Prostate biopsy: guidelines and evidence

Advanced prostate cancer diagnosed by bone metastasis biopsy immediately after initial negative prostate biopsy: a case report and literature review

Prostate cancer (PCa) is a prevalent male malignancy that originates in the epithelial cells of the prostate. In terms of incidence and mortality of malignant tumors in men, PCa ranks second and fifth globally and first and third among men in Europe and the United States, respectively. These figures have gradually increased in recent years. The primary modalities used to diagnose PCa include prostate-specific antigen (PSA), multiparametric magnetic resonance imaging (mpMRI), and prostate puncture biopsy. Among these techniques, prostate puncture biopsy is considered the gold standard for the diagnosis of PCa; however, this method carries the potential for missed diagnoses. The preoperative evaluation of the patient in this study suggested advanced PCa. However, the initial prostate puncture biopsy was inconsistent with the preoperative diagnosis, and instead of waiting for a repeat puncture of the prostate primary, we performed a biopsy of the rib metastasis, which was later diagnosed as advanced PCa.

Non-Invasive Diagnosis of Prostate Cancer and High-Grade Prostate Cancer Using Multiparametric Ultrasonography and Serological Examination

OBJECTIVES

This study aimed to assess the efficacy of multiparametric ultrasonography (mpUS) combined with serological examination, as a non-invasive method, in detecting prostate cancer (PCa) or high-grade prostate cancer (HGPCa) respectively.

METHODS

A cohort of 245 individuals with clinically suspected PCa were enrolled. All subjects underwent a comprehensive evaluation, including basic data collection, serological testing, mpUS and prostate biopsy. Random Forest (RF) models were developed, and the mean area under the curve (AUC) in 100 cross-validations was used to assess the performance in distinguishing PCa from HGPCa.

RESULTS

mpUS features showed significant differences (p < 0.001) between the PCa and non-PCa groups, as well as between the HGPCa and low-grade prostate cancer (LGPCa) groups including prostate-specific antigen density (PSAD), transrectal real-time elastography (TRTE) and intensity difference (ID). The RF model, based on these features, demonstrated an excellent discriminative ability for PCa with a mean area under the curve (AUC) of 0.896. Additionally, another model incorporating free prostate-specific antigen (FPSA) and color Doppler flow imaging (CDFI) achieved a high accuracy in predicting HGPCa with a mean AUC of 0.830. The nomogram derived from these models exhibited excellent individualized prediction of PCa and HGPCa.

CONCLUSION

The RF models incorporating mpUS and serological variables achieved satisfactory accuracies in predicting PCa and HGPCa.

Computer-aided detection of prostate cancer in early stages using multi-parameter MRI: A promising approach for early diagnosis

BACKGROUND

Transrectal ultrasound-guided prostate biopsy is the gold standard diagnostic test for prostate cancer, but it is an invasive examination of non-targeted puncture and has a high false-negative rate.

OBJECTIVE

In this study, we aimed to develop a computer-assisted prostate cancer diagnosis method based on multiparametric MRI (mpMRI) images.

METHODS

We retrospectively collected 106 patients who underwent radical prostatectomy after diagnosis with prostate biopsy. mpMRI images, including T2 weighted imaging (T2WI), diffusion weighted imaging (DWI), and dynamic-contrast enhanced (DCE), and were accordingly analyzed. We extracted the region of interest (ROI) about the tumor and benign area on the three sequential MRI axial images at the same level. The ROI data of 433 mpMRI images were obtained, of which 202 were benign and 231 were malignant. Of those, 50 benign and 50 malignant images were used for training, and the 333 images were used for verification. Five main feature groups, including histogram, GLCM, GLGCM, wavelet-based multi-fractional Brownian motion features and Minkowski function features, were extracted from the mpMRI images. The selected characteristic parameters were analyzed by MATLAB software, and three analysis methods with higher accuracy were selected.

RESULTS

Through prostate cancer identification based on mpMRI images, we found that the system uses 58 texture features and 3 classification algorithms, including Support Vector Machine (SVM), K-nearest Neighbor (KNN), and Ensemble Learning (EL), performed well. In the T2WI-based classification results, the SVM achieved the optimal accuracy and AUC values of 64.3% and 0.67. In the DCE-based classification results, the SVM achieved the optimal accuracy and AUC values of 72.2% and 0.77. In the DWI-based classification results, the ensemble learning achieved optimal accuracy as well as AUC values of 75.1% and 0.82. In the classification results based on all data combinations, the SVM achieved the optimal accuracy and AUC values of 66.4% and 0.73.

CONCLUSION

The proposed computer-aided diagnosis system provides a good assessment of the diagnosis of the prostate cancer, which may reduce the burden of radiologists and improve the early diagnosis of prostate cancer.

Detection technologies of volatile organic compounds in the breath for cancer diagnoses

Although there are new approaches in both cancer treatment and diagnosis, overall mortality is a major concern. New technologies have attempted to look at breath volatile organic compounds (VOCs) detection to diagnose cancer. Gas Chromatography and Mass Spectrometry (GC - MS) have remained the gold standard of VOC analysis for decades, but it has limitations in differentiating VOCs between cancer subtypes. To increase efficacy and accuracy, new methods to analyze these breath VOCs have been introduced, such as Solid Phase Microextraction/Gas Chromatography-Mass Spectrometry (SPME/GC-MS), Selected Ion Flow Tube - Mass Spectrometry (SIFT-MS), Proton Transfer Reaction - Mass Spectrometry (PRT-MS), Ion Mobility Spectrometry (IMS), and Colorimetric Sensors. This article highlights new technologies that have been studied and applied in the detection and quantification of breath VOCs for possible cancer diagnoses.

Two-year follow-up of a thyroid cartilage metastasis from prostate cancer: A case report

Few case reports describe metastatic prostate cancer to the thyroid cartilage. While earlier reports identified the metastatic lesions upon developing symptoms, more recent ones have detected them via prostate-specific membrane antigen positron emission tomography (PSMA PET). Herein, we report the case of a patient with metastatic castrate-resistant prostate cancer and a PSMA PET-detected lesion in the thyroid cartilage. Over the course of 2 years, he received multiple lines of chemotherapy and hormonal therapy, and his overall disease status fluctuated - some nodal and bony metastases resolved while others appeared anew. His thyroid cartilage lesion, however, slowly progressed in a consistent fashion with increasing uptake on successive PSMA PET images. Apart from mild dysphonia, the patient remained to be asymptomatic from this lesion, and no local therapies were used. To our knowledge, this is the first close follow-up of prostate cancer metastatic to the thyroid cartilage, shedding light on the course of such lesions and helping answer management-related questions, which are particularly relevant as more occult metastases are discovered in the PSMA PET era.

Novel device for dividing core needle biopsy specimens to provide paired mirror image-like tissues for genetic and pathological tests

In a world that seeks precision medicine, genetic testing is gaining importance in clinical decision making. We previously reported the utility of a novel tool for longitudinally dividing core needle biopsy (CNB) tissues into two filamentous tissues that can provide paired mirror image-like tissues (mirror-tissues) that spatially match each other. In this study, we investigated its application in gene panel testing in patients who underwent prostate CNB. Four hundred and forty-three biopsy cores were obtained from 40 patients. Of them, 361 biopsy cores (81.5%) were judged by a physician to be appropriate for dividing into two pieces using the new device, of which a histopathological diagnosis was successfully reached in 358 biopsy cores (99.2%). Among them, the quality and quantity of nucleic acid in 16 appropriately divided cores were assessed and found to be sufficient for gene panel testing, and histopathological diagnosis was successfully obtained from the remaining divided cores. The novel device for longitudinally-dividing CNB tissue provided mirror image-like paired-tissues for gene panel and pathology testing. The device might be a promising tool for obtaining genetic and molecular biological information, in addition to histopathological diagnosis, helping to advance personalized medicine.

Systematic Review and Meta-Analysis of Free-Hand and Fixed-Arm Spatial Tracking Methodologies in Software-Guided MRI-TRUS Fusion Prostate Biopsy Platforms

OBJECTIVE

To evaluate the cancer detection rate (CDR) between the 2 dominant spatial tracking methodologies in software-guided MRI-transrectal ultrasound fusion prostate biopsy (SGF-Bx) platforms: fixed-arm and free-hand.

METHODS

We conducted a systematic review and meta-analysis on published primary analyses of prospective trials and cohort studies that enrolled biopsy-naïve patients for SFG-Bx. Inclusion criteria included the use of the Prostate Imaging Reporting & Data System (PI-RADS) v2.0 or later and the targeting of lesions graded as PI-RADS 3 or higher. Random effects models were used to assess the overall prostate cancer (PCa) CDR and the clinically significant prostate cancer (csPCa) CDR for both platforms. csPCa was standardized to a definition of Gleason Grade Group 2 or higher when possible. Subgroup analysis was performed by stratifying studies into the average number of cores taken per lesion.

RESULTS

The PCa CDR was 0.674 for free-hand systems and 0.681 for fixed-arm systems. The csPCa CDR was 0.492 for free-hand systems and 0.500 for fixed-hand systems. There was no significant difference between free-hand and fixed-arm cancer detection rates for both overall PCa (P = .88) and csPCa (P = .90). Subgroup analyses revealed significant PCa CDR and csPCa CDR differences (P < .001) between free-hand and fixed-arm platforms only when 2 cores per lesion were taken, in favor of fixed-arm platforms.

CONCLUSIONS

Fixed-arm platforms performed similarly in cancer detection to free-hand platforms but show a minor benefit on fewer samples. While tracking methodology differences appear subtle, further investigation into the clinical impact of platform-specific features are warranted.

A Preclinical Study of an 125I-Labeled PSMA Ligand for Prostate-Cancer Puncture

Purpose: Prostate cancer (PCa) is characterized by high expression of prostate-specific 1membrane antigen (PSMA), a type II transmembrane protein. Prostate-specific membrane antigen positron emission tomography (PSMA PET) has high sensitivity and specificity and can therefore be potentially used to detect PCa. Exploiting the advantages of PSMA PET imaging, in this study, we aim to develop a novel radiopharmaceutical to facilitate biopsy punching of PCa. Methods: We synthesized a high-affinity radiopharmaceutical of PSMA (¹²⁵I-PSMA-7). We evaluated the properties of ¹²⁵I-PSMA-7, including the purity, stability, affinity, partition coefficient, and toxicity. (PSMA+) 22Rv1 and (PSMA−) PC3 cell lines were used to evaluate ¹²⁵I-PSMA-7 in vitro. BALB/c nude mice bearing 22Rv1 and PC3 xenografts were used for biodistribution and imaging. The uptake of the main organs was evaluated in vivo using single photon emission computed tomography (SPECT). Results: ¹²⁵I-PSMA-7 had a purity of 99.6% and remained stable for seven days and was therefore always safe to use. ¹²⁵I-PSMA-7 had a Ki of 4.037 × 10⁻¹¹ and a partition coefficient of −1.80. The results of in vitro cellular experiments showed a high uptake by 22Rv1 cells (ranging from 2.88 ± 0.14 IA%/10⁶ at 5 min to 61.98 ± 3.43 IA%/10⁶ at 24 h, where the internalization was 46.1% at 1 h and 88.06% at 24 h). However, the uptake of PC3 cells was very low (ranging from 0.34 ± 0.08 IA%/10⁶ at 5 min to 1.60 ± 0.15 IA%/10⁶ at 24 h). The tumors’ uptake of ¹²⁵I-PSMA-7 ranged from 9.02 ± 0.30 ID%/g at 1 h to 4.11 ± 1.04 ID%/g at 7 d and the tumor/muscle ratios and tumor/blood ratios increased over time. In addition, we used γ-counter to measure cpm per milligram of tumor and muscle on days 4 and 7. The background on day 4 is 42 cpm and the tumor is 1739 cpm/mg and the muscle is 45 cpm/mg, and the background on day 7 is 74 cpm and the tumor is 1404cpm/mg and the muscle is 32 cpm/mg. At 1 h post-injection, the high uptake of ¹²⁵I-PSMA-7 resulted in clear delineation of 22Rv1-derived tumors upon imaging. By comparison, 22Rv1-blocking mice took up less ¹²⁵I-PSMA-7. Conclusions: These results show that ¹²⁵I-PSMA-7 is a promising radiotracer that could be used to puncture the prostate. ¹²⁵I-PSMA-7 could be applied to targeted biopsy, reducing the need for saturated biopsy.

'Is it painful'? A qualitative study on experiences of patients before prostate needle biopsy

OBJECTIVE

To assess the experiences and emotional reactions of men prior to receiving a prostate needle biopsy (PNB).

DESIGN

This was a qualitative study involving (1) material research and filter, (2) interviewer training, (3) cognitive semistructured interviews with open-ended questions, (4) data analysis, including translation and back translation and (5) group discussions to determine common themes in the data. All interviews were digitally audio-recorded. The thematic analysis was conducted by repeatedly reading the data manuscript and engaging in group discussion.

SETTING AND PARTICIPANTS

A total of 30 participants with suspected prostate cancer (PC) who were scheduled to receive a PNB were interviewed. Eligible participants were Chinese native men aged 51-77 years, and the study was conducted in China between September and December 2020. All participants were informed about the purpose of the study and provided written informed consent.

RESULTS

Four main themes were identified based on the interview content: (1) fear (of pain, adverse effects and bad results), (2) impact of lower urinary tract symptoms (on emotional impact, work and sleeping), (3) inner struggles (relating to hesitation, regret and embarrassment) and (4) lifestyle change (including diet, exercise and receipt of traditional Chinese medicine).

CONCLUSIONS

This patient cohort had a low level of knowledge about PC and PNB. Providing additional education about these topics would help to reduce patient fear and anxiety and improve experiences of the procedure.

Time point-independent tumor positivity of 68Ga-PSMA-PET/CT pre- and post-biopsy in high-risk prostate cancer

OBJECTIVE

Prostate-specific membrane antigen (PSMA)-PET/CT imaging has gained increasing clinical importance for the detection and staging of high-risk primary prostate cancer (PCa). However, it is unclear whether the routine practice of prostate biopsy obscures the image finding of PSMA-PET/CT. This study aimed to compare the tumor positivity rate of PSMA-PET/CT performed pre- (PSMA-PET/CT_pre) and post-biopsy (PSMA-PET/CT_post) in high-risk PCa patients.

PATIENTS AND METHODS

We matched 58 PSMA-PET/CT_post with 58 PSMA-PET/CT_pre studies for primary detection of high-risk PCa according to clinical characteristics. Three subgroups of PSMA-PET/CT_post were defined by the intervals after biopsy (≤ 1 week, 1 ~ 2 weeks, and 2 ~ 5 weeks). Tumor positivity rates were determined, and SUVmax of primary tumors were compared separately for the two main groups and the related subgroups. Malignant prostate tissues from 20 of these patients were examined by immunohistochemical analysis of PSMA. In addition, the values of PSMA-PET/CT_pre and PSMA-PET/CT_post in assessing seminal vesicle invasion (SVI) were evaluated in patients who underwent radical prostatectomy.

RESULTS

All the primary tumors were positive on PSMA-PET/CT_post and PSMA-PET/CT_pre imaging, resulting in a patient-based positivity rates of 100% (58/58) in both groups. All examined IHC results (20/20) confirmed the high-level expression of PSMA. SUVmax of primary tumors did not differ between the two main groups (16.1, IQR 9.8-26.6 vs. 16.5, IQR 11.0-26.7, p > 0.05). Subgroup analysis of PSMA-PET/CT_post (≤ 1 week, 1 ~ 2 weeks, and 2 ~ 5 weeks) also showed no significant difference in tumor SUVmax (15.8, IQR 9.5-22.2; 17.8, IQR 9.8-29.2; and 15.4, IQR 10.1-30.3. p > 0.05). PSMA-PET/CT_post and PSMA-PET/CT_pre exhibited similar value in SVI detection as well.

CONCLUSIONS

The tumor positivity rate was consistently high for PSMA-PET/CT pre- and post-biopsy. A prior biopsy does not seem to affect the tumor positivity rate of PSMA-PET/CT in high-risk PCa.

Construction and Validation of a Clinical Predictive Nomogram for Improving the Cancer Detection of Prostate Naive Biopsy Based on Chinese Multicenter Clinical Data

Objectives

Prostate biopsy is a common approach for the diagnosis of prostate cancer (PCa) in patients with suspicious PCa. In order to increase the detection rate of prostate naive biopsy, we constructed two effective nomograms for predicting the diagnosis of PCa and clinically significant PCa (csPCa) prior to biopsy.

Materials and Methods

The data of 1,428 patients who underwent prostate biopsy in three Chinese medical centers from January 2018 to June 2021 were used to conduct this retrospective study. The KD cohort, which consisted of 701 patients, was used for model construction and internal validation; the DF cohort, which consisted of 385 patients, and the ZD cohort, which consisted of 342 patients, were used for external validation. Independent predictors were selected by univariate and multivariate binary logistic regression analysis and adopted for establishing the predictive nomogram. The apparent performance of the model was evaluated via internal validation and geographically external validation. For assessing the clinical utility of our model, decision curve analysis was also performed.

Results

The results of univariate and multivariate logistic regression analysis showed prostate-specific antigen density (PSAD) (P<0.001, OR:2.102, 95%CI:1.687-2.620) and prostate imaging-reporting and data system (PI-RADS) grade (P<0.001, OR:4.528, 95%CI:2.752-7.453) were independent predictors of PCa before biopsy. Therefore, a nomogram composed of PSAD and PI-RADS grade was constructed. Internal validation in the developed cohort showed that the nomogram had good discrimination (AUC=0.804), and the calibration curve indicated that the predicted incidence was consistent with the observed incidence of PCa; the brier score was 0.172. External validation was performed in the DF and ZD cohorts. The AUC values were 0.884 and 0.882, in the DF and ZD cohorts, respectively. Calibration curves elucidated greatly predicted the accuracy of PCa in the two validation cohorts; the brier scores were 0.129 in the DF cohort and 0.131 in the ZD cohort. Decision curve analysis showed that our model can add net benefits for patients. A separated predicted model for csPCa was also established and validated. The apparent performance of our nomogram for PCa was also assessed in three different PSA groups, and the results were as good as we expected.

Conclusions

In this study, we put forward two simple and convenient clinical predictive models comprised of PSAD and PI-RADS grade with excellent reproducibility and generalizability. They provide a novel calculator for the prediction of the diagnosis of an individual patient with suspicious PCa.

Metabolic Phenotyping in Prostate Cancer Using Multi-Omics Approaches

Prostate cancer (PCa), one of the most frequently diagnosed cancers among men worldwide, is characterized by a diverse biological heterogeneity. It is well known that PCa cells rewire their cellular metabolism to meet the higher demands required for survival, proliferation, and invasion. In this context, a deeper understanding of metabolic reprogramming, an emerging hallmark of cancer, could provide novel opportunities for cancer diagnosis, prognosis, and treatment. In this setting, multi-omics data integration approaches, including genomics, epigenomics, transcriptomics, proteomics, lipidomics, and metabolomics, could offer unprecedented opportunities for uncovering the molecular changes underlying metabolic rewiring in complex diseases, such as PCa. Recent studies, focused on the integrated analysis of multi-omics data derived from PCa patients, have in fact revealed new insights into specific metabolic reprogramming events and vulnerabilities that have the potential to better guide therapy and improve outcomes for patients. This review aims to provide an up-to-date summary of multi-omics studies focused on the characterization of the metabolomic phenotype of PCa, as well as an in-depth analysis of the correlation between changes identified in the multi-omics studies and the metabolic profile of PCa tumors.

Comprehensive Metabolomics and Lipidomics Profiling of Prostate Cancer Tissue Reveals Metabolic Dysregulations Associated with Disease Development

Prostate cancer (PCa) is a global health problem that affects millions of men every year. In the past decade, metabolomics and related subareas, such as lipidomics, have demonstrated an enormous potential to identify novel mechanisms underlying PCa development and progression, providing a good basis for the development of new and more effective therapies and diagnostics. In this study, a multiplatform metabolomics and lipidomics approach, combining untargeted mass spectrometry (MS) and nuclear magnetic resonance (NMR)-based techniques, was applied to PCa tissues to investigate dysregulations associated with PCa development, in a cohort of 40 patients submitted to radical prostatectomy for PCa. Results revealed significant alterations in the levels of 26 metabolites and 21 phospholipid species in PCa tissue compared with adjacent nonmalignant tissue, suggesting dysregulation in 13 metabolic pathways associated with PCa development. The most affected metabolic pathways were amino acid metabolism, nicotinate and nicotinamide metabolism, purine metabolism, and glycerophospholipid metabolism. A clear interconnection between metabolites and phospholipid species participating in these pathways was observed through correlation analysis. Overall, these dysregulations may reflect the reprogramming of metabolic responses to produce high levels of cellular building blocks required for rapid PCa cell proliferation.

Transperineal Parallel Biopsy of the Prostate: A New Approach of Tissue Sampling for Precision Medicine

Purpose

Through an observational study to present a new approach for obtaining high-quality samples for the targeted therapy of prostate cancer.

Patients and Methods

Parallel biopsy, which was defined as collecting the tissue from the same site by two biopsies, was performed on patients with elevated PSA. Each tissue was stained by ink to identify the pathological characteristics, including Gleason score and tumor tissue ratio. Kendall tau-b test and intraclass correlation coefficient test were used to compare the consistency between each paired sample. Then, based on the pathology of the biopsies, high-quality tissues would be selected for sequencing, and PyClone model was used to track the clonal evolution.

Results

In total, 252 pairs of biopsies were collected. The consistency of Gleason score between each paired biopsy is 0.777 (p<0.01), and the consistency of tumor tissue ratio is 0.853 (p<0.01). With the application of parallel biopsy, on average five nonsynonymous mutations could be identified in patients with castration-resistant prostate cancer. Six out of eight had at least one biology-relevant alteration in patients, guiding further treatment. Meanwhile, clonal evolution was constructed to investigate the progress of tumor.

Conclusion

Parallel biopsy is a reliable approach to collect high-quality tissue and shows potential application in precision medicine.

Advances and Perspectives in Prostate Cancer Biomarker Discovery in the Last 5 Years through Tissue and Urine Metabolomics

Prostate cancer (PCa) is the second most diagnosed cancer in men worldwide. For its screening, serum prostate specific antigen (PSA) test has been largely performed over the past decade, despite its lack of accuracy and inability to distinguish indolent from aggressive disease. Metabolomics has been widely applied in cancer biomarker discovery due to the well-known metabolic reprogramming characteristic of cancer cells. Most of the metabolomic studies have reported alterations in urine of PCa patients due its noninvasive collection, but the analysis of prostate tissue metabolome is an ideal approach to disclose specific modifications in PCa development. This review aims to summarize and discuss the most recent findings from tissue and urine metabolomic studies applied to PCa biomarker discovery. Eighteen metabolites were found consistently altered in PCa tissue among different studies, including alanine, arginine, uracil, glutamate, fumarate, and citrate. Urine metabolomic studies also showed consistency in the dysregulation of 15 metabolites and, interestingly, alterations in the levels of valine, taurine, leucine and citrate were found in common between urine and tissue studies. These findings unveil that the impact of PCa development in human metabolome may offer a promising strategy to find novel biomarkers for PCa diagnosis.