressing medical need. Chronic inflammation has been implicated in PC etiology. The pro-inflammatory cytokines IL-6, IL-23 and IL-17 are key mediators to promote growth of PC. Here, we evaluate the potential of immunoproteasome inhibition for anti-inflammatory and direct anti-tumorigenic therapy of PC. The anti-tumor effect of immunoproteasome inhibitor ONX 0914 was tested in mouse and human PC cells and the in vivo therapeutic efficacy of immunoproteasome inhibition was analyzed in transgenic adenocarcinoma of the mouse prostate (TRAMP) mice in preventive and therapeutic settings and in castration-resistant (CR)PC after castration. Inhibition of the immunoproteasome subunit LMP7 induced apoptotic cell death in PC cell lines. In TRAMP mice, ONX 0914-treatment resulted in significant inhibition of PC growth with a decreased frequency of malignant prostatic lesions and inhibition of metastasis formation. The number of immunosuppressive myeloid cells in PC was greatly reduced in response to ONX 0914. Thus, immunoproteasome inhibition shows remarkable efficacy against PC progression in vivo and impedes tumor recurrence in CRPC-TRAMP mice by blocking the immunosuppressive inflammatory response in the tumor microenvironment. In conclusion, we show that the immunoproteasome is a promising drug target for the treatment of PC.

Radiomics-based machine-learning method to diagnose prostate cancer using mp-MRI: a comparison between conventional and fused models.

Multiparametric MRI (mp-MRI) has been significantly used for detection, localization and staging of Prostate cancer (PCa). However, all the assessment suffers from poor reproducibility among the readers. The aim of this study was to evaluate radiomics models to diagnose PCa using high-resolution T2-weighted (T2-W) and dynamic contrast-enhanced (DCE) MRI. Thirty two patients who had high prostate specific antigen level were recruited. The prostate biopsies considered as the reference to differentiate between 66 benign and 36 malignant prostate lesions. 181 features were extracted from each modality. K-nearest neighbors, artificial neural network, decision tree, and linear discriminant analysis were used for machine-learning study. The leave-one-out cross-validation method was used to prevent overfitting and build robust models. Radiomics analysis showed that T2-W images were more effective in PCa detection compare to DCE images. Local binary pattern features and speeded up robust features had the highest ability for prediction in T2-W and DCE images, respectively. The classifier fusion using decision template method showed the highest performance with accuracy, specificity, and sensitivity of 100%. The findings of this framework provide researchers on PCa with a promising method for reliable detection of prostate lesions in MR images by fused model.

Magnetic Resonance Image Compilation Was Used in Conjunction with Prostate PI-RADS v2.1 Score Has Diagnostic Relevance for Benign and Malignant Prostate Lesions.

Objective To assess the diagnostic usefulness of magic in conjunction with PI-RADS v2.1 for prostate cancer malignant foci. Methods A total of 202 lesions (97 transitional zone lesions and 105 peripheral zone lesions) from 198 people were investigated retrospectively using traditional MRI and magic images. Each lesion has a unique pathological consequence. Lesions T1, T2, and PD values were employed as magic observation markers. The locations of the lesions were aggregated, and the paired t-test and receiver operating characteristic curve (ROC) were employed to find the indices with statistical significance in separating benign from malignant prostatic nodules (+1 point) and (−1 point) respectively. Draw a ROC curve and compare it to the PI-RADS v2.1 score using the magic positive and negative indices as well as the PI-RADS v2.1 score. By comparing the ROC curves scored separately, the diagnostic efficiency of the two scoring approaches for benign and malignant prostate lesions was investigated. Results T2 value has the highest diagnostic efficiency among the magic observation indices. T2 value of 77 ms for transitional zone lesions and T2 value of 89 ms for peripheral zone lesions are positive indices, whereas T2 value >77 ms and T2 value >89 ms are negative indexes. PI-RADS v2.1 combines one score and magic. In the transitional zone, the sensitivity, specificity, positive predictive value, and negative predictive value of the two scoring methods were 57.52, 87.70, 76.70, and 74.6 percent and 82.50, 73.68, 95.5, and 74.7 percent, respectively, and the AUC values were 0.735 and 0.846, respectively (P = 0.004); in the peripheral zone, the AUC values were 86.15 percent, 68.42 percent, 82.4. Conclusions Magic T2 value is a favorable sign for diagnosing benign and malignant prostate cancers when used in conjunction with PI-RADS v2.1. The end product exceeds PI-RADS v2.1 on its own, which is more useful in identifying benign and malignant prostate lesions, decreasing unnecessary puncture and alleviating patient pain.

Cut-off Point of Ki-67 Proliferation Marker in Differentiating Premalignant and Malignant Prostatic Lesions

Immunohistochemistry Ki-67 is a marker of cell proliferation. Using immunohistochemical markers can be helpful to determine and diagnose prostate intraepithelial neoplasia (PIN) and carcinoma prostate. There are many studies about Ki67 as a prognostic tool, a diagnostic tool, and a potential therapeutic target for cancer therapy, but there are no standard criteria to define the cut-off point for the value of Ki-67 in premalignant and malignant prostate lesions.

Texture analysis based on PI-RADS 4/5-scored magnetic resonance images combined with machine learning to distinguish benign lesions from prostate cancer.

BackgroundThe global morbidity and mortality of prostate cancer (PCa) increase sharply every year. Early diagnosis is essential; it determines survival and outcome. So, this study extracted the texture features of apparent diffusion coefficient images in multiparametric magnetic resonance imaging (mp-MRI) and built machine learning models based on radiomics texture analysis (TA) to determine its ability to distinguish benign from PCa lesions using the Prostate Imaging Reporting and Data System (PI-RADS) 4/5 score.MethodsWe enrolled 103 patients who underwent mp-MRI examinations and transrectal ultrasound and magnetic resonance fusion imaging (TRUS-MRI) targeted prostate biopsy and obtained pathological confirmation at our hospital from August 2017 to January 2020. We used ImageJ software to obtain texture feature parameters based on apparent diffusion coefficient (ADC) images, then standardized texture feature parameters, and used LASSO regression to reduce multiple feature parameters; 70% of the cases were randomly selected from the PCa group and the benign prostate hyperplasia group as the training set. The remaining 30% was used as the test set. The machine learning classification model for identifying benign and malignant prostate lesions was constructed using the feature parameters after dimensionality reduction. The clinical indicators were statistically analyzed, and we constructed a machine learning classification model based on clinical indicators of benign and malignant prostate lesions. Finally, we compared the model’s performance based on radiomics texture features and clinical indicators to identify benign and malignant prostate lesions in PI-RADS 4/5 score.ResultsThe area under the curve (AUC) of the R-logistic model test set was 0.838, higher than the R-SVM and R-AdaBoost classification models. At this time, the corresponding R-logistic classification model formula is as follow: Y_radiomics=9.396-7.464*median ADC-0.584*kurtosis+0.627*skewness+0.576*MRI lesions volume; analysis of clinical indicators shows that the corresponding C-logistic classification model formula is as follows: Y_clinical =-2.608+0.324*PSA-3.045*Fib+4.147*LDL-C, the AUC value of the model training set was 0.860, smaller than the training set R-logistic classification model AUC value of 0.936.ConclusionsRadiomics combined with the machine learning classifier model has strong classification performance in identifying benign and PCa in PI-RADS 4/5 score. Various treatments and outcomes for PCa patients can be applied clinically.

THE ROLE OF MAGNETIC RESONANCE SPECTROSCOPY FOR PROSTATE CANCER DIAGNOSIS

Prostate cancer (PC) is the most common malignancy among men and is the fifth leading cause of cancer-related death in men worldwide. The present study aims to systematically review the ability of magnetic resonance spectroscopy (MRS) to differentiate between benign and malignant prostate lesions. According to the 06-PRISMA guideline, we searched in five English databases, including Scopus, PubMed, Web of Science, EMBASE, and Google Scholar without time limitation for publications related to the role of magnetic resonance spectroscopy for prostate cancer diagnosis. The searched words and terms were: “prostate cancer”, “prostatitis”, “magnetic resonance spectroscopy”, “benign prostate hyperplasia”, “malignant prostate hyperplasia”, “comparison”. Totally 1927 papers were identified by database searching. Out of these papers, 261 papers were discarded because of duplication. Of the remaining 1666 papers, 1604 papers were discarded because of the inadequate information and the ones in which the abstract was submitted in congresses as preceding papers, conferences, and editorials without full text. Out of the remaining 62 papers which were studied for eligibility, 52 papers were removed for a number of reasons including inconsistency between methods with results, incorrect interpretation of the results, poor methodology, etc. Finally, 10 papers were included in this present study. In general, based on the results of the review of articles, MRS has optimal sensitivity, specificity and accuracy in diagnosing prostate cancer and differentiating it from benign prostate hyperplasia in comparison with other diagnostic and pathological methods. Due to the small number of studies related to the sensitivity and specificity of MRS, further checking was not possible to confirm these results. Therefore, further studies in this regard are recommended.

Combining Magnetic Resonance Diffusion-Weighted Imaging with Prostate-Specific Antigen to Differentiate Between Malignant and Benign Prostate Lesions.

BackgroundWe aimed to develop a combined model of quantitative parameters derived from 3 different magnetic resonance imaging (MRI) diffusion models and laboratory data related to prostate-specific antigen (PSA) for differentiating between prostate cancer (PCa) and benign lesions.Material/MethodsEighty-four patients pathologically confirmed as having PCa or benign disease were enrolled. All patients underwent multiparametric MRI before biopsy, added intravoxel incoherent motion (IVIM) imaging, and diffusion kurtosis imaging (DKI). The following data were collected: quantitative parameters of diffusion-weighted imaging (DWI), IVIM, and DKI, preoperative total PSA, free/total PSA ratio, and PSA density (PSAD) values. A combined logistic regression model was established by above MRI quantitative parameters and PSA data to diagnose PCa. The Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) was used to assess the lesions for comparison.ResultsThirty-two patients had PCa and 52 patients had benign lesions. In multivariate logistic regression analysis, only apparent diffusion coefficient (ADC) and PSAD were significant variables (P<0.05) and were thus retained in the model. The area under curve value of the combined model (0.911) was higher than that of ADC, PSAD, and PI-RADS v2 (0.887, 0.861, and 0.859, respectively) in univariate analysis, but without any statistically significant differences. The combined model generated greater clinical benefit than the independent application of ADC, PSAD, and PI-RADS v2.ConclusionsADC and PSAD were the 2 most important metrics for distinguishing PCa from benign lesions. The combined model of ADC and PSAD demonstrated satisfactory discrimination and improved clinical net benefit.