Predicting the Need for Biopsy to Detect Clinically Significant Prostate Cancer in Patients with a Magnetic Resonance Imaging-detected Prostate Imaging Reporting and Data System/Likert ≥3 Lesion: Development and Multinational External Validation of the Imperial Rapid Access to Prostate Imaging and Diagnosis Risk Score

The Role of Dynamic Contrast Enhanced Magnetic Resonance Imaging in Evaluating Prostate Adenocarcinoma: A Partially-Blinded Retrospective Study of a Prostatectomy Patient Cohort With Whole Gland Histopathology Correlation and Application of PI-RADS or TNM Staging

BACKGROUND

Dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) in the current Prostate Imaging-Reporting and Data System version 2.1 (PI-RADS v2.1) is considered optional, with primary scoring based on T2-weighted imaging (T2WI) and diffusion weighted imaging (DWI). Our study is designed to assess the relative contribution of DCE MRI in a patient-cohort with whole mount prostate histopathology and spatially-mapped prostate adenocarcinoma (PCa) for reference.

METHODS

We performed a partially-blinded retrospective review of 47 prostatectomy patients with recent multi-parametric MRI (mpMRI). Scans included T2WI, DWI with apparent diffusion coefficient (ADC) mapping, and DCE imaging. Lesion conspicuity was scored on a 10-point scale with ≥ 6 considered "positive," and image quality was assessed on a 4-point scale for each sequence. The diagnostic contribution of DCE images was evaluated on a 4-point scale. The mpMRI studies were assigned PI-RADS scores and tumor, node, metastasis (TNM) T-stage with blinded comparison to spatially-mapped whole-mount pathology. Results were compared to the prospective clinical reports, which used standardized PI-RADS templates that emphasize T2WI, DWI and ADC.

RESULTS

Per lesion sensitivity for PCa was 93.5%, 82.6%, 63.0%, and 58.7% on T2WI, DCE, ADC and DWI, respectively. Mean lesion conspicuity was 8.5, 7.9, 6.2, and 6.1, on T2W, DCE, ADC and DWI, respectively. The higher values on T2WI and DCE imaging were not significantly different from each other but were both significantly different from DWI and ADC (p < 0.001). DCE scans were determined to have a marked diagnostic contribution in 83% of patients, with the most common diagnostic yield being detection of contralateral peripheral zone tumor or delineating presence/absence of extra-prostatic extension (EPE), contributing to more accurate PCa staging by PI-RADS or TNM, as compared to histopathology.

CONCLUSION

We demonstrate that DCE may contribute to lesion detection and local staging as compared to T2WI plus DWI-ADC alone and that lesion conspicuity using DCE is markedly improved as compared to DWI-ADC. These findings support modification of PI-RADS v2.1 to include use of DCE acquisitions and that a TNM staging is feasible on mpMRI as compared to surgical pathology.

Natural history of histologically benign PIRADS 4-5 lesions in multiparametric MRI: Real-life experience in an academic center

INTRODUCTION

The follow-up findings of patients who underwent prostate biopsy for prostate image reporting and data system (PIRADS) 4 or 5 multiparametric magnetic resonance imaging (mpMRI) findings and had benign histology were retrospectively reviewed.

METHODS

There were 190 biopsy-naive patients. Patients with at least 12 months of follow-up between 2012 and 2023 were evaluated. All MRIs were interpreted by two very experienced uroradiologists. Of the patients, 125 had either cognitive or software fusion MR-targeted biopsies with 4 + 8/10 cores. The remaining 65 patients had in-bore biopsies with 4-5 cores. Prostate-specific antigen (PSA) levels below 4 ng/mL were defined as PSA regression following biopsy. PIRADS 1-3 lesions on new MRI images were classified as MRI regression.

RESULTS

Median patient age and PSA were 62 (39-82) years and six (0.4-33) ng/mL, respectively, at the initial work-up. During a median follow-up period of 44 months, 37 (19.4%) patients were lost to follow-up. Of the remaining 153 patients, 82 (53.6%) had persistently high PSA. Among them, 72 (87.8%) had repeat mpMRI within 6-24 months which showed regressive findings (PIRADS 1-3) in 53 patients (73.6%) and PIRADS 4-5 index lesion persistence in 19 cases (26.4%). The latter group was recommended to have rebiopsy. Of these 19 patients, 16 underwent MRI-targeted rebiopsy. Prostate cancer was diagnosed in six (37.5%) patients and of these four (25%) were clinically significant (>Grade Group 1). Totally, clinically significant prostate cancer was detected in 4/153 (2.6%) patients followed up.

CONCLUSION

Patients should be warned against the relative relaxing effect of a negative biopsy after identification of PIRADS 4-5 index lesion. While PSA decrease was observed in many patients during follow-up, persistent MRI findings were present in nearly a quarter of patients with persistently high PSA. A rebiopsy is warranted in these patients, with significant prostate cancer diagnosed in a quarter of patients with rebiopsy.

Effects of the lesion size on clinically significant prostate cancer detection rates in PI-RADS category 3-5 lesions

INTRODUCTION

Prostate cancer (PCa) ranks second among prevalent cancers in men, necessitating effective screening tools such as multiparametric magnetic resonance imaging (mpMRI) with the prostate imaging reporting and data system (PI-RADS) classification. This study explores the impact of lesion volume on clinically significant prostate cancer (csPCa) detection rates in PI-RADS 3-5 lesions, aiming to contribute insights into the underexplored relationship between lesion size and csPCa detection.

MATERIALS AND METHODS

A retrospective analysis was conducted on data from 754 patients undergoing mpMRI-guided transrectal ultrasound (TRUS) prostate biopsy between January 2016 and 2023. Patients with PI-RADS 3, 4, and 5 lesions were included. Lesion size and PI-RADS categories were assessed through mpMRI, followed by MR fusion biopsy.

RESULTS

Of the patients, 33.7%, 52.3%, and 14.1% had PI-RADS 3, 4, and 5 lesions, respectively. Lesion sizes correlated significantly with csPCa detection in PI-RADS 4 and 5 categories. For PI-RADS 3 lesions, no significant differences in csPCa rates were observed based on lesion size. However, in PI-RADS 4 and 5 groups, larger lesions showed higher csPCa rates.

CONCLUSION

This study suggests that subgroup categorizations based on lesion volume could predict clinically significant PCa with high accuracy, potentially reducing unnecessary biopsies and associated overtreatment. Future research should further explore the relationship between lesion size and csPCa, clarifying discussions regarding the inclusion of systematic biopsies in diagnostic protocols.

Development and validation of a novel nomogram to avoid unnecessary biopsy in patients with PI-RADS category ≥ 4 lesions and PSA ≤ 20 ng/ml

OBJECTIVES

To develop and validate a prediction model for identifying non-prostate cancer (non-PCa) in biopsy-naive patients with PI-RADS category ≥ 4 lesions and PSA ≤ 20 ng/ml to avoid unnecessary biopsy.

PATIENTS AND METHODS

Eligible patients who underwent transperineal biopsies at West China Hospital between 2018 and 2022 were included. The patients were randomly divided into training cohort (70%) and validation cohort (30%). Logistic regression was used to screen for independent predictors of non-PCa, and a nomogram was constructed based on the regression coefficients. The discrimination and calibration were assessed by the C-index and calibration plots, respectively. Decision curve analysis (DCA) and clinical impact curves (CIC) were applied to measure the clinical net benefit.

RESULTS

A total of 1580 patients were included, with 634 non-PCa. Age, prostate volume, prostate-specific antigen density (PSAD), apparent diffusion coefficient (ADC) and lesion zone were independent predictors incorporated into the optimal prediction model, and a corresponding nomogram was constructed ( https://nomogramscu.shinyapps.io/PI-RADS-4-5/ ). The model achieved a C-index of 0.931 (95% CI, 0.910-0.953) in the validation cohort. The DCA and CIC demonstrated an increased net benefit over a wide range of threshold probabilities. At biopsy-free thresholds of 60%, 70%, and 80%, the nomogram was able to avoid 74.0%, 65.8%, and 55.6% of unnecessary biopsies against 9.0%, 5.0%, and 3.6% of missed PCa (or 35.9%, 30.2% and 25.1% of foregone biopsies, respectively).

CONCLUSION

The developed nomogram has favorable predictive capability and clinical utility can help identify non-PCa to support clinical decision-making and reduce unnecessary prostate biopsies.

Repeat Prostate-specific Antigen Testing Improves Risk-based Selection of Men for Prostate Biopsy After Magnetic Resonance Imaging

Background and objective

The aim of our study was to investigate whether repeat prostate-specific antigen (PSA) testing as currently recommended improves risk stratification for men undergoing magnetic resonance imaging (MRI) and targeted biopsy for suspected prostate cancer (PCa).

Methods

Consecutive men undergoing MRI and prostate biopsy who had at least two PSA tests before prostate biopsy were retrospectively registered and assigned to a development cohort (n = 427) or a validation (n = 174) cohort. Change in PSA level was assessed as a predictor of clinically significant PCa (csPCa; Gleason score ≥3 + 4, grade group ≥2) by multivariable logistic regression analysis. We developed a multivariable prediction model (MRI-RC) and a dichotomous biopsy decision strategy incorporating the PSA change. The performance of the MRI-RC model and dichotomous decision strategy was assessed in the validation cohort and compared to prediction models and decision strategies not including PSA change in terms of discriminative ability and decision curve analysis.

Results

Men who had a decrease on repeat PSA testing had significantly lower risk of csPCa than men without a decrease (odds ratio [OR] 0.3, 95% confidence interval [CI] 0.16-0.54; p < 0.001). Men with an increased repeat PSA had a significantly higher risk of csPCa than men without an increase (OR 2.97, 95% CI 1.62-5.45; p < 0.001). Risk stratification using both the MRI-RC model and the dichotomous decision strategy was improved by incorporating change in PSA as a parameter.

Conclusions and clinical implications

Repeat PSA testing gives predictive information regarding men undergoing MRI and targeted prostate biopsy. Inclusion of PSA change as a parameter in an MRI-RC model and a dichotomous biopsy decision strategy improves their predictive performance and clinical utility without requiring additional investigations.

Patient summary

For men with a suspicion of prostate cancer, repeat PSA (prostate-specific antigen) testing after an MRI (magnetic resonance imaging) scan can help in identifying patients who can safely avoid prostate biopsy.

Risk calculators for the detection of prostate cancer: a systematic review

BACKGROUND

Prostate cancer (PCa) (early) detection poses significant challenges, including unnecessary testing and the risk of potential overdiagnosis. The European Association of Urology therefore suggests an individual risk-adapted approach, incorporating risk calculators (RCs) into the PCa detection pathway. In the context of 'The PRostate Cancer Awareness and Initiative for Screening in the European Union' (PRAISE-U) project ( https://uroweb.org/praise-u ), we aim to provide an overview of the currently available clinical RCs applicable in an early PCa detection algorithm.

METHODS

We performed a systematic review to identify RCs predicting detection of clinically significant PCa at biopsy. A search was performed in the databases Medline ALL, Embase, Web of Science Core Collection, Cochrane Central Register of Controlled Trials and Google Scholar for publications between January 2010 and July 2023. We retrieved relevant literature by using the terms "prostate cancer", "screening/diagnosis" and "predictive model". Inclusion criteria included systematic reviews, meta-analyses, and clinical trials. Exclusion criteria applied to studies involving pre-targeted high-risk populations, diagnosed PCa patients, or a sample sizes under 50 men.

RESULTS

We identified 6474 articles, of which 140 were included after screening abstracts and full texts. In total, we identified 96 unique RCs. Among these, 45 underwent external validation, with 28 validated in multiple cohorts. Of the externally validated RCs, 17 are based on clinical factors, 19 incorporate clinical factors along with MRI details, 4 were based on blood biomarkers alone or in combination with clinical factors, and 5 included urinary biomarkers. The median AUC of externally validated RCs ranged from 0.63 to 0.93.

CONCLUSIONS

This systematic review offers an extensive analysis of currently available RCs, their variable utilization, and performance within validation cohorts. RCs have consistently demonstrated their capacity to mitigate the limitations associated with early detection and have been integrated into modern practice and screening trials. Nevertheless, the lack of external validation data raises concerns about numerous RCs, and it is crucial to factor in this omission when evaluating whether a specific RC is applicable to one's target population.

Risk factor analysis and optimal cutoff value selection of PSAD for diagnosing clinically significant prostate cancer in patients with negative mpMRI: results from a high-volume center in Southeast China

BACKGROUND

Multi-parametric magnetic resonance imaging (mpMRI) is a diagnostic tool used for screening, localizing, and staging prostate cancer. Patients with Prostate Imaging Reporting and Data System (PI-RADS) score of 1 and 2 are considered negative mpMRI, with a lower likelihood of detecting clinically significant prostate cancer (csPCa). However, relying solely on mpMRI is insufficient to completely exclude csPCa, necessitating further stratification of csPCa patients using biomarkers.

METHODS

A retrospective study was conducted on mpMRI-negative patients who underwent prostate biopsy at the First Affiliated Hospital of Zhejiang University from January 2022 to June 2023. A total of 607 patients were included based on inclusion and exclusion criteria. Univariate and multivariate logistic regression analysis were performed to identify risk factors for diagnosing csPCa in patients with negative mpMRI. Receiver Operating Characteristic (ROC) curves were plotted to compare the discriminatory ability of different Prostate-Specific Antigen Density (PSAD) cutoff values for csPCa.

RESULTS

Among the 607 patients with negative mpMRI, 73 patients were diagnosed with csPCa. In univariate logistic regression analysis, age, PSA, f/tPSA, prostate volume, and PSAD were all associated with diagnosing csPCa in patients with negative mpMRI (P < 0.05), with PSAD being the most accurate predictor. In multivariate logistic regression analysis, f/tPSA, age, and PSAD were independent predictors of csPCa (P < 0.05). PSAD cutoff value of 0.20 ng/ml/ml has better discriminatory ability for predicting csPCa and is a significant risk factor for csPCa in multivariate analysis.

CONCLUSION

Age, f/tPSA, and PSAD are independent predictors of diagnosing csPCa in patients with negative mpMRI. It is suggested that patients with negative mpMRI and PSAD less than 0.20 ng/ml/ml could avoid prostate biopsy, as a PSAD cutoff value of 0.20 ng/ml/ml has better diagnostic performance than the traditional cutoff value of 0.15 ng/ml/ml.

Risk score model to automatically detect prostate cancer patients by integrating diagnostic parameters

Introduction

Prostate cancer (PCa) is one of the prevailing forms of cancer among men. At present, multiparametric MRI is the imaging method for localizing tumors and staging cancer. Radiomics plays a key role and hold potential for PCa detection, reducing the need for unnecessary biopsies, characterizing tumor aggression, and overseeing PCa recurrence post-treatment.

Methods

Furthermore, the integration of radiomics data with clinical and histopathological data can further enhance the understanding and management of PCa and decrease unnecessary transfers to specialized care for expensive and intrusive biopsies. Therefore, the aim of this study is to develop a risk model score to automatically detect PCa patients by integrating non-invasive diagnostic parameters (radiomics and Prostate-Specific Antigen levels) along with patient's age.

Results

The proposed approach was evaluated using a dataset of 189 PCa patients who underwent bi-parametric MRI from two centers. Elastic-Net Regularized Generalized Linear Model achieved 91% AUC to automatically detect PCa patients. The model risk score was also used to assess doubt cases of PCa at biopsy and then compared to bi-parametric PI-RADS v2.

Discussion

This study explored the relative utility of a well-developed risk model by combining radiomics, Prostate-Specific Antigen levels and age for objective and accurate PCa risk stratification and supporting the process of making clinical decisions during follow up.

Performance of magnetic resonance imaging-based prostate cancer risk calculators and decision strategies in two large European medical centres

OBJECTIVES

To compare the performance of currently available biopsy decision support tools incorporating magnetic resonance imaging (MRI) findings in predicting clinically significant prostate cancer (csPCa).

PATIENTS AND METHODS

We retrospectively included men who underwent prostate MRI and subsequent targeted and/or systematic prostate biopsies in two large European centres. Available decision support tools were identified by a PubMed search. Performance was assessed by calibration, discrimination, decision curve analysis (DCA) and numbers of biopsies avoided vs csPCa cases missed, before and after recalibration, at risk thresholds of 5%-20%.

RESULTS

A total of 940 men were included, 507 (54%) had csPCa. The median (interquartile range) age, prostate-specific antigen (PSA) level, and PSA density (PSAD) were 68 (63-72) years, 9 (7-15) ng/mL, and 0.20 (0.13-0.32) ng/mL2 , respectively. In all, 18 multivariable risk calculators (MRI-RCs) and dichotomous biopsy decision strategies based on MRI findings and PSAD thresholds were assessed. The Van Leeuwen model and the Rotterdam Prostate Cancer Risk Calculator (RPCRC) had the best discriminative ability (area under the receiver operating characteristic curve 0.86) of the MRI-RCs that could be assessed in the whole cohort. DCA showed the highest clinical utility for the Van Leeuwen model, followed by the RPCRC. At the 10% threshold the Van Leeuwen model would avoid 22% of biopsies, missing 1.8% of csPCa, whilst the RPCRC would avoid 20% of biopsies, missing 2.6% of csPCas. These multivariable models outperformed all dichotomous decision strategies based only on MRI-findings and PSAD.

CONCLUSIONS

Even in this high-risk cohort, biopsy decision support tools would avoid many prostate biopsies, whilst missing very few csPCa cases. The Van Leeuwen model had the highest clinical utility, followed by the RPCRC. These multivariable MRI-RCs outperformed and should be favoured over decision strategies based only on MRI and PSAD.

Developing a diagnostic model for predicting prostate cancer: a retrospective study based on Chinese multicenter clinical data

The overdiagnosis of prostate cancer (PCa) caused by nonspecific elevation serum prostate-specific antigen (PSA) and the overtreatment of indolent PCa have become a global problem that needs to be solved urgently. We aimed to construct a prediction model and provide a risk stratification system to reduce unnecessary biopsies. In this retrospective study, clinical data of 1807 patients from three Chinese hospitals were used. The final model was built using stepwise logistic regression analysis. The apparent performance of the model was assessed by receiver operating characteristic curves, calibration plots, and decision curve analysis. Finally, a risk stratification system of clinically significant prostate cancer (csPCa) was created, and diagnosis-free survival analyses were performed. Following multivariable screening and evaluation of the diagnostic performances, a final diagnostic model comprised of the PSA density and Prostate Imaging-Reporting and Data System (PI-RADS) score was established. Model validation in the development cohort and two external cohorts showed excellent discrimination and calibration. Finally, we created a risk stratification system using risk thresholds of 0.05 and 0.60 as the cut-off values. The follow-up results indicated that the diagnosis-free survival rate for csPCa at 12 months and 24 months postoperatively was 99.7% and 99.4%, respectively, for patients with a risk threshold below 0.05 after the initial negative prostate biopsy, which was significantly better than patients with higher risk. Our diagnostic model and risk stratification system can achieve a personalized risk calculation of csPCa. It provides a standardized tool for Chinese patients and physicians when considering the necessity of prostate biopsy.

Is Likert better than PI-RADS at predicting prostate cancer on MRI and can a mathematical algorithm achieve similar results?

BACKGROUND

Prostate Imaging Reporting & Data System (PI-RADS) is an internationally recognized system to quantify risk of prostate cancer on magnetic resonance imaging (MRI). However, studies have suggested methods to improve predictive accuracy.

PURPOSE

To assess two different methods that aim to improve the accuracy of PI-RADS scores: a subjective Likert score given by experienced reporters, and an objective Calculated Adjustment of PI-RADS Equivocal Score (CAPES).

MATERIAL AND METHODS

Five experienced reporters in a quaternary referral unit used a standardized reporting template to prospectively collect PI-RADS and Likert scores for 1467 multiparametric MRI (mpMRI) scans between January 2021 and June 2022. Histology results were recorded for patients who underwent trans-perineal biopsy. The CAPES tool was retrospectively applied to the cases scoring PI-RADS 3. A theoretical standardized biopsy protocol (assuming all patients scoring ≥3 were referred for biopsy) was used to compare the three scoring systems for sensitivity, specificity, and positive predictive value (PPV).

RESULTS

Across all reporters, significantly fewer equivocal "3" scores were given using Likert (15.7%) or CAPES (2.2%) compared to PI-RADS (24.1%). Assuming a protocol where all patients scoring ≥3 were biopsied, Likert had a higher specificity (69.0% vs. 54.4%), sensitivity (98.3% vs. 97.7%), and PPV (49.9% vs. 40.3%) than PI-RADS for identifying ISUP ≥2 cancer. The CAPES tool had an even higher specificity (81.4%) and PPV (61.2%) with only a slightly lower sensitivity (93.4%) resulting in 37.1% (n = 316) fewer biopsies than PI-RADS, and 22.4% (n = 155) fewer biopsies than Likert across 1467 patients.

CONCLUSIONS

Compared to PI-RADS scoring, Likert scoring or CAPES can result in fewer equivocal scores, greater PPV, and fewer unnecessary biopsies.

Multivariable stratification of PI-RADS version 2.1 categories for the risk of false-positive target biopsy: Impact on prostate biopsy decisions

PURPOSE

To identify clinical and multiparametric magnetic resonance imaging (mpMRI) factors predicting false positive target biopsy (FP-TB) of prostate imaging reporting and data system version 2.1 (PI-RADSv2.1) ≥ 3 findings.

METHOD

We retrospectively included 221 men with and without previous negative prostate biopsy who underwent 3.0 T/1.5 T mpMRI for suspicious clinically significant prostate cancer (csPCa) between April 2019-July 2021. A study coordinator revised mpMRI reports provided by one of two radiologists (experience of > 1500/>500 mpMRI examinations, respectively) and matched them with the results of transperineal systematic biopsy plus fusion target biopsy (TB) of PI-RADSv2.1 ≥ 3 lesions or PI-RADSv2.1 ≤ 2 men with higher clinical risk. A multivariable model was built to identify features predicting FP-TB of index lesions, defined as the absence of csPCa (International Society of Urogenital Pathology [ISUP] ≥ 2). The model was internally validated with the bootstrap technique, receiving operating characteristics (ROC) analysis, and decision analysis.

RESULTS

Features significantly associated with FP-TB were age < 65 years (odds ratio [OR] 2.77), prostate-specific antigen density (PSAD) < 0.15 ng/mL/mL (OR 2.45), PI-RADS 4/5 category vs. category 3 (OR 0.15/0.07), and multifocality (OR 0.46), with a 0.815 area under the curve (AUC) in assessing FP-TB. When adjusting PI-RADSv2.1 categorization for the model, mpMRI showed 87.5% sensitivity and 79.9% specificity for csPCa, with a greater net benefit in triggering biopsy compared to unadjusted categorization or adjustment for PSAD only at decision analysis, from threshold probability ≥ 15%.

CONCLUSION

Adjusting PI-RADSv2.1 categories for a multivariable risk of FP-TB is potentially more effective in triggering TB of index lesions than unadjusted PI-RADS categorization or adjustment for PSAD alone.

Nomograms for Predicting the Risk and Prognosis of Liver Metastases in Pancreatic Cancer: A Population-Based Analysis

The liver is the most prevalent location of distant metastasis for pancreatic cancer (PC), which is highly aggressive. Pancreatic cancer with liver metastases (PCLM) patients have a poor prognosis. Furthermore, there is a lack of effective predictive tools for anticipating the diagnostic and prognostic techniques that are needed for the PCLM patients in current clinical work. Therefore, we aimed to construct two nomogram predictive models incorporating common clinical indicators to anticipate the risk factors and prognosis for PCLM patients. Clinicopathological information on pancreatic cancer that referred to patients who had been diagnosed between the years of 2004 and 2015 was extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistic regression analyses and a Cox regression analysis were utilized to recognize the independent risk variables and independent predictive factors for the PCLM patients, respectively. Using the independent risk as well as prognostic factors derived from the multivariate regression analysis, we constructed two novel nomogram models for predicting the risk and prognosis of PCLM patients. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve, the consistency index (C-index), and the calibration curve were then utilized to establish the accuracy of the nomograms’ predictions and their discriminability between groups. Using a decision curve analysis (DCA), the clinical values of the two predictors were examined. Finally, we utilized Kaplan–Meier curves to examine the effects of different factors on the prognostic overall survival (OS). As many as 1898 PCLM patients were screened. The patient’s sex, primary site, histopathological type, grade, T stage, N stage, bone metastases, lung metastases, tumor size, surgical resection, radiotherapy, and chemotherapy were all found to be independent risks variables for PCLM in a multivariate logistic regression analysis. Using a multivariate Cox regression analysis, we discovered that age, histopathological type, grade, bone metastasis, lung metastasis, tumor size, and surgery were all independent prognostic variables for PCLM. According to these factors, two nomogram models were developed to anticipate the prognostic OS as well as the risk variables for the progression of PCLM in PCLM patients, and a web-based version of the prediction model was constructed. The diagnostic nomogram model had a C-index of 0.884 (95% CI: 0.876–0.892); the prognostic model had a C-index of 0.686 (95% CI: 0.648–0.722) in the training cohort and a C-index of 0.705 (95% CI: 0.647–0.758) in the validation cohort. Subsequent AUC, calibration curve, and DCA analyses revealed that the risk and predictive model of PCLM had high accuracy as well as efficacy for clinical application. The nomograms constructed can effectively predict risk and prognosis factors in PCLM patients, which facilitates personalized clinical decision-making for patients.

Foggia Prostate Cancer Risk Calculator 2.0: A Novel Risk Calculator including MRI and Bladder Outlet Obstruction Parameters to Reduce Unnecessary Biopsies

Risk calculator (RC) combining PSA with other clinical information can help to better select patients at risk of prostate cancer (PCa) for prostate biopsy. The present study aimed to develop a new Pca RC, including MRI and bladder outlet obstruction parameters (BOOP). The ability of these parameters in predicting PCa and clinically significant PCa (csPCa: ISUP GG ≥ 2) was assessed by binary logistic regression. A total of 728 patients were included from two institutions. Of these, 395 (54.3%) had negative biopsies and 161 (22.11%) and 172 (23.6%) had a diagnosis of ISUP GG1 PCa and csPCa. The two RC ultimately included age, PSA, DRE, prostate volume (pVol), post-voided residual urinary volume (PVR), and PIRADS score. Regarding BOOP, higher prostate volumes (csPCa: OR 0.98, CI 0.97,0.99) and PVR ≥ 50 mL (csPCa: OR 0.27, CI 0.15, 0.47) were protective factors for the diagnosis of any PCa and csPCa. AUCs after internal validation were 0.78 (0.75, 0.82) and 0.82 (0.79, 0.86), respectively. Finally, decision curves analysis demonstrated higher benefit compared to the first-generation calculator and MRI alone. These novel RC based on MRI and BOOP may help to better select patient for prostate biopsy after 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.

Development and Validation of a Prognostic Nomogram for Lung Adenocarcinoma: A Population-Based Study

PURPOSE

To establish an effective and accurate prognostic nomogram for lung adenocarcinoma (LUAD). Patients and Methods. 62,355 LUAD patients from 1975 to 2016 enrolled in the Surveillance, Epidemiology, and End Results (SEER) database were randomly and equally divided into the training cohort (n = 31,179) and the validation cohort (n = 31,176). Univariate and multivariate Cox regression analyses screened the predictive effects of each variable on survival. The concordance index (C-index), calibration curves, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC) were used to examine and validate the predictive accuracy of the nomogram. Kaplan-Meier curves were used to estimate overall survival (OS).

RESULTS

10 prognostic factors associated with OS were identified, including age, sex, race, marital status, American Joint Committee on Cancer (AJCC) TNM stage, tumor size, grade, and primary site. A nomogram was established based on these results. C-indexes of the nomogram model reached 0.777 (95% confidence interval (CI), 0.773 to 0.781) and 0.779 (95% CI, 0.775 to 0.783) in the training and validation cohorts, respectively. The calibration curves were well-fitted for both cohorts. The AUC for the 3- and 5-year OS presented great prognostic accuracy in the training cohort (AUC = 0.832 and 0.827, respectively) and validation cohort (AUC = 0.835 and 0.828, respectively). The Kaplan-Meier curves presented significant differences in OS among the groups.

CONCLUSION

The nomogram allows accurate and comprehensive prognostic prediction for patients with LUAD.