Beyond transrectal ultrasound-guided prostate biopsies: available techniques and approaches

Development and validation of a clinical-radiomics model for prediction of prostate cancer: a multicenter study

PURPOSE

To develop an early diagnosis model of prostate cancer based on clinical-radiomics to improve the accuracy of imaging diagnosis of prostate cancer.

METHODS

The multicenter study enrolled a total of 449 patients with prostate cancer from December 2017 to January 2022. We retrospectively collected information from 342 patients who underwent prostate biopsy at Minhang Hospital. We extracted T2WI images through 3D-Slice, and used mask tools to mark the prostate area manually. The radiomics features were extracted by Python using the "Pyradiomics" module. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for data dimensionality reduction and feature selection, and the radiomics score was calculated according to the correlation coefficients. Multivariate logistic regression analysis was used to develop predictive models. We incorporated the radiomics score, PI-RADS, and clinical features, and this was presented as a nomogram. The model was validated using a cohort of 107 patients from the Xuhui Hospital.

RESULTS

In total, 110 effective radiomics features were extracted. Finally, 9 features were significantly associated with the diagnosis of prostate cancer, from which we calculated the radiomics score. The predictors contained in the individualized prediction nomogram included age, fPSA/tPSA, PI-RADS, and radiomics score. The clinical-radiomics model showed good discrimination in the validation cohort (C-index = 0.88).

CONCLUSION

This study presents a clinical-radiomics model that incorporates age, fPSA/PSA, PI-RADS, and radiomics score, which can be conveniently used to facilitate individualized prediction of prostate cancer before prostate biopsy.

Current Approach to Complications and Difficulties during Transrectal Ultrasound-Guided Prostate Biopsies

Prostate cancer is one of the most common male malignancies worldwide. It affects middle-aged men (45-60 years) and is the leading cause of cancer-related mortality in Western countries. The TRUS (trans rectal ultrasound)-guided prostate biopsy has been a standard procedure in prostate cancer detection for more than thirty years, and it is recommended in male patients with an abnormal PSA (prostate-specific antigens) or abnormalities found during digital rectal examinations. During this procedure, urologists might encounter difficulties which may cause subsequent complications. This manuscript aims to present both the complications and the technical difficulties that may occur during TRUS-guided prostate biopsy, along with resolutions and solutions found in the specialized literature. The conclusions of this manuscript will note that the TRUS-guided prostate biopsy remains a solid, cost-efficient, and safe procedure with which to diagnose prostate cancer. The complications are usually self-limiting and do not require additional medical assistance. The difficulties posed by the procedure can be safely overcome if there are no other available alternatives. Open communication with the patients improves both pre- and post-procedure compliance.

Schema and cancer detection rates for transperineal prostate biopsy templates: a review

Prostate cancer (PCa) is the most common noncutaneous malignancy in men and is the second leading cause of cancer mortality in men in the United States. Current practice requires histopathological confirmation of cancer achieved through biopsy for diagnosis. The transrectal approach for prostate biopsy has been the standard for several decades. However, the risks and limitations of transrectal biopsies have led to a recent resurgence of transperineal prostatic biopsies. Recent studies have demonstrated the transperineal approach for prostate biopsies to be effective, associated with minimal complications and superior in several aspects to traditional transrectal biopsies. While sextant and extended sextant templates are widely accepted templates for transrectal biopsy, there are a diverse set of transperineal biopsy templates available for use, without consensus on the optimal sampling strategy. We aim to critically appraise the salient features of established transperineal biopsy templates.

Biomechanical modelling of the pelvic system: improving the accuracy of the location of neoplasms in MRI-TRUS fusion prostate biopsy

Background

An accurate knowledge of the relocation of prostate neoplasms during biopsy is of great importance to reduce the number of false negative results. Prostate neoplasms are visible in magnetic resonance images (MRI) but it is difficult for the practitioner to locate them at the time of performing a transrectal ultrasound (TRUS) guided biopsy. In this study, we present a new methodology, based on simulation, that predicts both prostate deformation and lesion migration during the biopsy.

Methods

A three-dimensional (3-D) anatomy model of the pelvic region, based on medical images, is constructed. A finite element (FE) numerical simulation of the organs motion and deformation as a result of the pressure exerted by the TRUS probe is carried out using the Code-Aster open-source computer software. Initial positions of potential prostate lesions prior to biopsy are taken into consideration and the final location of each lesion is targeted in the FE simulation output.

Results

Our 3-D FE simulations show that the effect of the pressure exerted by the TRUS probe is twofold as the prostate experiences both a motion and a deformation of its original shape. We targeted the relocation of five small prostate lesions when the TRUS probe exerts a force of 30 N on the rectum inner wall. The distance travelled by these lesions ranged between 5.6 and 13.9 mm.

Conclusions

Our new methodology can help to predict the location of neoplasms during a prostate biopsy but further studies are needed to validate our results. Moreover, the new methodology is completely developed on open-source software, which means that its implementation would be affordable to all healthcare providers.

Extracellular Vesicle Proteome in Prostate Cancer: A Comparative Analysis of Mass Spectrometry Studies

Molecular diagnostics based on discovery research holds the promise of improving screening methods for prostate cancer (PCa). Furthermore, the congregated information prompts the question whether the urinary extracellular vesicles (uEV) proteome has been thoroughly explored, especially at the proteome level. In fact, most extracellular vesicles (EV) based biomarker studies have mainly targeted plasma or serum. Therefore, in this study, we aim to inquire about possible strategies for urinary biomarker discovery particularly focused on the proteome of urine EVs. Proteomics data deposited in the PRIDE archive were reanalyzed to target identifications of potential PCa markers. Network analysis of the markers proposed by different prostate cancer studies revealed moderate overlap. The recent throughput improvements in mass spectrometry together with the network analysis performed in this study, suggest that a larger standardized cohort may provide potential biomarkers that are able to fully characterize the heterogeneity of PCa. According to our analysis PCa studies based on urinary EV proteome presents higher protein coverage compared to plasma, plasma EV, and voided urine proteome. This together with a direct interaction of the prostate gland and urethra makes uEVs an attractive option for protein biomarker studies. In addition, urinary proteome based PCa studies must also evaluate samples from bladder and renal cancers to assess specificity for PCa.

Folate-Receptor Positive Circulating Tumor Cell Is a Potential Diagnostic Marker of Prostate Cancer

Folate-receptor positive circulating tumor cells (FR+CTCs) shows an important role in the diagnosis and dynamic monitoring for many solid tumors; however, the application of FR+CTCs in prostate cancer remains unclear. We explored the potential application of FR+CTCs in this retrospective study. The levels of FR+CTCs were detected in 30 prostate cancer patients and 7 bladder cancer patients in Peking University Cancer Hospital from August 2017 to August 2021. Clinical and pathology data were collected. One-way ANOVA was used to compare the difference in FR+CTCs levels in patients with prostate cancer, bladder cancer, and benign disease. The area under the receiver operating curve (AUROC) was used to compare the accuracy of FR+CTCs and tPSA in the diagnosis of prostate cancer. We found that levels of FR+CTCs were significantly higher in cancer patients (both prostate and bladder cancer) than in patients with benign urinary disease (p < 0.001). Besides, FR+CTCs level was consistently high in the prostate cancer patients with different tPSA levels (p < 0.001), and it was significantly higher in the patients with f/tPSA levels <0.16 than in those patients with f/tPSA levels >0.16 (12.20 ± 1.31 vs. 8.73 ± 0.92 FU/3 ml, p = 0.043). The diagnosis efficiency of FR+CTCs is better than the tPSA in prostate cancer patients with tPSA <10 ng/ml (0.871 vs. 0.857). In the prostate cancer patients with tPSA <10 ng/ml and f/tPSA <0.16, a combination of FR+CTCs and tPSA (AUROC, 0.934) further increased the diagnosis efficiency of each of these biomarkers alone (FR+CTCs, 0.912; tPSA, 0.857). Therefore, FR+CTCs could serve as an early diagnosis marker in the prostate cancer patients with uncertain tPSA levels.

Comparison of cross-sectional imaging techniques for the detection of prostate cancer lymph node metastasis: a critical review

Conventional staging for prostate cancer (PCa) is performed for men diagnosed with unfavorable-intermediate or higher risk disease. Computed tomography (CT) of the abdomen and pelvis and whole body bone scan remains the standard of care for the detection of visceral, nodal, and bone metastasis. The implementation of the 2012 United States Preventive Services Task Force recommendation against routine prostate specific antigen (PSA) screening resulted in a rise of metastatic PCa at the time of diagnosis, emphasizing the importance of effective imaging modalities for evaluating metastatic disease. CT plays a major role in clinical staging at the time of PCa diagnosis, but multi-parametric magnetic resonance imaging (MRI) is now integrated into many prostate biopsy protocols for the detection of primary PCa, and may be a surrogate for CT for nodal staging. Current guidelines incorporate both CT and MRI as appropriate cross-sectional imaging modalities for the identification of nodal metastasis in indicated patients. There is an ongoing debate about the utility of traditional cross-sectional imaging modalities as well as advanced imaging modalities in detection of both organ-confined PCa detection and nodal involvement.

Deep Learning for Real-time, Automatic, and Scanner-adapted Prostate (Zone) Segmentation of Transrectal Ultrasound, for Example, Magnetic Resonance Imaging-transrectal Ultrasound Fusion Prostate Biopsy

BACKGROUND

Although recent advances in multiparametric magnetic resonance imaging (MRI) led to an increase in MRI-transrectal ultrasound (TRUS) fusion prostate biopsies, these are time consuming, laborious, and costly. Introduction of deep-learning approach would improve prostate segmentation.

OBJECTIVE

To exploit deep learning to perform automatic, real-time prostate (zone) segmentation on TRUS images from different scanners.

DESIGN, SETTING, AND PARTICIPANTS

Three datasets with TRUS images were collected at different institutions, using an iU22 (Philips Healthcare, Bothell, WA, USA), a Pro Focus 2202a (BK Medical), and an Aixplorer (SuperSonic Imagine, Aix-en-Provence, France) ultrasound scanner. The datasets contained 436 images from 181 men.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Manual delineations from an expert panel were used as ground truth. The (zonal) segmentation performance was evaluated in terms of the pixel-wise accuracy, Jaccard index, and Hausdorff distance.

RESULTS AND LIMITATIONS

The developed deep-learning approach was demonstrated to significantly improve prostate segmentation compared with a conventional automated technique, reaching median accuracy of 98% (95% confidence interval 95-99%), a Jaccard index of 0.93 (0.80-0.96), and a Hausdorff distance of 3.0 (1.3-8.7) mm. Zonal segmentation yielded pixel-wise accuracy of 97% (95-99%) and 98% (96-99%) for the peripheral and transition zones, respectively. Supervised domain adaptation resulted in retainment of high performance when applied to images from different ultrasound scanners (p > 0.05). Moreover, the algorithm's assessment of its own segmentation performance showed a strong correlation with the actual segmentation performance (Pearson's correlation 0.72, p < 0.001), indicating that possible incorrect segmentations can be identified swiftly.

CONCLUSIONS

Fusion-guided prostate biopsies, targeting suspicious lesions on MRI using TRUS are increasingly performed. The requirement for (semi)manual prostate delineation places a substantial burden on clinicians. Deep learning provides a means for fast and accurate (zonal) prostate segmentation of TRUS images that translates to different scanners.

PATIENT SUMMARY

Artificial intelligence for automatic delineation of the prostate on ultrasound was shown to be reliable and applicable to different scanners. This method can, for example, be applied to speed up, and possibly improve, guided prostate biopsies using magnetic resonance imaging-transrectal ultrasound fusion.