Multiparametric ultrasound versus multiparametric MRI to diagnose prostate cancer (CADMUS): a prospective, multicentre, paired-cohort, confirmatory study

Predicting pathological tumor volume in prostate cancer lesions: A head-to-head comparison of micro-ultrasound vs. MRI

BACKGROUND AND OBJECTIVE

Our objective was to evaluate the agreement between micro-ultrasound, MRI and pathological tumor and prostate volume.

METHODS

Retrospective analysis of consecutive prostate cancer patients with MRI and micro-ultrasound diagnostic assessment who subsequently underwent radical prostatectomy. Tumor and prostate volume on micro-ultrasound and MRI imaging calculated by a dedicated software were compared to those of the prostatectomy specimen. Clinical, radiological, and pathological predictors of pathological tumor size were assessed.

RESULTS

65 men with a total of 104 lesions in the final pathology were included. Median micro-ultrasound tumor size was 1.05 ml (IQR 0.35-2.65). On MRI T2WI, DWI and ADC sequences median tumor volume was 0.73 ml (IQR 0.34-1.94), 0.94 ml (IQR 0.38-2.09) and 0.86 ml (IQR 0.42-1.58), respectively. The pathological median tumor size was 1.2 ml (IQR 0.2-3.9). On average, micro-ultrasound underestimated pathological tumor volume by 0.15 ml (P < 0.01) while DWI, the most precise MRI sequence underestimated tumor size by 0.26 ml (P < 0.01). The MRI and micro-ultrasound underestimated the pathological prostate volume by 6 ml (P < 0.01) and 3 ml (P = 0.47), respectively.

CONCLUSIONS

Both micro-ultrasound and MRI tend to slightly underestimate pathological tumor and prostate volume. Our study shows that both micro-ultrasound and MRI can be useful in the surgical planning although the underestimation of actual tumor size should be considered.

Biopsy strategies in the era of mpMRI: a comprehensive review

BACKGROUND

Since its initial description the prostate biopsy technique for detection of prostate cancer (PCA) has constantly evolved. Multiparametric magnetic resonance imaging (mpMRI) has been proven to have a sensitivity exceeding 90% to detect the index lesion. This narrative review discusses the evidence around several biopsy strategies, especially in the context of patients that might be eligible for focal therapy.

METHOD

A non-systematic literature research was performed on February 15th 2024 using the Medical Literature Analysis and Retrieval System Online (Medline), Web of Science and Google Scholar.

RESULTS

The transrectal (TR) route is associated with an increased postoperative sepsis rate, even with adequate antibiotic prophylaxis. The transperineal (TP) route is now recommended by international guidelines, firstly for its decreased rate of urosepsis. Recent evidence shows a non-inferiority of TP compared to TR route, and even a higher detection rate of clinically significant PCA (csPCA) in the anterior and apical region, that are usually difficult to target using the TR route. Several targeting techniques (cognitive, software-fusion or in-bore) enhance our ability to provide an accurate risk assessment of prostate cancer aggressiveness and burden, while reducing the number of cores and reducing the number of clinically insignificant prostate cancer (ciPCA). While MRI-TB have proven their role, the role of systematic biopsies (SB) is still important because it detects 5-16% of csPCA that would have been missed by MRI-TB alone. The strategies of SB depend mainly on the route used (TR vs. TP) and the number of cores to be collected (10-12 cores vs. saturation biopsies vs. trans-perineal template mapping-biopsies or Ginsburg Protocol vs. regional biopsies).

CONCLUSION

Several biopsy strategies have been described and should be known when assessing patients for focal therapy. Because MRI systematically under evaluates the lesion size, systematic biopsies, and especially perilesional biopsies, can help to increase sensitivity at the cost of an increased number of cores.

Developments in Ultrasound-Based Imaging for Prostate Cancer Detection

BACKGROUND

Prostate cancer is a significant health issue worldwide, but methods to screen for and diagnose this disease have significant inherent limitations. Some efforts to address these limitations have involved the use of ultrasound-based imaging methods.

METHODS

This narrative review paper focuses on recent developments in the use of medical imaging, with a focus on ultrasound and related methods, to improve the diagnosis of prostate cancer. These methods include: elastography, contrast-enhanced ultrasound, targeted contrast agents, quantitative ultrasound, multiparametric ultrasound, micro-ultrasound, and photoacoustic imaging.

RESULTS

This paper provides an update on clinically relevant imaging technologies which are in the technical and preclinical literature.

CONCLUSION

Novel methods and their performance are highlighted, including how they address limitations in current clinical care.

Prostate Specific Antigen Density and Clinically-Significant Prostate Cancer: The Influence of Prostatic Volume

BACKGROUND

Prostate specific antigen density (PSAd) is one of the strongest predictors of clinically-significant prostate cancer (csPCa) in risk calculators. There is little evidence on the effect of prostate volume on the diagnostic performance of PSAd. Our aim was to define the diagnostic accuracy of PSAd for predicting csPCa across prostate volumes.

METHODS

548 patients who underwent magnetic resonance imaging (MRI) and biopsy were included in this retrospective study. Patients were grouped by prostate volume; small (≤ 30 mL), medium (> 30 to < 50 mL), large (≥ 50 mL). Sensitivity and specificity of PSAd were assessed at thresholds of ≥ 0.10, ≥ 0.15, and ≥ 0.20 ng/mL/mL for two definitions of csPCa.

RESULTS

At all PSAd thresholds and for both definitions of csPCa, there was a statistically significant and clinically-relevant difference in diagnostic performance across prostate volume groups. Sensitivity was highest in small glands, lowest in large glands; the opposite being true for specificity. Using a PSAd threshold of ≥ 0.15 ng/mL/mL, sensitivity for ISUP grade ≥ 2 PCa was 83.1%, 63.6%, and 33.3% for small, medium and large prostates (p ≤ 0.001) with specificities of 48.5%, 67.5% and 79.3%, respectively (p = 0.005).

CONCLUSIONS

Diagnostic performance of PSAd varied significantly by prostate volume, and by applying a single PSAd threshold across all prostate volumes risks missing csPCa in men with larger glands, whilst performing unnecessary biopsies in those with smaller glands. Defining PSAd thresholds according to prostate volume categories can therefore improve its use as a risk predictor for csPCa.

Treatment cycles per unit prostate volume (CPV) for transurethral water vapor therapy (Rezūm) in catheter-dependent patients

BACKGROUND

For transurethral water vapor therapy (Rezūm), the number of water vapor treatment cycles has classically been determined by the prostate length, or the fields of vision (FOV). Apart from treating lower urinary tract symptoms, there is emerging evidence on Rezūm for refractory retention. We aim to investigate the optimal number of treatment cycles for Rezūm in catheter-dependent refractory retention.

METHODS

From 02/2021 to 09/2023, 168 consecutive catheter-dependent patients undergoing Rezūm at three centres were included in this prospective registry. Treatment Cycles Per Unit Prostate Volume (CPV) was calculated by dividing the number of treatment cycles by the prostate size. After propensity score matching of age and prostate size, 144 patients were analyzed in the CPV ≤ 0.15 and CPV > 0.15 groups in 1:1 ratio. The primary outcome was the International Prostate Symptom Score (IPSS) at 1-year follow-up. Secondary outcomes included catheter removal time, 30-day readmission rates, prostate-specific antigen (PSA) reduction. Logistic regression model and linear mixed model were used.

RESULTS

The CPV > 0.15 group demonstrated significantly better IPSS at 1-year follow-up (adjusted mean difference -2.8 points, p = 0.040), and lower 30-day readmission rates (4.2% vs 16.7%, OR 0.22, p = 0.029). Greater PSA reduction was observed in the higher CPV group at 3 months (adjusted mean difference of log-transformed PSA: -0.4 ng/ml, p = 0.022). Median catheter removal times were 14 days (interquartile range 9-29 days) and 15 days (interquartile range 12-40 days) for lower and higher CPV groups respectively (p = 0.059). Six-week IPSS and IPSS-QoL (quality of life score) were similar (p = 0.359 and p = 0.464 respectively).

CONCLUSION

Higher CPV (>0.15) in Rezūm demonstrated superior 1-year IPSS, lower 30-day readmission rates in our matched cohort. A more aggressive treatment approach, contrasting to the standard FOV-based approach, may benefit catheter-dependent patients.

A multi-center study: development and validation of a BpMRI focused model in transition zone PI-RADS 3 and 4 lesions to detect clinically significant prostate cancer

OBJECTIVE

To develop and validate a biparametric magnetic resonance imaging(BpMRI) focused model for detecting clinically significant prostate cancer(csPCa)( Gleason score ≥ 7) in TZ PI-RADS 3 and 4 lesions, compared to the Risk-based model (PI-RADS ≥ 3 and PSA density (PSAD) ≥ 0.15 ng/ml/cm³).

METHODS

A multi-center, retrospective cohort analysis was conducted on consecutive patients with PI-RADS 3 or 4 and eligible biopsy result. Multivariable logistic regression identified predictors of csPCa, followed by the areas under the curve(AUC) and decision curve analysis (DCA) comparisons between the Risk-based and BpMRI focused models, with external validation.

RESULTS

A total of 121 patients with 231 lesions in the development cohort(cohort 1) and 45 patients with 81 lesions the external validation cohort(cohort 2) were included between January 2020 and December 2024. The AUCs of the BpMRI-focused model were higher than those of the risk-based model in both the development cohort (0.71 [95% CI: 0.62-0.81] vs. 0.83 [95% CI: 0.74-0.92], p < 0.05) and the external validation cohort (0.75 [95% CI: 0.63-0.87] vs. 0.87 [95% CI: 0.79-0.95], p < 0.05). Furthermore, the BpMRI Focused Model significantly reduced the number of false positives for clinically significant prostate cancer compared to the Risk-Based Model [54 (23%) vs. 142 (61%), p < 0.002], while maintaining a cancer detection rate comparable to the PI-RADS ≥ 3 strategy (both p > 0.05). Additionally, the BpMRI Focused Model achieved a higher biopsy avoidance rate for csPCa [15 (6%)] compared to the Risk-Based Model [10 (4%)], though the difference was not statistically significant (p = 0.30).

CONCLUSION

In clinical decision-making, lesions in the TZ with PI-RADS 3 or 4 can be incorporated into the BpMRI focused model to reduce unnecessary biopsies.

Development and Validation of an Algorithm for Segmentation of the Prostate and its Zones from Three-dimensional Transrectal Multiparametric Ultrasound Images

Background and objective

Multiparametric ultrasound (mpUS) is being investigated as an alternative to magnetic resonance imaging (MRI) for detection of prostate cancer (PC). Automated prostate segmentation facilitates workflows, and zonal segmentation can aid in PC diagnosis, accounting for differences in imaging characteristics and tumor incidence. Our aim was to develop a deep learning algorithm that can automatically segment the prostate and its zones on three-dimensional (3D) contrast-enhanced ultrasound (CEUS) and conventional brightness-mode (B-mode) images (NCT04605276).

Methods

A total of 259 3D mpUS images were collected from men with suspicion for PC in a prospective multicenter trial to develop a computer-aided diagnosis system for PC. Manual segmentation was performed using a custom tool, and an algorithm was developed using a convolutional neural network based on the U-Net architecture.

Key findings and limitations

Cross-validation of the automated segmentation algorithm revealed Dice similarity coefficients (DSCs) of 0.91 (95% confidence interval [CI] 0.90-0.91) for CEUS and 0.94 (95% CI 0.93-0.94) for B-mode ultrasound for 3D prostate segmentation. Zonal segmentation was less accurate, with DSCs of 0.83 (95% CI 0.82-0.84) for CEUS and 0.86 (95% CI 0.85-0.87) for B-mode ultrasound. There was high agreement for prostate volume between automatic segmentation on CEUS and physician-estimated volumes on MRI (R2 = 0.96). Qualitative assessment of prostate segmentation using a scale from 1 to 5 revealed a median grade of 5 (interquartile range [IQR] 4-5) for manual segmentation and 4 (IQR 4-5) for automated segmentation (p = 0.10).

Conclusions and clinical implications

Our deep learning algorithm demonstrated strong performance for automatic prostate and zonal segmentation from 3D CEUS and B-mode ultrasound images.

Patient summary

We developed a computer tool to automatically identify the prostate in three-dimensional ultrasound images. The results show high accuracy and closely match manual assessments by urologists. This tool has potential for use in a computer-aided diagnostic system for prostate cancer.

A retrospective study on predicting clinically significant prostate cancer via a bi-parametric ultrasound-based deep learning radiomics model

Purpose

This study aimed to establish and evaluate a model utilizing bi-parametric ultrasound-based deep learning radiomics (DLR) in conjunction with clinical factors to anticipate clinically significant prostate cancer (csPCa).

Methods

We retrospectively analyzed 232 participants from our institution who underwent both B-mode ultrasound and shear wave elastography (SWE) prior to prostate biopsy between June 2022 and December 2023. A random allocation placed the participants into training and test cohorts with a 7:3 distribution. We developed a nomogram that integrates DLR with clinical factors within the training cohort, which was subsequently validated using the test cohort. The diagnostic performance and clinical applicability were evaluated with receiver operating characteristic (ROC) curve analysis and decision curve analysis.

Results

In our study, the bi-parametric ultrasound-based DLR model demonstrated an area under the curve (AUC) of 0.80 (95%CI: 0.70-0.91) in the test set, surpassing the performance of both the radiomics and deep learning models individually. By integrating clinical factors, a composite model, presented as the nomogram, was developed and exhibited superior diagnostic performance, achieving an AUC of 0.87 (95%CI: 0.77-0.95) in the test set. The performance exceeded that of the DLR (P = 0.049) and the clinical model (AUC = 0.79, 95%CI: 0.69-0.86, P = 0.041). Furthermore, the decision curve analysis indicated that the composite model provided a greater net benefit across a various high-risk threshold than the DLR or the clinical model alone.

Conclusion

To our knowledge, this is the first proposal of a nomogram integrating ultrasound-based DLR with clinical indicators for predicting csPCa. This nomogram can improve the accuracy of csPCa prediction and may help physicians make more confident decisions regarding interventions, particularly in settings where MRI is unavailable.

Patients with multiple mpMRI region of interests: should we omit targeted biopsies of secondary lesions?

PURPOSE

To assess the value of secondary lesion-targeted biopsy (SLx) in detecting prostate cancer (PCa) among patients with multifocal disease.

METHODS

A total of 298 biopsy-naïve patients with 612 lesions (all with Prostate Imaging Reporting and Data System [PI-RADS] v2.1 ≥ 3) underwent cognitive fusion-targeted biopsy (TB) combined with systematic biopsy (SB). Our primary endpoints were to compare the detection rates of PCa and clinically significant PCa (csPCa) across different biopsy strategies (Index lesion-targeted biopsy [ILx] vs. ILx + SLx and ILx + SB vs. ILx + SLx + SB) and to define potential indications for SLx using PI-RADS and PSA density (PSAD). Secondary endpoint was to evaluate the predictive performance of index lesion (IL)- and SL-based multivariate logistic regression (MVA) models for csPCa.

RESULTS

The overall detection rates for PCa and csPCa were 71% and 60%, with ILx + SLx + SB as the gold standard. Adding SLx to ILx modestly increased detection rates for PCa (63% vs. 65%, P = 0.016) and csPCa (55% vs. 58%, P = 0.004), but offered no significant advantage over ILx + SB. Stratification by PI-RADS and PSAD revealed that focusing on 80% intermediate- to high-risk lesions detected 39% csPCa while reducing 20% low-risk SLx at the cost of missing 1.6% csPCa. IL-based models outperformed SL-based models in predicting csPCa (Hosmer-Lemeshow P = 0.653 vs. 0.461).

CONCLUSION

SLx provides limited benefit in csPCa detection when ILx and SB have already been performed. Combining PI-RADS scores and PSAD helps identify patients who could benefit from SLx while avoiding unnecessary procedures in low-risk cases.

CLINICAL TRIAL REGISTRATION

No. 2016 - 1252, January 2017.

Mapping of prostate cancer microvascular patterns using super-resolution ultrasound imaging

BACKGROUND

Super-resolution ultrasound imaging (SRUI) is a rapidly expanding field with the potential to impact cancer management. Image processing algorithms applied to contrast-enhanced ultrasound (CEUS) video data can track the path of the contrast agent and produce high-resolution maps of vascular networks. Our aim was to develop SRUI for mapping prostate vascular dynamics and to assess the feasibility of identifying vascular patterns associated with prostate cancer.

METHODS

Tracking algorithms for SRUI were developed using in silico data and validated in pre-clinical CEUS video collected from the sheep ovary. Algorithm performance was then assessed in a retrospective study of 54 image planes within 14 human prostates. CEUS data was collected for each plane, and regions of suspected cancer in each were identified from biopsy data.

RESULTS

Of three algorithms assessed, utilising vascular knowledge was found to be the most robust method. Regions of suspected cancer were associated with increased blood flow volume and speed while avascular regions were also identified. Ten scan planes had confirmed Gleason 7 cancer; of these 10 planes, 7 had distinct regions of fast and high-volume flow, while 6 had both avascular and high flow regions. The cancer-free planes had more consistent, low blood flow values across the plane.

CONCLUSION

SRUI can be used to identify imaging biomarkers associated with vascular architecture and dynamics. These multiparameter biomarkers may be useful in pinpointing regions of significant prostate cancer.

RELEVANCE STATEMENT

Super-resolution ultrasound imaging can generate microvascular maps of the prostate, revealing tissue patterns and presenting significant potential for the identification of multiple biomarkers associated with the localisation of prostate cancer.

TRIAL REGISTRATION

Retrospectively registered NCT02831920, date 5/7/2016 https://www.

CLINICALTRIALS

gov/study/NCT02831920 .

KEY POINTS

An algorithm was developed and tested in synthetic pre-clinical and clinical data. Maps of blood vessels were created using contrast-enhanced ultrasound imaging. Specific presentations of vasculature at regions of prostate cancer have been identified.

Predicting clinically significant prostate cancer in elderly patients: A nomogram approach with shear wave elastography

PURPOSE

This study was to construct a nomogram utilizing shear wave elastography and assess its efficacy in detecting clinically significant prostate cancer (csPCa).

METHODS

290 elderly people with suspected PCa who received prostate biopsy and shear wave elastography (SWE) imaging were respectively registered from April 2022 to December 2023. The elderly participants were stratified into two groups: those with csPCa and those without csPCa, which encompassed cases of clinically insignificant prostate cancer (cisPCa) and non-prostate cancer tissue, as determined by pathology findings. The LASSO algorithm, known as the least absolute shrinkage and selection operator, was utilized to identify features. Logistic regression analysis was utilized to establish models. Receiver operating characteristic (ROC) and calibration curves were utilized to evaluate the discriminatory ability of the nomogram. Bootstrap (1000 bootstrap iterations) was employed for internal validation and comparison with two models. A decision curve and a clinical impact curve were employed to assess the clinical usefulness.

RESULTS

Our nomogram, which contained Emean, ΔEmean, prostate volume, prostate-specific antigen density (PSAD), and transrectal ultrasound (TRUS), showed better discrimination (AUC = 0.89; 95% CI: 0.83-0.94), compared to the clinical model without SWE parameters (p = 0.0007). Its accuracy, sensitivity and specificity were 0.83, 0.89 and 0.78, respectively. Based on the analysis of decision curve, the thresholds ranged from 5% to 90%. According to our nomogram, biopsying patients at a 20% probability threshold resulted in a 25% reduction in biopsies without missing any csPCa. The clinical impact curve demonstrated that the nomogram's predicted outcome is closer to the observed outcome when the probability threshold reaches 20% or greater.

CONCLUSION

Our nomogram demonstrates efficacy in identifying elderly individuals with clinically significant prostate cancer, thereby facilitating informed clinical decision-making based on diagnostic outcomes and potential clinical benefits.

French AFU Cancer Committee Guidelines - Update 2024-2026: Prostate cancer - Diagnosis and management of localised disease

OBJECTIVE

The aim of the Oncology Committee of the French Urology Association is to propose updated recommendations for the diagnosis and management of localized prostate cancer (PCa).

METHODS

A systematic review of the literature from 2022 to 2024 was conducted by the CCAFU on the elements of diagnosis and therapeutic management of localized PCa, evaluating references with their level of evidence.

RESULTS

The recommendations set out the genetics, epidemiology and diagnostic methods of PCa, as well as the concepts of screening and early detection. MRI, the reference imaging test for localized cancer, is recommended before prostate biopsies are performed. Molecular imaging is an option for disease staging. Performing biopsies via the transperineal route reduces the risk of infection. Active surveillance is the standard treatment for tumours with a low risk of progression. Therapeutic methods are described in detail, and recommended according to the clinical situation.

CONCLUSION

This update of French recommendations should help to improve the management of localized PCa.

Prostate cancer screening and management in patients candidate for endoscopic enucleation of the prostate: an international survey

BACKGROUND

To explore how urologists manage prostate cancer (PCa) screening and treatment in patients undergoing endoscopic enucleation of the prostate (EEP).

METHODS

A team of experts in EEP collaboratively formulated the survey questions through an interactive process. The survey opened in January 2024 and closed in February 2024.

RESULTS

102 urologists responded, revealing that most use PSA and digital rectal examination for screening, with high PSA and abnormal DRE prompting prostate MRI. 75% perform pre-EEP biopsies. For incidental low-grade PCa, active surveillance is preferred. For intermediate-grade PCa, most use PSA and MRI for workup, often choosing active surveillance if post-EEP biopsies are negative. There's no consensus on abnormal post-operative PSA thresholds.

CONCLUSIONS

While urologists are aware of PCa management in EEP candidates, future work should focus on developing optimal post-EEP screening protocols.

Clinical Validation of Multiparametric Ultrasound for Detecting Clinically Significant Prostate Cancer Using Computer-Aided Diagnosis: A Direct Comparison with the Magnetic Resonance Imaging Pathway

We present the protocol for a study testing the hypothesis that a computer-aided diagnosis (CAD) system for three-dimensional multiparametric ultrasound (3D mpUS) is noninferior to magnetic resonance imaging (MRI) in guiding prostate biopsies for detection of clinically significant prostate cancer (csPCa). The prospective study has a fully paired design for assessment of diagnostic accuracy and is registered on ClinicalTrials.gov as NCT06281769. A total of 438 biopsy-naïve men scheduled for prostate MRI evaluation because of an abnormal digital rectal examination and/or elevated serum prostate-specific antigen will be included. All patients will undergo both MRI (multiparametric or biparametric) and 3D mpUS with CAD (PCaVision). Suspicious lesions will be independently identified using each imaging technique. MRI targeted biopsy (TBx) and/or PCaVision TBx will be performed if suspicious lesions are identified on imaging. When both PCaVision and MRI identify lesions in an individual patient, the TBx order for this patient will be randomized. Three TBx samples per lesion will be taken for a maximum of two lesions per modality. The primary objective is the detection rate for csPCa (International Society of Urological Pathology grade group [GG] ≥2) with the PCaVision versus the MRI TBx pathway. The noninferiority margin for the absolute difference in detection rates is set at a difference of 5%. Secondary outcomes are the proportion of men in whom TBx could have been safely omitted in each pathway. Additional diagnostic accuracy analyses will be performed for different definitions of PCa (GG ≥3; GG ≥2 with cribriform growth and/or intraductal carcinoma; and GG 1). The frequency of insufficient image quality for the two pathways will also be assessed. Lastly, we will determine the diagnostic performance for csPCa detection at various 3D mpUS image quality thresholds for PCaVision.

EAU-EANM-ESTRO-ESUR-ISUP-SIOG Guidelines on Prostate Cancer-2024 Update. Part I: Screening, Diagnosis, and Local Treatment with Curative Intent

BACKGROUND AND OBJECTIVE

The European Association of Urology (EAU)-European Association of Nuclear Medicine (EANM)-European Society for Radiotherapy and Oncology (ESTRO)-European Society of Urogenital Radiology (ESUR)-International Society of Urological Pathology (ISUP)-International Society of Geriatric Oncology (SIOG) guidelines provide recommendations for the management of clinically localised prostate cancer (PCa). This paper aims to present a summary of the 2024 version of the EAU-EANM-ESTRO-ESUR-ISUP-SIOG guidelines on the screening, diagnosis, and treatment of clinically localised PCa.

METHODS

The panel performed a literature review of all new data published in English, covering the time frame between May 2020 and 2023. The guidelines were updated, and a strength rating for each recommendation was added based on a systematic review of the evidence.

KEY FINDINGS AND LIMITATIONS

A risk-adapted strategy for identifying men who may develop PCa is advised, generally commencing at 50 yr of age and based on individualised life expectancy. The use of multiparametric magnetic resonance imaging in order to avoid unnecessary biopsies is recommended. When a biopsy is considered, a combination of targeted and regional biopsies should be performed. Prostate-specific membrane antigen positron emission tomography imaging is the most sensitive technique for identifying metastatic spread. Active surveillance is the appropriate management for men with low-risk PCa, as well as for selected favourable intermediate-risk patients with International Society of Urological Pathology grade group 2 lesions. Local therapies are addressed, as well as the management of persistent prostate-specific antigen after surgery. A recommendation to consider hypofractionation in intermediate-risk patients is provided. Patients with cN1 PCa should be offered a local treatment combined with long-term intensified hormonal treatment.

CONCLUSIONS AND CLINICAL IMPLICATIONS

The evidence in the field of diagnosis, staging, and treatment of localised PCa is evolving rapidly. These PCa guidelines reflect the multidisciplinary nature of PCa management.

PATIENT SUMMARY

This article is the summary of the guidelines for "curable" prostate cancer. Prostate cancer is "found" through a multistep risk-based screening process. The objective is to find as many men as possible with a curable cancer. Prostate cancer is curable if it resides in the prostate; it is then classified into low-, intermediary-, and high-risk localised and locally advanced prostate cancer. These risk classes are the basis of the treatments. Low-risk prostate cancer is treated with "active surveillance", a treatment with excellent prognosis. For low-intermediary-risk active surveillance should also be discussed as an option. In other cases, active treatments, surgery, or radiation treatment should be discussed along with the potential side effects to allow shared decision-making.

Can 3D Multiparametric Ultrasound Imaging Predict Prostate Biopsy Outcome?

OBJECTIVES

To assess the value of 3D multiparametric ultrasound imaging, combining hemodynamic and tissue stiffness quantifications by machine learning, for the prediction of prostate biopsy outcomes.

METHODS

After signing informed consent, 54 biopsy-naïve patients underwent a 3D dynamic contrast-enhanced ultrasound (DCE-US) recording, a multi-plane 2D shear-wave elastography (SWE) scan with manual sweeping from base to apex of the prostate, and received 12-core systematic biopsies (SBx). 3D maps of 18 hemodynamic parameters were extracted from the 3D DCE-US quantification and a 3D SWE elasticity map was reconstructed based on the multi-plane 2D SWE acquisitions. Subsequently, all the 3D maps were segmented and subdivided into 12 regions corresponding to the SBx locations. Per region, the set of 19 computed parameters was further extended by derivation of eight radiomic features per parameter. Based on this feature set, a multiparametric ultrasound approach was implemented using five different classifiers together with a sequential floating forward selection method and hyperparameter tuning. The classification accuracy with respect to the biopsy reference was assessed by a group-k-fold cross-validation procedure, and the performance was evaluated by the Area Under the Receiver Operating Characteristics Curve (AUC).

RESULTS

Of the 54 patients, 20 were found with clinically significant prostate cancer (csPCa) based on SBx. The 18 hemodynamic parameters showed mean AUC values varying from 0.63 to 0.75, and SWE elasticity showed an AUC of 0.66. The multiparametric approach using radiomic features derived from hemodynamic parameters only produced an AUC of 0.81, while the combination of hemodynamic and tissue-stiffness quantifications yielded a significantly improved AUC of 0.85 for csPCa detection (p-value < 0.05) using the Gradient Boosting classifier.

CONCLUSIONS

Our results suggest 3D multiparametric ultrasound imaging combining hemodynamic and tissue-stiffness features to represent a promising diagnostic tool for biopsy outcome prediction, aiding in csPCa localization.

Factors associated with pathological up-staging in MRI cT3a prostate cancer - a retrospective study from a high-volume centre

INTRODUCTION

Multiparametric MRI (mpMRI) parameters of pT3a prostate cancer have not been examined in large cohort studies. Therefore, we aimed to identify factors associated with up-staging of mpMRI cT3a in post-operative histopathological confirmation.

METHODS

Retrospective analysis of a prospectively maintained database of a single UK cancer centre. Only cT3a cases who underwent robotic-assisted radical prostatectomy (RARP) were included (N = 383). MRI and specimen histopathology was reviewed independently by expert uro-radiologists and uro-histopathologists, respectively. Factors included age, BMI, prostate-specific antigen (PSA) level, biopsy international society of urological pathology (ISUP) grade, Prostate Imaging Reporting & Data System (PI-RADS®) score, tumour size, tumour coverage of gland (%), gland weight and surgical margins were analysed as predictors of pT3a prostate cancer.

RESULTS

N = 383. Mean age 66 years (58-71), mean BMI 27.1 kg/m2 (25.0-30.0). 314 (82.0%) cases down- unchanged or down-staged, and 69 (18.0%) cases upstaged. PSA level (P = 0.002), PI-RADS score (P < 0.001) and ISUP grade (P < 0.001) are positively associated with upstage categories. ISUP grade ≥3 (OR 5.45, CI 1.88, 9.29, P < 0.002), PI-RADS score ≥4 (OR 3.92, CI 1.88-9.29, P < 0.001) and tumour coverage (OR 1.06, CI 1.05-1.08, P < 0.001) significantly positively associated with upstaging disease, with concurrent decreased probability of downstaging (OR 0.55, 0.14, 0.44, respectively, P < 0.05). Tumour coverage was positively correlated with increasing positive surgical margins (P < 0.05). Capsular contact > 15 mm was very unlikely to be upstaged (OR 0.36, CI 0.21-0.62, P < 0.001), aligning with published results past the widely accepted significant level for extracapsular disease on MRI.

CONCLUSION

The study has identified PSA level, ISUP, PI-RADS score, tumour volume and percentage coverage are key predictive factors in cT3a upstaging. This study uniquely shows tumour coverage percentage as a predictor of cT3a upstaging on mpMRI. ISUP is the strongest predictor, followed by PI-RADS score and tumour coverage of gland. Multi-institutional studies are needed to confirm our findings.

Deep learning model for the detection of prostate cancer and classification of clinically significant disease using multiparametric MRI in comparison to PI-RADs score

BACKGROUND

The Prostate Imaging Reporting and Data System (PI-RADS) is an established reporting scheme for multiparametric magnetic resonance imaging (mpMRI) to distinguish clinically significant prostate cancer (csPCa). Deep learning (DL) holds great potential for automating csPCa classification on mpMRI.

METHOD

To compare the performance between a DL algorithm and PI-RADS categorization in PCa detection and csPCa classification, we included 1,729 consecutive patients who underwent radical prostatectomy or biopsy in Tongji hospital. We developed DL models by integrating individual mpMRI sequences and employing an ensemble approach for distinguishing between csPCa and CiSPCa (specifically defined as PCa with a Gleason group 1 or benign prostate disease, training cohort: 1,285 patients vs. external testing cohort: 315 patients).

RESULTS

DL-based models exhibited higher csPCa detection rates than PI-RADS categorization (area under the curve [AUC]: 0.902; sensitivity: 0.728; specificity: 0.906 vs. AUC: 0.759; sensitivity: 0.761; specificity: 0.756) (P < 0.001) Notably, DL networks exhibited significant strength in the prostate-specific antigen (PSA) arm < 10 ng/ml compared with PI-RADS assessment (AUC: 0.788; sensitivity: 0.588; specificity: 0.883 vs. AUC: 0.618; sensitivity: 0.379; specificity: 0.763) (P = 0.041).

CONCLUSIONS

We developed DL-based mpMRI ensemble models for csPCa classification with improved sensitivity, specificity, and accuracy compared with clinical PI-RADS assessment. In the PSA-stratified condition, the DL ensemble model performed better than PI-RADS in the detection of csPCa in both the high PSA group and the low PSA group.

Diagnostic accuracy of qualitative and quantitative magnetic resonance imaging-guided contrast-enhanced ultrasound (MRI-guided CEUS) for the detection of prostate cancer: a prospective and multicenter study

PURPOSE

To evaluate the diagnostic value of MRI-guided contrast-enhanced ultrasound (CEUS) for prostate cancer (PCa) diagnosis, and characteristics of PCa in qualitative and quantitative CEUS.

MATERIAL AND METHODS

This prospective and multicenter study included 250 patients (133 in the training cohort, 57 in the validation cohort and 60 in the test cohort) who underwent MRI, MRI-guided CEUS and prostate biopsy between March 2021 and February 2023. MRI interpretation, qualitative and quantitative CEUS analysis were conducted. Multitree extreme gradient boosting (XGBoost) machine learning-based models were applied to select the eight most important quantitative parameters. Univariate and multivariate logistic regression models were constructed to select independent predictors of PCa. Diagnostic value was determined for MRI, qualitative and quantitative CEUS using the area under receiver operating characteristic curve (AUC).

RESULTS

The performance of quantitative CEUS was superior to that of the qualitative CEUS and MRI in predicting PCa. The AUC was 0.779 (95%CI 0.70-0.849), 0.756 (95%CI 0.638-0.874) and 0.759 (95%CI 0.638-0.879) of qualitative CEUS, and 0.885 (95%CI 0.831-0.940), 0.802 (95%CI 0.684-0.919) and 0.824 (95%CI 0.713-0.936) of quantitative CEUS in training, validation and test cohort, respectively. Compared with quantitative CEUS, MRI achieved less well performance for AUC 0.811 (95%CI 0.741-0.882, p = 0.099), 0.748 (95%CI 0.628-0.868, p = 0.539) and 0.737 (95%CI 0.602-0.873, p = 0.029), respectively. Moreover, the highest specificity of 80.6% was obtained by quantitative CEUS.

CONCLUSION

We developed a reliable method of MRI-guided CEUS that demonstrated enhanced performance compared to MRI. The qualitative and quantitative CEUS characteristics will contribute to improved diagnosis of PCa.

Identification of impactful imaging biomarker: Clinical applications for breast and prostate carcinoma

BACKGROUND

Imaging biomarkers are quantitative parameters from imaging modalities, which are collected noninvasively, allow conclusions about physiological and pathophysiological processes, and may consist of single (monoparametric) or multiple parameters (bi- or multiparametric).

METHOD

This review aims to present the state of the art for the quantification of multimodal and multiparametric imaging biomarkers. Here, the use of biomarkers using artificial intelligence will be addressed and the clinical application of imaging biomarkers in breast and prostate cancers will be explained. For the preparation of the review article, an extensive literature search was performed based on Pubmed, Web of Science and Google Scholar. The results were evaluated and discussed for consistency and generality.

RESULTS AND CONCLUSION

Different imaging biomarkers (multiparametric) are quantified based on the use of complementary imaging modalities (multimodal) from radiology, nuclear medicine, or hybrid imaging. From these techniques, parameters are determined at the morphological (e. g., size), functional (e. g., vascularization or diffusion), metabolic (e. g., glucose metabolism), or molecular (e. g., expression of prostate specific membrane antigen, PSMA) level. The integration and weighting of imaging biomarkers are increasingly being performed with artificial intelligence, using machine learning algorithms. In this way, the clinical application of imaging biomarkers is increasing, as illustrated by the diagnosis of breast and prostate cancers.

KEY POINTS

· Imaging biomarkers are quantitative parameters to detect physiological and pathophysiological processes.. · Imaging biomarkers from multimodality and multiparametric imaging are integrated using artificial intelligence algorithms.. · Quantitative imaging parameters are a fundamental component of diagnostics for all tumor entities, such as for mammary and prostate carcinomas..

CITATION FORMAT

· Bäuerle T, Dietzel M, Pinker K et al. Identification of impactful imaging biomarker: Clinical applications for breast and prostate carcinoma. Fortschr Röntgenstr 2024; 196: 354 - 362.

Radiogenomic analysis of ultrasound phenotypic features coupled to proteomes predicts metastatic risk in primary prostate cancer

BACKGROUND

Primary prostate cancer with metastasis has a poor prognosis, so assessing its risk of metastasis is essential.

METHODS

This study combined comprehensive ultrasound features with tissue proteomic analysis to obtain biomarkers and practical diagnostic image features that signify prostate cancer metastasis.

RESULTS

In this study, 17 ultrasound image features of benign prostatic hyperplasia (BPH), primary prostate cancer without metastasis (PPCWOM), and primary prostate cancer with metastasis (PPCWM) were comprehensively analyzed and combined with the corresponding tissue proteome data to perform weighted gene co-expression network analysis (WGCNA), which resulted in two modules highly correlated with the ultrasound phenotype. We screened proteins with temporal expression trends based on the progression of the disease from BPH to PPCWOM and ultimately to PPCWM from two modules and obtained a protein that can promote prostate cancer metastasis. Subsequently, four ultrasound image features significantly associated with the metastatic biomarker HNRNPC (Heterogeneous nuclear ribonucleoprotein C) were identified by analyzing the correlation between the protein and ultrasound image features. The biomarker HNRNPC showed a significant difference in the five-year survival rate of prostate cancer patients (p < 0.0053). On the other hand, we validated the diagnostic efficiency of the four ultrasound image features in clinical data from 112 patients with PPCWOM and 150 patients with PPCWM, obtaining a combined diagnostic AUC of 0.904. In summary, using ultrasound imaging features for predicting whether prostate cancer is metastatic has many applications.

CONCLUSION

The above study reveals noninvasive ultrasound image biomarkers and their underlying biological significance, which provide a basis for early diagnosis, treatment, and prognosis of primary prostate cancer with metastasis.

Do we need MRI in all biopsy naïve patients? A multicenter cohort analysis

PURPOSE

The combined approach (CB) of magnetic resonance imaging (MRI)-guided biopsy (TB) and systematic biopsy (SB) is strongly recommended based on numerous studies in biopsy naïve men with suspicion of clinically significant prostate cancer (csPCA). However, the unbalanced accessibility of MRI, challenges related to reimbursement and the scarcity of specialized medical practitioners continue to impede a widespread implementation. Therefore, our objective was to determine a subset of men that could undergo SB without an increased risk of underdiagnosis at reduced expenses.

METHODS

A multicenter analysis of 2714 men with confirmed PCA and suspicious MRI who underwent CB were enrolled. Cancer detection rates were compared between the different biopsy routes SB, TB and CB using McNemar paired test. Additionally, Gleason grade up- and down-grading was determined.

RESULTS

CB detected more csPCA than TB and SB (p < 0.001), irrespective of MRI findings or biopsy route (transperineal vs. transrectal). Thereby, single biopsy approaches misgraded > 50% of csPCA. TB showed higher diagnostic efficiency, defined as csPCA detection per biopsy core than CB and SB (p < 0.001). For patients with abnormal DRE and PSA levels > 12.5 ng/ml, PSAD > 0.35 ng/ml/cm3, or > 75 years, SB and CB showed similar csPCA detection rates.

CONCLUSION

Conducting CB provides the highest level of diagnostic certainty and minimizes the risk of underdiagnosis in almost all biopsy-naive men. However, in patients with suspicious DRE and high PSA levels, PSAD, or advanced age solely using SB leads to similar csPCA detection rates. Thus, a reduced biopsy protocol may be considered for these men in case resources are limited.

Is imaging accurate enough to detect index lesion in prostate cancer? Analysis of the performance of MRI and other imaging modalities

Prostate imaging techniques have progressed across the years allowing for a better detection and characterization of prostate cancer (PCa) lesions. These advancements have led to the possibility to also improve and tailor the treatments on the most aggressive lesion, defined as Index Lesion (IL), to reduce morbidity. The IL is, indeed, considered as the entity which encompass the most aggressive features in prostate cancer disease. Multiparametric magnetic resonance imaging (mpMRI) has emerged as the suggested tool to detect the disease and plan treatments, including those under investigation such as focal therapy (FT). Our review aimed to query the literature on the ability of mpMRI in IL detection and to explore the future perspectives in PCa IL diagnosis. A review of the literature was performed from January 2010 to July 2023. All studies investigating the performance of mpMRI and other main imaging techniques able to detect the IL were assessed and evaluated. mpMRI performs well in the detection of IL with a sensitivity which reaches 71% to 94% among the different studies. However, mpMRI seems to have limited sensitivity in the detection of small tumours (<0.5 mL) and low-grade histology lesions. To overcome these limitations other diagnostic imaging techniques have been proposed. Multiparametric Ultrasound has shown results comparable to mpMRI while detecting 4.3% fewer clinically significant PCa (P=0.042). Positron emission tomography-based modalities using PSMA seems to have higher sensitivity than mpMRI, being able to yield from 13.5% to 18.2% additional cancers. MRI has emerged as the recommended tool since most of the IL can be easily identified, and is the imaging of choice while selecting patients for FT. Other imaging modalities has been proposed to improve PCa lesions detection, but results need to be confirmed by ongoing randomized controlled trial.

Assessing cancer risk in the anterior part of the prostate using micro-ultrasound: validation of a novel distinct protocol

PURPOSE

To develop and validate a micro-ultrasound risk score that predicts the likelihood of significant prostate cancer in the anterior zone.

METHODS

Patients were enrolled from three expert institutions familiar with micro-ultrasound. The study was conducted in two phases. First, the PRI-MUS anterior score was developed by assessing selected prostate videos from patients who subsequently underwent radical prostatectomy. Second, seven urology readers with varying levels of experience in micro-ultrasound examination evaluated prostate loops according to the PRI-MUS anterior score. Each reader watched the videos and recorded the likelihood of the presence of significant cancer in the anterior part of the prostate in a three-point scale. The coherence among the readers was calculated using the Fleiss kappa and the Cronbach alpha.

RESULTS

A total of 102 selected prostate scans were used to develop the risk assessment for anterior zone cancer in the prostate. The score comprised three categories: likely, equivocal, and unlikely. The median (IQR) sensitivity, specificity, positive predictive value, and negative predictive value for the seven readers were 72% (68-84), 68% (64-84), 75% (72-81), and 73% (71-80), respectively. The mean SD ROC AUC was 0.75 ± 2%, while the Fleiss kappa and the Cronbach alpha were 0.179 and 0.56, respectively.

CONCLUSION

Micro-ultrasound can detect cancerous lesions in the anterior part of the prostate. When combined with the PRI-MUS protocol to assess the peripheral part, it enables an assessment of the entire prostate gland. Pending external validation, the PRI-MUS anterior score developed in this study might be implemented in clinical practice.

[Prostate cancer-multiparametric MRI and alternative approaches in intervention and therapy planning]

BACKGROUND

In recent years, multiparametric magnetic resonance imaging (mpMRI) of the prostate has gained importance and plays a crucial role in both personalized diagnostics and increasingly in the treatment planning for patients with prostate cancer.

OBJECTIVE

The aim of this study is to present established and innovative applications of MRI in the diagnosis and treatment of localized prostate cancer, evaluating their strengths and weaknesses. Furthermore, it will explore alternative approaches and compare them in a comprehensive manner.

MATERIALS AND METHODS

A systematic literature review on the application of mpMRI for biopsy and therapy planning was conducted.

RESULTS

The integration of modern imaging techniques, especially mpMRI, into the diagnostic algorithm has revolutionized prostate cancer diagnosis. MRI and MRI-guided biopsy detect more significant prostate cancer, with the potential to reduce unnecessary biopsies and the diagnosis of clinically insignificant carcinomas. In addition, MRI provides crucial information for risk stratification and treatment planning in prostate cancer patients, both before radical prostatectomy and during active surveillance.

CONCLUSION

Multiparametric MRI offers significant added value for the diagnosis and treatment of localized prostate cancer. The advancement of MRI analysis, such as the implementation of artificial intelligence algorithms, holds the potential for further enhancing imaging diagnostics.

Emerging diagnostic and therapeutic technologies based on ultrasound-triggered biomaterials

Ultrasound (US) is a kind of acoustic wave with frequency higher than 20 kHz. Learning from the echo detection ability of bats and dolphins, scientists applied US for clinical imaging by sending out US waves and detecting echoes with shifted intensities and frequencies from human tissue. US has long played a critical role in noninvasive, real-time, low-cost and portable diagnostic imaging. With the in-depth study of US in multidisciplinary fields, US and US-responsive materials have shown practical value in not only disease diagnosis, but also disease treatment. In this review, we introduce the recently proposed and representative US-responsive materials for biomedical applications, including diagnostic and therapeutic applications. We focused on US-mediated physicochemical therapies, such as sonodynamic therapy, high-intensity focused US ablation, sonothermal therapy, thrombolysis, etc, and US-controlled delivery of chemotherapeutics, gases, genes, proteins and bacteria. We conclude with the current challenges facing the clinical translation of smart US-responsive materials and prospects for the future development of US medicine.

Advancements in MRI-Based Radiomics and Artificial Intelligence for Prostate Cancer: A Comprehensive Review and Future Prospects

The use of multiparametric magnetic resonance imaging (mpMRI) has become a common technique used in guiding biopsy and developing treatment plans for prostate lesions. While this technique is effective, non-invasive methods such as radiomics have gained popularity for extracting imaging features to develop predictive models for clinical tasks. The aim is to minimize invasive processes for improved management of prostate cancer (PCa). This study reviews recent research progress in MRI-based radiomics for PCa, including the radiomics pipeline and potential factors affecting personalized diagnosis. The integration of artificial intelligence (AI) with medical imaging is also discussed, in line with the development trend of radiogenomics and multi-omics. The survey highlights the need for more data from multiple institutions to avoid bias and generalize the predictive model. The AI-based radiomics model is considered a promising clinical tool with good prospects for application.

Development and validation of a clinic machine-learning nomogram for the prediction of risk stratifications of prostate cancer based on functional subsets of peripheral lymphocyte

BACKGROUND

Non-invasive risk stratification contributes to the precise treatment of prostate cancer (PCa). In previous studies, lymphocyte subsets were used to differentiate between low-/intermediate-risk and high-risk PCa, with limited clinical value and poor interpretability. Based on functional subsets of peripheral lymphocyte with the largest sample size to date, this study aims to construct an easy-to-use and robust nomogram to guide the tripartite risk stratifications for PCa.

METHODS

We retrospectively collected data from 2039 PCa and benign prostate disease (BPD) patients with 42 clinical characteristics on functional subsets of peripheral lymphocyte. After quality control and feature selection, clinical data with the optimal feature subset were utilized for the 10-fold cross-validation of five Machine Learning (ML) models for the task of predicting low-, intermediate- and high-risk stratification of PCa. Then, a novel clinic-ML nomogram was constructed using probabilistic predictions of the trained ML models via the combination of a multivariable Ordinal Logistic Regression analysis and the proposed feature mapping algorithm.

RESULTS

197 PCa patients, including 56 BPD, were enrolled in the study. An optimal subset with nine clinical features was selected. Compared with the best ML model and the clinic nomogram, the clinic-ML nomogram achieved the superior performance with a sensitivity of 0.713 (95% CI 0.573-0.853), specificity of 0.869 (95% CI 0.764-0.974), F1 of 0.699 (95% CI 0.557-0.841), and AUC of 0.864 (95% CI 0.794-0.935). The calibration curve and Decision Curve Analysis (DCA) indicated the predictive capacity and net benefits of the clinic-ML nomogram were improved.

CONCLUSION

Combining the interpretability and simplicity of a nomogram with the efficacy and robustness of ML models, the proposed clinic-ML nomogram can serve as an insight tool for preoperative assessment of PCa risk stratifications, and could provide essential information for the individual diagnosis and treatment in PCa patients.

Three-dimensional convolutional neural network model to identify clinically significant prostate cancer in transrectal ultrasound videos: a prospective, multi-institutional, diagnostic study

BACKGROUND

Identifying patients with clinically significant prostate cancer (csPCa) before biopsy helps reduce unnecessary biopsies and improve patient prognosis. The diagnostic performance of traditional transrectal ultrasound (TRUS) for csPCa is relatively limited. This study was aimed to develop a high-performance convolutional neural network (CNN) model (P-Net) based on a TRUS video of the entire prostate and investigate its efficacy in identifying csPCa.

METHODS

Between January 2021 and December 2022, this study prospectively evaluated 832 patients from four centres who underwent prostate biopsy and/or radical prostatectomy. All patients had a standardised TRUS video of the whole prostate. A two-dimensional CNN (2D P-Net) and three-dimensional CNN (3D P-Net) were constructed using the training cohort (559 patients) and tested on the internal validation cohort (140 patients) as well as on the external validation cohort (133 patients). The performance of 2D P-Net and 3D P-Net in predicting csPCa was assessed in terms of the area under the receiver operating characteristic curve (AUC), biopsy rate, and unnecessary biopsy rate, and compared with the TRUS 5-point Likert score system as well as multiparametric magnetic resonance imaging (mp-MRI) prostate imaging reporting and data system (PI-RADS) v2.1. Decision curve analyses (DCAs) were used to determine the net benefits associated with their use. The study is registered at https://www.chictr.org.cn with the unique identifier ChiCTR2200064545.

FINDINGS

The diagnostic performance of 3D P-Net (AUC: 0.85-0.89) was superior to TRUS 5-point Likert score system (AUC: 0.71-0.78, P = 0.003-0.040), and similar to mp-MRI PI-RADS v2.1 score system interpreted by experienced radiologists (AUC: 0.83-0.86, P = 0.460-0.732) and 2D P-Net (AUC: 0.79-0.86, P = 0.066-0.678) in the internal and external validation cohorts. The biopsy rate decreased from 40.3% (TRUS 5-point Likert score system) and 47.6% (mp-MRI PI-RADS v2.1 score system) to 35.5% (2D P-Net) and 34.0% (3D P-Net). The unnecessary biopsy rate decreased from 38.1% (TRUS 5-point Likert score system) and 35.2% (mp-MRI PI-RADS v2.1 score system) to 32.0% (2D P-Net) and 25.8% (3D P-Net). 3D P-Net yielded the highest net benefit according to the DCAs.

INTERPRETATION

3D P-Net based on a prostate grayscale TRUS video achieved satisfactory performance in identifying csPCa and potentially reducing unnecessary biopsies. More studies to determine how AI models better integrate into routine practice and randomized controlled trials to show the values of these models in real clinical applications are warranted.

FUNDING

The National Natural Science Foundation of China (Grants 82202174 and 82202153), the Science and Technology Commission of Shanghai Municipality (Grants 18441905500 and 19DZ2251100), Shanghai Municipal Health Commission (Grants 2019LJ21 and SHSLCZDZK03502), Shanghai Science and Technology Innovation Action Plan (21Y11911200), and Fundamental Research Funds for the Central Universities (ZD-11-202151), Scientific Research and Development Fund of Zhongshan Hospital of Fudan University (Grant 2022ZSQD07).

Textural Features of MR Images Correlate with an Increased Risk of Clinically Significant Cancer in Patients with High PSA Levels

BACKGROUND

Prostate cancer, which is associated with gland biology and also with environmental risks, is a serious clinical problem in the male population worldwide. Important progress has been made in the diagnostic and clinical setups designed for the detection of prostate cancer, with a multiparametric magnetic resonance diagnostic process based on the PIRADS protocol playing a key role. This method relies on image evaluation by an imaging specialist. The medical community has expressed its desire for image analysis techniques that can detect important image features that may indicate cancer risk.

METHODS

Anonymized scans of 41 patients with laboratory diagnosed PSA levels who were routinely scanned for prostate cancer were used. The peripheral and central zones of the prostate were depicted manually with demarcation of suspected tumor foci under medical supervision. More than 7000 textural features in the marked regions were calculated using MaZda software. Then, these 7000 features were used to perform region parameterization. Statistical analyses were performed to find correlations with PSA-level-based diagnosis that might be used to distinguish suspected (different) lesions. Further multiparametrical analysis using MIL-SVM machine learning was used to obtain greater accuracy.

RESULTS

Multiparametric classification using MIL-SVM allowed us to reach 92% accuracy.

CONCLUSIONS

There is an important correlation between the textural parameters of MRI prostate images made using the PIRADS MR protocol with PSA levels > 4 mg/mL. The correlations found express dependence between image features with high cancer markers and hence the cancer risk.

Multi-reader evaluation of different image quality scoring systems in prostate MRI

OBJECTIVES

To evaluate different image quality scoring systems in the assessment of factors limiting diagnostic accuracy of prostate MRI.

METHODS

This retrospective IRB-approved study included 281 patients undergoing prostate MRI prior to biopsy. Four readers (2 experienced, 2 novice) independently reviewed all MRI examinations (n = 295) and assigned scores for subjective image quality (1-5; 1:poor, 5:excellent), the PI-QUAL and the PSHS scoring system. The original PI-RADS scores were extracted from the report and transperineal template saturation biopsy served as histopathological reference.

RESULTS

Inter-reader agreement was found to be good, with PSHS showing highest agreement (kappa: 0.65). The PSHS scoring system performed well assessing the influence of image quality on sensitivity of MR for clinically-significant cancer for the experienced readers using a PI-RADS score cut-off ≥ 3/≥4, as did the PI-QUAL scoring system with a PI-RADS cut-off ≥ 4. For the less experienced radiologist, this was true for PSHS (clinically-significant and all cancers) and PI-QUAL scores (clinically-significant cancers) for a PI-RADS score ≥ 3. PSHS scores were positively associated with the detection of clinically-significant cancer based on a PI-RADS cut-off ≥ 4, OR 1.86 (95 % CI 1.22-2.82), and had the highest Somers' D.

CONCLUSIONS

The PSHS scoring system performed well in assessing the effect of image quality on detection rates, as did the PI-QUAL system. Since both systems focus on different aspects of image quality, their incorporation into prostate MRI reports could further enhance standardization and allow for a reliable assessment of image quality as a potential confounder in prostate MRI.

Landmarks in the evolution of prostate biopsy

Approaches and techniques used for diagnostic prostate biopsy have undergone considerable evolution over the past few decades: from the original finger-guided techniques to the latest MRI-directed strategies, from aspiration cytology to tissue core sampling, and from transrectal to transperineal approaches. In particular, increased adoption of transperineal biopsy approaches have led to reduced infectious complications and improved antibiotic stewardship. Furthermore, as image fusion has become integral, these novel techniques could be incorporated into prostate biopsy methods in the future, enabling 3D-ultrasonography fusion reconstruction, molecular targeting based on PET imaging and autonomous robotic-assisted biopsy.

Beyond Multiparametric MRI and towards Radiomics to Detect Prostate Cancer: A Machine Learning Model to Predict Clinically Significant Lesions

The risk of misclassifying clinically significant prostate cancer (csPCa) by multiparametric magnetic resonance imaging is consistent, also using the updated PIRADS score and although different definitions of csPCa, patients with Gleason Grade group (GG) ≥ 3 have a significantly worse prognosis. This study aims to develop a machine learning model predicting csPCa (i.e., any GG ≥ 3 lesion at target biopsy) by mpMRI radiomic features and analyzing similarities between GG groups. One hundred and two patients with 117 PIRADS ≥ 3 lesions at mpMRI underwent target+systematic biopsy, providing histologic diagnosis of PCa, 61 GG < 3 and 56 GG ≥ 3. Features were generated locally from an apparent diffusion coefficient and selected, using the LASSO method and Wilcoxon rank-sum test (p < 0.001), to achieve only four features. After data augmentation, the features were exploited to train a support vector machine classifier, subsequently validated on a test set. To assess the results, Kruskal−Wallis and Wilcoxon rank-sum tests (p < 0.001) and receiver operating characteristic (ROC)-related metrics were used. GG1 and GG2 were equivalent (p = 0.26), whilst clear separations between either GG[1,2] and GG ≥ 3 exist (p < 10−6). On the test set, the area under the curve = 0.88 (95% CI, 0.68−0.94), with positive and negative predictive values being 84%. The features retain a histological interpretation. Our model hints at GG2 being much more similar to GG1 than GG ≥ 3.

Role of the elastography strain ratio using transrectal ultrasonography in the diagnosis of prostate cancer and clinically significant prostate cancer

This study investigated the efficacy of the elastography strain ratio (ESR) as a predictor of prostate cancer (PCa) in targeted prostate biopsy. In total, 257 patients who underwent magnetic resonance imaging-targeted biopsy were enrolled. Before biopsy, we placed regions of interest (zone A and B) in the lesion and levator ani. The ESR was measured as zone A/zone B. Multivariate analyses were performed to predict PCa and clinically significant PCa. There were 206 (71.5%) positive cancer lesions. No difference in digit rectal examination findings was found between patients with and without PCa. For predicting clinically significant PCa, an ESR ≥ 6.8 was significantly higher in the PCa (+) group than in the PCa (-) group (p < 0.001). The area under the receiver operating characteristic curve (AUC) for the conventional variables (model 1) plus the ESR was 0.845, which was significantly higher than that for model 1 (p = 0.001). In prostate imaging reporting and data system score 3 lesions, an ESR ≥ 4.6 was a significant predictor of PCa (p = 0.002). The AUC in model 1 plus the ESR was 0.856, which was significantly higher than that in model 1 alone (p = 0.017). The ESR is useful for predicting clinically significant PCa.

Self-activated arsenic manganite nanohybrids for visible and synergistic thermo/immuno-arsenotherapy

Arsenotherapy has been clinically exploited to treat a few types of solid tumors despite of acute promyelocytic leukemia using arsenic trioxide (ATO), however, its efficacy is hampered by inadequate delivery of ATO into solid tumors owing to the absence of efficient and biodegradable vehicles. Precise spatiotemporal control of subcellular ATO delivery for potent arsenotherapy thus remains challengeable. Herein, we report the self-activated arsenic manganite nanohybrids for high-contrast magnetic resonance imaging (MRI) and arsenotherapeutic synergy on triple-negative breast cancer (TNBC). The nanohybrids, composed of arsenic‑manganese-co-biomineralized nanoparticles inside albumin nanocages (As/Mn-NHs), switch signal-silent background to high proton relaxivity, and simultaneously afford remarkable subcellular ATO level in acidic and glutathione environments, together with reduced ATO resistance against tumor cells. Then, the nanohybrids enable in vivo high-contrast T₁-weighted MRI signals in various tumor models for delineating tumor boundary, and simultaneously yield efficient arsenotherapeutic efficacy through multiple apoptotic pathways for potently suppressing subcutaneous and orthotopic breast models. As/Mn-NHs exhibited the maximum tumor-to-normal tissue (T/N) contrast ratio of 205% and tumor growth inhibition rate of 88% at subcutaneous 4T1 tumors. These nanohybrids further yield preferable synergistic antitumor efficacy against both primary and metastatic breast tumors upon combination with concurrent thermotherapy. More importantly, As/Mn-NHs considerably induce immunogenic cell death (ICD) effect to activate the immunogenically "cold" tumor microenvironment into "hot" one, thus synergizing with immune checkpoint blockade to yield the strongest tumor inhibition and negligible metastatic foci in the lung. Our study offers the insight into clinically potential arsenotherapeutic nanomedicine for potent therapy against solid tumors.

Multiparametric ultrasound of prostate: role in prostate cancer diagnosis

Recent advances in ultrasonography (US) technology established modalities, such as Doppler-US, HistoScanning, contrast-enhanced ultrasonography (CEUS), elastography, and micro-ultrasound. The early results of these US modalities have been promising, although there are limitations including the need for specialized equipment, inconsistent results, lack of standardizations, and external validation. In this review, we identified studies evaluating multiparametric ultrasonography (mpUS), the combination of multiple US modalities, for prostate cancer (PCa) diagnosis. In the past 5 years, a growing number of studies have shown that use of mpUS resulted in high PCa and clinically significant prostate cancer (CSPCa) detection performance using radical prostatectomy histology as the reference standard. Recent studies have demonstrated the role mpUS in improving detection of CSPCa and guidance for prostate biopsy and therapy. Furthermore, some aspects including lower costs, real-time imaging, applicability for some patients who have contraindication for magnetic resonance imaging (MRI) and availability in the office setting are clear advantages of mpUS. Interobserver agreement of mpUS was overall low; however, this limitation can be improved using standardized and objective evaluation systems such as the machine learning model. Whether mpUS outperforms MRI is unclear. Multicenter randomized controlled trials directly comparing mpUS and multiparametric MRI are warranted.