Development and Validation of a Clinical Prognostic Stage Group System for Nonmetastatic Prostate Cancer Using Disease-Specific Mortality Results From the International Staging Collaboration for Cancer of the Prostate

Refining Risk Stratification of High-risk and Locoregional Prostate Cancer: A Pooled Analysis of Randomized Trials

BACKGROUND AND OBJECTIVE

Radiotherapy (RT) and long-term androgen deprivation therapy (ltADT; 18-36 mo) is a standard of care in the treatment of high-risk localized/locoregional prostate cancer (HRLPC). We evaluated the outcomes in patients treated with RT + ltADT to identify which patients have poorer prognosis with standard therapy.

METHODS

Individual patient data from patients with HRLPC (as defined by any of the following three risk factors [RFs] in the context of cN0 disease-Gleason score ≥8, cT3-4, and prostate-specific antigen [PSA] >20 ng/ml, or cN1 disease) treated with RT and ltADT in randomized controlled trials collated by the Intermediate Clinical Endpoints in Cancer of the Prostate group. The outcome measures of interest were metastasis-free survival (MFS), overall survival (OS), time to metastasis, and prostate cancer-specific mortality. Multivariable Cox and Fine-Gray regression estimated hazard ratios (HRs) for the three RFs and cN1 disease.

KEY FINDINGS AND LIMITATIONS

A total of 3604 patients from ten trials were evaluated, with a median PSA value of 24 ng/ml. Gleason score ≥8 (MFS HR = 1.45; OS HR = 1.42), cN1 disease (MFS HR = 1.86; OS HR = 1.77), cT3-4 disease (MFS HR = 1.28; OS HR = 1.22), and PSA >20 ng/ml (MFS HR = 1.30; OS HR = 1.21) were associated with poorer outcomes. Adjusted 5-yr MFS rates were 83% and 78%, and 10-yr MFS rates were 63% and 53% for patients with one and two to three RFs, respectively; corresponding 10-yr adjusted OS rates were 67% and 60%, respectively. In cN1 patients, adjusted 5- and 10-yr MFS rates were 67% and 36%, respectively, and 10-yr OS was 47%.

CONCLUSIONS AND CLINICAL IMPLICATIONS

HRLPC patients with two to three RFs (and cN0) or cN1 disease had the poorest outcomes on RT and ltADT. This will help in counseling patients treated in routine practice and in guiding adjuvant trials in HRLPC.

PATIENT SUMMARY

Radiotherapy and long-term hormone therapy are standard treatments for high-risk and locoregional prostate cancer. In this report, we defined prognostic groups within high-risk/locoregional prostate cancer and showed that outcomes to standard therapy are poorest in those with two or more "high-risk" factors or evidence of lymph node involvement. Such patients may therefore be the best candidates for intensification of treatment.

Model-agnostic explanations for survival prediction models

Advanced machine learning methods capable of capturing complex and nonlinear relationships can be used in biomedical research to accurately predict time-to-event outcomes. However, these methods have been criticized as "black boxes" that are not interpretable and thus are difficult to trust in making important clinical decisions. Explainable machine learning proposes the use of model-agnostic explainers that can be applied to predictions from any complex model. These explainers describe how a patient's characteristics are contributing to their prediction, and thus provide insight into how the model is arriving at that prediction. The specific application of these explainers to survival prediction models can be used to obtain explanations for (i) survival predictions at particular follow-up times, and (ii) a patient's overall predicted survival curve. Here, we present a model-agnostic approach for obtaining these explanations from any survival prediction model. We extend the local interpretable model-agnostic explainer framework for classification outcomes to survival prediction models. Using simulated data, we assess the performance of the proposed approaches under various settings. We illustrate application of the new methodology using prostate cancer data.

Prognostic nomogram to predict cancer-specific survival with small-cell carcinoma of the prostate: a multi-institutional study

Objective

The aim of this study is to examine the predictive factors for cancer-specific survival (CSS) in patients diagnosed with Small-Cell Carcinoma of the Prostate (SCCP) and to construct a prognostic model.

Methods

Cases were selected using the Surveillance, Epidemiology, and End Results (SEER) database. The Kaplan-Meier method was utilized to calculate survival rates, while Lasso and Cox regression were employed to analyze prognostic factors. An independent prognostic factor-based nomogram was created to forecast CSS at 12 and 24 months. The model's predictive efficacy was assessed using the consistency index (C-index), calibration curve, and decision curve analysis (DCA) in separate tests.

Results

Following the analysis of Cox and Lasso regression, age, race, Summary stage, and chemotherapy were determined to be significant risk factors (P < 0.05). In the group of participants who received training, the rate of 12-month CSS was 44.6%, the rate of 24-month CSS was 25.5%, and the median time for CSS was 10.5 months. The C-index for the training cohort was 0.7688 ± 0.024. As for the validation cohort, it was 0.661 ± 0.041. According to the nomogram, CSS was accurately predicted and demonstrated consistent and satisfactory predictive performance at both 12 months (87.3% compared to 71.2%) and 24 months (80.4% compared to 71.7%). As shown in the external validation calibration plot, the AUC for 12- and 24-month is 64.6% vs. 56.9% and 87.0% vs. 70.7%, respectively. Based on the calibration plot of the CSS nomogram at both the 12-month and 24-month marks, it can be observed that both the actual values and the nomogram predictions indicate a predominantly stable CSS. When compared to the AJCC staging system, DCA demonstrated a higher level of accuracy in predicting CSS through the use of a nomogram.

Conclusion

Clinical prognostic factors can be utilized with nomograms to forecast CSS in Small-Cell Carcinoma of the Prostate (SCCP).

Expert Perspectives on Controversies in Castration-Sensitive Prostate Cancer Management: Narrative Review and Report of the First US Prostate Cancer Conference Part 1

Purpose

Castration-sensitive prostate cancer (CSPC) is a complex and heterogeneous condition encompassing a range of clinical presentations. As new approaches have expanded management options, clinicians are left with myriad questions and controversies regarding the optimal individualized management of CSPC.

Materials and Methods

The US Prostate Cancer Conference (USPCC) multidisciplinary panel was assembled to address the challenges of prostate cancer management. The first annual USPCC meeting included experts in urology, medical oncology, radiation oncology, and nuclear medicine. USPCC co-chairs and session moderators identified key areas of controversy and uncertainty in prostate cancer management and organized the sessions with multidisciplinary presentations and discussion. Throughout the meeting, experts responded to questions prepared by chairs and moderators to identify areas of agreement and controversy.

Results

The USPCC panel discussion and question responses for CSPC-related topics are presented. Key advances in CSPC management endorsed by USPCC experts included the development and clinical utilization of gene expression classifiers and artificial intelligence (AI) models for risk stratification and treatment selection in specific patient populations, the use of advanced imaging modalities in patients with clinically localized unfavorable intermediate or high-risk disease and those with biochemical recurrence, recommendations of doublet or triplet therapy for metastatic CSPC (mCSPC), and consideration of prostate and/or metastasis-directed radiation therapy in select patients with mCSPC.

Conclusions

CSPC is a diverse disease with many therapeutic options and the potential for adverse outcomes associated with either undertreatment or overtreatment. Future studies are needed to validate and clinically integrate novel technologies, including genomics, AI, and advanced imaging, to optimize outcomes among patients with CSPC.

The Ability of the STAR-CAP Staging System to Prognosticate the Risk of Subsequent Therapies and Metastases After Initial Treatment of M0 Prostate Cancer

INTRODUCTION

The International Staging Collaboration for Prostate Cancer (STAR-CAP) has been proposed as a risk model for prostate cancer with superior prognostic power compared to the current staging system. This study aimed to evaluate the performance of STAR-CAP in predicting the risk of subsequent therapy after initial treatment and the risk of developing metastases.

PATIENTS AND METHODS

The study included 3425 men from an institutional observational registry with a median age of 64.9 years and a median follow-up time of 5.4 years. The primary endpoints were metastases and progression to additional therapy after initial therapy (radiation ± surgery). The risk of progression in the STAR-CAP group was estimated using a competing risk model (death).

RESULTS

The results showed that patients with STAR-CAP stages 1A-1C had a similar risk of requiring additional therapies and developing metastasis. Compared to stage IC, each stage from 2A to 3B incrementally increased the risk of subsequent therapy (hazard ratio (HR) 1.4-5.8, respectively) and metastases (HR 1.5-10.8, respectively). The 5-year probability of receiving subsequent therapy for a patient with stage IC was 8.6%, which increased from 11.4% to 37.4% for those with stages 2A to 3B. The 5-year probability of developing metastases for patients with stage IC was 1.5%, which increased from 2.2% to 8.2% for patients with stages 2A to 3B.

CONCLUSIONS

The probability of receiving subsequent therapy was higher for patients undergoing surgery, while radiation therapy patients were more likely to receive treatment with intensified multimodality therapies upfront.

Age 70 +/- 5 Years and Cancer-Specific Outcomes After Treatment of Localized Prostate Cancer: A Systematic Review

A secondary analysis of 2 randomized Radiation Therapy Oncology Group trials demonstrated that age ≥70 years was a favorable prognostic factor among men treated with external beam radiation therapy (EBRT). In contrast, several series based on men undergoing radical prostatectomy (RP) suggested that older age was an unfavorable prognostic factor. Our study was initiated to determine whether these observations reflect a true but paradoxical underlying age-related treatment-dependent biological phenomenon. We conducted a systematic review (PubMed, January 1, 1999-January 30, 2023) evaluating the effect of age on cancer-specific outcomes after definitive local treatment with either RP or EBRT. Our main objective was to assess possible interactions between age (using a cutoff of 70 +/- 5 years) and treatment type, with regard to adverse cancer-specific outcomes (eg, pathology, biochemical failure, distant metastasis, or prostate cancer-specific survival). Forty-five studies were selected for inclusion in this systematic review, including 30 and 15 studies with patients treated with RP and EBRT, respectively. Among patients treated with RP, 10 (50%) of these studies suggested that older age was associated with worse outcome(s) after RP. None suggested that age was a favorable prognostic factor after RP. Among the EBRT-based studies, 8 (53%) suggested that older age was associated with better outcomes, with an additional 3 studies (21%) trending to support a better outcome. None of these studies involving EBRT suggested that older age was an adverse prognostic factor. This systematic review suggests that age using a categorical cutoff of 70 +/- 5 years may be an adverse prognostic factor for men undergoing RP but a favorable prognostic factor for men treated with EBRT. Further research is needed to validate these findings.

Validation of the Combined Clinical Cell-Cycle Risk Score to Prognosticate Early Prostate Cancer Metastasis From Biopsy Specimens and Comparison With Other Routinely Used Risk Classifiers

PURPOSE

We aim to independently validate the prognostic utility of the combined cell-cycle risk (CCR) multimodality threshold to estimate risk of early metastasis after definitive treatment of prostate cancer and compare this prognostic ability with other validated biomarkers.

METHODS

Patients diagnosed with localized prostate cancer were enrolled into a single-institutional registry for the prospective observational cohort study. The primary end point was risk of metastasis within 3 years of diagnostic biopsy. Secondary end points included time to definitive treatment, time to subsequent therapy, and metastasis after completion of initial definitive treatment. Multivariable cause-specific Cox proportional hazards regression models were produced accounting for competing risk of death and stratified on the basis of the CCR active surveillance and multimodality (MM) thresholds. Time-dependent areas under the receiver operating characteristic curve were calculated.

RESULTS

The cohort consisted of 554 men with prostate cancer and available CCR score from biopsy. The CCR score was prognostic for metastasis (hazard ratio [HR], 2.32 [95% CI, 1.17 to 4.59]; P = .02), with scores above the MM threshold having a higher risk than those below the threshold (HR, 5.44 [95% CI, 2.72 to 10.91]; P < .001). The AUC for 3-year risk of metastasis on the basis of CCR was 0.736. When men with CCR above the MM threshold received MM therapy, their 3-year risk of metastasis was significantly lower than those receiving single-modality therapy (3% v 14%). Similarly, a CCR score above the active surveillance threshold portended a faster time to first definitive treatment.

CONCLUSION

CCR outperforms other commonly used biomarkers for prediction of early metastasis. We illustrate the clinical utility of the CCR active surveillance and multimodality thresholds. Molecular genomic tests can inform patient selection and personalization of treatment for localized prostate cancer.

International Society of Urological Pathology (ISUP) Gleason Grade Groups stratify outcomes in the CHHiP Phase 3 prostate radiotherapy trial

OBJECTIVES

To compare the results of Gleason Grade Group (GGG) classification following central pathology review with previous local pathology assessment, and to examine the difference between using overall and worst GGG in a large patient cohort treated with radiotherapy and short-course hormone therapy.

PATIENTS AND METHODS

Patients with low- to high-risk localized prostate cancer were randomized into the multicentre CHHiP fractionation trial between 2002 and 2011. Patients received short-course hormone therapy (≤6 month) and radical intensity-modulated radiotherapy (IMRT). Of 2749 consented patients, 1875 had adequate diagnostic biopsy tissue for blinded central pathology review. The median follow-up was 9.3 years. Agreement between local pathology and central pathology-derived GGG and between central pathology-derived overall and worst GGG was assessed using kappa (κ) statistics. Multivariate Cox regression and Kaplan-Meier methods were used to compare the biochemical/clinical failure (BCF) and distant metastases (DM) outcomes of patients with GGG 1-5.

RESULTS

There was poor agreement between local pathology- and central pathology-derived GGG (κ = 0.19) but good agreement between overall and worst GGG on central pathology review (κ = 0.89). Central pathology-derived GGG stratified BCF and DM outcomes better than local pathology, while overall and worst GGG on central pathology review performed similarly. GGG 3 segregated with GGG 4 for BCF, with BCF-free rates of 90%, 82%, 74%, 71% and 58% for GGGs 1-5, respectively, at 8 years when assessed using overall GGG. There was a progressive decrease in DM-free rates from 98%, 96%, 92%, 88% and 83% for GGGs 1-5, respectively, at 8 years with overall GGG. Patients (n = 57) who were upgraded from GGG 2-3 using worst GS had BCF-free and DM-free rates of 74% and 92% at 8 years. CHHiP eligibility criteria limit the interpretation of these results.

CONCLUSION

Contemporary review of International Society of Urological Pathology GGG successfully stratified patients treated with short-course hormone therapy and IMRT with regard to both BCF-free and DM-free outcomes. Patients upgraded from GGG 2 to GGG 3 using worst biopsy GS segregate with GGG 3 on long-term follow-up. We recommend that both overall and worst GS be used to derive GGG.

Adaption and National Validation of a Tool for Predicting Mortality from Other Causes Among Men with Nonmetastatic Prostate Cancer

BACKGROUND

An electronic health record-based tool could improve accuracy and eliminate bias in provider estimation of the risk of death from other causes among men with nonmetastatic cancer.

OBJECTIVE

To recalibrate and validate the Veterans Aging Cohort Study Charlson Comorbidity Index (VACS-CCI) to predict non-prostate cancer mortality (non-PCM) and to compare it with a tool predicting prostate cancer mortality (PCM).

DESIGN, SETTING, AND PARTICIPANTS

An observational cohort of men with biopsy-confirmed nonmetastatic prostate cancer, enrolled from 2001 to 2018 in the national US Veterans Health Administration (VA), was divided by the year of diagnosis into the development (2001-2006 and 2008-2018) and validation (2007) sets.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Mortality (all cause, non-PCM, and PCM) was evaluated. Accuracy was assessed using calibration curves and C statistic in the development, validation, and combined sets; overall; and by age (<65 and 65+ yr), race (White and Black), Hispanic ethnicity, and treatment groups.

RESULTS AND LIMITATIONS

Among 107 370 individuals, we observed 24 977 deaths (86% non-PCM). The median age was 65 yr, 4947 were Black, and 5010 were Hispanic. Compared with CCI and age alone (C statistic 0.67, 95% confidence interval [CI] 0.67-0.68), VACS-CCI demonstrated improved validated discrimination (C statistic 0.75, 95% CI 0.74-0.75 for non-PCM). The prostate cancer mortality tool also discriminated well in validation (C statistic 0.81, 95% CI 0.78-0.83). Both were well calibrated overall and within subgroups. Owing to missing data, 18 009/125 379 (14%) were excluded, and VACS-CCI should be validated outside the VA prior to outside application.

CONCLUSIONS

VACS-CCI is ready for implementation within the VA. Electronic health record-assisted calculation is feasible, improves accuracy over age and CCI alone, and could mitigate inaccuracy and bias in provider estimation.

PATIENT SUMMARY

Veterans Aging Cohort Study Charlson Comorbidity Index is ready for application within the Veterans Health Administration. Electronic health record-assisted calculation is feasible, improves accuracy over age and Charlson Comorbidity Index alone, and might help mitigate inaccuracy and bias in provider estimation of the risk of non-prostate cancer mortality.

Multiparametric MRI-based 5-year Risk Prediction Model for Biochemical Recurrence of Prostate Cancer after Radical Prostatectomy

Background Current predictive tools to estimate the risk of biochemical recurrence (BCR) after treatment of prostate cancer do not consider multiparametric MRI (mpMRI) information. Purpose To develop a risk prediction tool that considers mpMRI findings to assess the risk of 5-year BCR after radical prostatectomy. Materials and Methods In this retrospective single-center analysis in 1459 patients with prostate cancer who underwent mpMRI before radical prostatectomy (in 2012-2015), the outcome of interest was 5-year BCR (two consecutive prostate-specific antigen [PSA] levels > 0.2 ng/mL [0.2 µg/L]). Patients were randomly divided into training (70%) and test (30%) sets. Kaplan-Meier plots were applied to the training set to estimate survival probabilities. Multivariable Cox regression models were used to test the relationship between BCR and different sets of exploratory variables. The C-index of the final model was calculated for the training and test sets and was compared with European Association of Urology, University of California San Francisco Cancer of the Prostate Risk Assessment, Memorial Sloan-Kettering Cancer Center, and Partin risk tools using the partial likelihood ratio test. Five risk categories were created. Results The median duration of follow-up in the whole cohort was 59 months (IQR, 32-81 months); 376 of 1459 (25.8%) patients had BCR. A multivariable Cox regression model (referred to as PIPEN, and composed of PSA density, International Society of Urological Pathology grade group, Prostate Imaging Reporting and Data System category, European Society of Urogenital Radiology extraprostatic extension score, nodes) fitted to the training data yielded a C-index of 0.74, superior to that of other predictive tools (C-index 0.70 for all models; P ≤ .01) and a median higher C-index on 500 test set replications (C-index, 0.73). Five PIPEN risk categories were identified with 5-year BCR-free survival rates of 92%, 84%, 71%, 56%, and 26% in very low-, low-, intermediate-, high-, and very high-risk patients, respectively (all P < .001). Conclusion A five-item model for predicting the risk of 5-year BCR after radical prostatectomy for prostate cancer was developed and internally verified, and five risk categories were identified. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Aguirre and Ortegón in this issue.

Outcomes after definitive radiation therapy for localized prostate cancer in a national health care delivery system

PURPOSE

Accurate information regarding real-world outcomes after contemporary radiation therapy for localized prostate cancer is important for shared decision-making. Clinically relevant end points at 10 years among men treated within a national health care delivery system were examined.

METHODS

National administrative, cancer registry, and electronic health record data were used for patients undergoing definitive radiation therapy with or without concurrent androgen deprivation therapy within the Veterans Health Administration from 2005 to 2015. National Death Index data were used through 2019 for overall and prostate cancer-specific survival and identified date of incident metastatic prostate cancer using a validated natural language processing algorithm. Metastasis-free, prostate cancer-specific, and overall survival using Kaplan-Meier methods were estimated.

RESULTS

Among 41,735 men treated with definitive radiation therapy, the median age at diagnosis was 65 years and median follow-up was 8.7 years. Most had intermediate (42%) and high-risk (33%) disease, with 40% receiving androgen deprivation therapy as part of initial therapy. Unadjusted 10-year metastasis-free survival was 96%, 92%, and 80% for low-, intermediate-, and high-risk disease. Similarly, unadjusted 10-year prostate cancer-specific survival was 98%, 97%, and 90% for low-, intermediate-, and high-risk disease. The unadjusted overall survival was lower across increasing disease risk categories at 77%, 71%, and 62% for low-, intermediate-, and high-risk disease (p < .001).

CONCLUSIONS

These data provide population-based 10-year benchmarks for clinically relevant end points, including metastasis-free survival, among patients with localized prostate cancer undergoing radiation therapy using contemporary techniques. The survival rates for high-risk disease in particular suggest that outcomes have recently improved.

Use of the Decipher genomic classifier among men with prostate cancer in the United States

BACKGROUND

Management of localized or recurrent prostate cancer since the 1990s has been based on risk stratification using clinicopathological variables, including Gleason score, T stage (based on digital rectal exam), and prostate-specific antigen (PSA). In this study a novel prognostic test, the Decipher Prostate Genomic Classifier (GC), was used to stratify risk of prostate cancer progression in a US national database of men with prostate cancer.

METHODS

Records of prostate cancer cases from participating SEER (Surveillance, Epidemiology, and End Results) program registries, diagnosed during the period from 2010 through 2018, were linked to records of testing with the GC prognostic test. Multivariable analysis was used to quantify the association between GC scores or risk groups and use of definitive local therapy after diagnosis in the GC biopsy-tested cohort and postoperative radiotherapy in the GC-tested cohort as well as adverse pathological findings after prostatectomy.

RESULTS

A total of 572 545 patients were included in the analysis, of whom 8927 patients underwent GC testing. GC biopsy-tested patients were more likely to undergo active active surveillance or watchful waiting than untested patients (odds ratio [OR] =2.21, 95% confidence interval [CI] = 2.04 to 2.38, P < .001). The highest use of active surveillance or watchful waiting was for patients with a low-risk GC classification (41%) compared with those with an intermediate- (27%) or high-risk (11%) GC classification (P < .001). Among National Comprehensive Cancer Network patients with low and favorable-intermediate risk, higher GC risk class was associated with greater use of local therapy (OR = 4.79, 95% CI = 3.51 to 6.55, P < .001). Within this subset of patients who were subsequently treated with prostatectomy, high GC risk was associated with harboring adverse pathological findings (OR = 2.94, 95% CI = 1.38 to 6.27, P = .005). Use of radiation after prostatectomy was statistically significantly associated with higher GC risk groups (OR = 2.69, 95% CI = 1.89 to 3.84).

CONCLUSIONS

There is a strong association between use of the biopsy GC test and likelihood of conservative management. Higher genomic classifier scores are associated with higher rates of adverse pathology at time of surgery and greater use of postoperative radiotherapy.In this study the Decipher Prostate Genomic Classifier (GC) was used to analyze a US national database of men with prostate cancer. Use of the GC was associated with conservative management (ie, active surveillance). Among men who had high-risk GC scores and then had surgery, there was a 3-fold higher chance of having worrisome findings in surgical specimens.

A Personalized Clinical Dynamic Prediction Model to Characterize Prognosis for Patients With Localized Prostate Cancer: Analysis of the CHHiP Phase 3 Trial

PURPOSE

The CHHiP trial assessed moderately hypofractionated radiation therapy in localized prostate cancer. We utilized longitudinal prostate-specific antigen (PSA) measurements collected over time to evaluate and characterize patient prognosis.

METHODS AND MATERIALS

We developed a clinical dynamic prediction joint model to predict the risk of biochemical or clinical recurrence. Modeling included repeated PSA values and adjusted for baseline prognostic risk factors of age, tumor characteristics, and treatment received. We included 3071 trial participants for model development using a mixed-effect submodel for the longitudinal PSAs and a time-to-event hazard submodel for predicting recurrence of prostate cancer. We evaluated how baseline prognostic factor subgroups affected the nonlinear PSA levels over time and quantified the association of PSA on time to recurrence. We assessed bootstrapped optimism-adjusted predictive performance on calibration and discrimination. Additionally, we performed comparative dynamic predictions on patients with contrasting prognostic factors and investigated PSA thresholds over landmark times to correlate with prognosis.

RESULTS

Patients who developed recurrence had generally higher baseline and overall PSA values during follow-up and had an exponentially rising PSA in the 2 years before recurrence. Additionally, most baseline risk factors were significant in the mixed-effect and relative-risk submodels. PSA value and rate of change were predictive of recurrence. Predictive performance of the model was good across different prediction times over an 8-year period, with an overall mean area under the curve of 0.70, mean Brier score of 0.10, and mean integrated calibration index of 0.048; these were further improved for predictions after 5 years of accrued longitudinal posttreatment PSA assessments. PSA thresholds <0.23 ng/mL after 3 years were indicative of a minimal risk of recurrence by 8 years.

CONCLUSIONS

We successfully developed a joint statistical model to predict prostate cancer recurrence, evaluating prognostic factors and longitudinal PSA. We showed dynamically updated PSA information can improve prognostication, which can be used to guide follow-up and treatment management options.

Analysis of the surgical approach in prostate cancer staging: results from the surveillance, epidemiology and end results program

Surgery is not used as a criterion for staging prostate cancer, although there is evidence that the number of analyzed and affected lymph nodes have prognosis value. The aim of this study was to determine whether there are significant differences in staging criteria in patients who underwent prostatectomy compared to those who did not, and whether the number of affected and analyzed lymph nodes (LN) plays a prognostic role. In this retrospective study, a test cohort consisting of 404,210 newly diagnosed men with prostate cancer, between 2004 and 2010, was obtained from the 17 registries (Nov 2021 submission); a validation consisting of 147,719 newly diagnosed men with prostate cancer between 2004 and 2019 was obtained from the 8 registries (Nov 2021 submission). Prostate cancer-specific survival was analyzed by Kaplan-Meier curves, survival tables and Cox regression; overall survival was analyzed only to compare Harrell's C-index between different staging criteria. In initial analyses, it was observed that the prognostic value of lymph node metastasis changes according to the type of staging (clinical or pathological), which is linked to the surgical approach (prostatectomy). Compared with T4/N0/M0 patients, which are also classified as stage IVA, N1/M0 patients had a shorter [adjusted HR: 1.767 (1429-2184), p < 0.0005] and a longer [adjusted HR: 0.832 (0.740-0.935), p = 0.002] specific survival when submitted to prostatectomy or not, respectively. Analyzing separately the patients who were submitted to prostatectomy and those who were not, it was possible to obtain new LN metastasis classifications (N1: 1 + LN; N2: 2 + LNs; N3: > 2 + LNs). This new (pathological) classification of N allowed the reclassification of patients based on T and Gleason grade groups, mainly those with T3 and T4 disease. In the validation group, this new staging criterion was proven to be superior [specific survival C-index: 0.908 (0.906-0.911); overall survival C-index: 0.788 (0.786-0.791)] compared to that currently used by the AJCC [8th edition; specific survival C-index: 0.892 (0.889-0.895); overall survival C-index: 0.744 (0.741-0.747)]. In addition, an adequate number of dissected lymph nodes results in a 39% reduction in death risk [adjusted HR: 0.610 (0.498-0.747), p < 0.0005]. As main conclusion, the surgery has a major impact on prostate cancer staging, mainly modifying the effect of N on survival, and enabling the stratification of pathological N according to the number of affected LN. Such a factor, when considered as staging criteria, improves the prognosis classification.

Risk stratification for early biochemical recurrence of prostate cancer in the era of multiparametric magnetic resonance imagining-targeted biopsy

BACKGROUND

Multiparametric magnetic resonance imaging (MRI) and MRI-targeted biopsy are nowadays recommended in the prostate cancer (PCa) diagnostic pathway. Ploussard and Mazzone have integrated these tools into novel risk classification systems predicting the risk of early biochemical recurrence (eBCR) in PCa patients who underwent radical prostatectomy (RP). We aimed to assess available risk classification systems and to define the best-performing.

METHODS

Data on 1371 patients diagnosed by MRI-targeted biopsy and treated by RP between 2014 and 2022 at eight European tertiary referral centers were analyzed. Risk classifications systems included were the European Association of Urology (EAU) and National Comprehensive Cancer Network (NCCN) risk groups, the Cancer of the Prostate Risk Assessment (CAPRA) score, the International Staging Collaboration for Cancer of the Prostate (STAR-CAP) classification, the Ploussard and Mazzone models, and ISUP grade group. Kaplan-Meier analyses were used to compare eBCR among risk classification systems. Performance was assessed in terms of discrimination quantified using Harrell's c-index, calibration, and decision curve analysis (DCA).

RESULTS

Overall, 152 (11%) patients had eBCR at a median follow-up of 31 months (interquartile range: 19-45). The 3-year eBCR-free survival rate was 91% (95% confidence interval [CI]: 89-93). For each risk classification system, a significant difference among survival probabilities was observed (log-rank test p < 0.05) except for NCCN classification (p = 0.06). The highest discrimination was obtained with the STAR-CAP classification (c-index 66%) compared to CAPRA score (63% vs. 66%, p = 0.2), ISUP grade group (62% vs. 66, p = 0.07), Ploussard (61% vs. 66%, p = 0.003) and Mazzone models (59% vs. 66%, p = 0.02), and EAU (57% vs. 66%, p < 0.001) and NCCN (57% vs. 66%, p < 0.001) risk groups. Risk classification systems demonstrated good calibration characteristics. At DCA, the CAPRA score showed the highest net benefit at a probability threshold of 9%-15%.

CONCLUSIONS

The performance of risk classification systems using MRI and MRI-targeted information was less optimistic when tested in a contemporary set of patients. CAPRA score and STAR-CAP classification were the best-performing and should be preferred for treatment decision-making.

Development and validation of a prognostic nomogram for neuroendocrine prostate cancer, based on the SEER database

Background

The tumor biology of neuroendocrine prostate cancer (NEPC) is different from that of ordinary prostate cancer, herefore, existing clinical prognosis models for prostate cancer patients are unsuitable for NEPC. The specialized individual situation assessment and clinical decision-making tools for NEPC patients are urgently needed. This study aimed to develop a valid NEPC prognostic nomogram and risk stratification model to predict risk associated with patient outcomes.

Methods

We collected 340 de-novo NEPC patients from the SEER database, and randomly selected 240 of them as the training set and the remaining 100 as the validation set. Cox regression model was used to screen for risk factors affecting overall survival (OS) and cancer-specific survival (CSS) and construct a corresponding nomogram. The receiver operating characteristic (ROC) curves, calibration curves, C-indexes, and decision curve analysis (DCA) curves are used to verify and calibrate nomograms.

Results

NEPC prognosis nomograms were constructed by integrating independent risk factors. The C-indexes, ROC curves, calibration curves, and DCA curves revealed excellent prediction accuracy of the prognostic nomogram. Furthermore, we demonstrated that NEPC patients in the high-risk group had significantly lower OS and CSS than those in the low-risk group with risk scores calculated from nomograms.

Conclusions

The nomogram established in this research has the potential to be applied to the clinic to evaluate the prognosis of NEPC patients and support corresponding clinical decision-making.

Limited Relevance of the Very Low Risk Prostate Cancer Classification in the Modern Era: Results from a Large Institutional Active Surveillance Cohort

Although the American Urological Association recently dropped the very low-risk (VLR) subcategory for low-risk prostate cancer (PCa) and the European Association of Urology does not substratify low-risk PCa, the National Comprehensive Cancer Network (NCCN) guidelines still maintain this stratum, which is based on the number of positive biopsy cores, tumor extent in each core, and prostate-specific antigen density. This subdivision may be less applicable in the modern era in which imaging-targeted prostate biopsies are common practice. In our large institutional active surveillance cohort of patients diagnosed from 2000 to 2020 (n = 1276), the number of patients meeting NCCN VLR criteria decreased significantly in recent years, with no patient meeting VLR criteria after 2018. By contrast, the multivariable Cancer of the Prostate Risk Assessment (CAPRA) score effectively substratified patients over the same period and was predictive of upgrading on repeat biopsy to Gleason grade group ≥2 on multivariable Cox proportional-hazards regression modeling (hazard ratio 1.21, 95% confidence interval 1.05-1.39; p < 0.01), independent of age, genomic test results, and magnetic resonance imaging findings. These findings suggest that the NCCN VLR criteria are less applicable in the targeted biopsy era, and that the CAPRA score or similar instruments are better contemporary risk stratification tools for men on active surveillance. PATIENT SUMMARY: We investigated whether the National Comprehensive Cancer Network classification of very low risk (VLR) for prostate cancer is relevant in the modern era. We found that in a large group of patients on active surveillance, no man diagnosed after 2018 satisfied the VLR criteria. However, the Cancer of the Prostate Risk Assessment (CAPRA) score discriminated patients by cancer risk at diagnosis and was predictive of outcomes on active surveillance, and thus may be a more relevant classification scheme in the modern era.

Genetic Risk Prediction for Prostate Cancer: Implications for Early Detection and Prevention

CONTEXT

Prostate cancer (PCa) is a leading cause of death and partially heritable. Genetic risk prediction might be useful for strategies to reduce PCa mortality through early detection and prevention.

OBJECTIVE

To review evidence for genetic risk prediction for PCa.

EVIDENCE ACQUISITION

A collaborative literature review was conducted using PubMed and Google Scholar. Search terms included genetic, risk, prediction, and "prostate cancer". Articles addressing screening, early detection, or prevention were prioritized, as were studies involving diverse populations.

EVIDENCE SYNTHESIS

Rare pathogenic mutations (RPMs), especially in DNA damage repair genes, increase PCa risk. RPMs in BRCA2 are most clearly deleterious, conferring 2-8.6 times higher risk of PCa and a higher risk of aggressive disease. Common genetic variants can be combined into genetic risk scores (GRSs). A high GRS (top 20-25% of the population) confers two to three times higher risk of PCa than average; a very high GRS (top 1-5%) confers six to eight times higher risk. GRSs are not specific for aggressive PCa, possibly due to methodological limitations and/or a field effect of an elevated risk for both low- and high-grade PCa. It is challenging to disentangle genetics from structural racism and social determinants of health to understand PCa racial disparities. GRSs are independently associated with a lethal PCa risk after accounting for family history and race/ancestry. Healthy lifestyle might partially mitigate the risk of lethal PCa.

CONCLUSIONS

Genetic risk assessment is becoming more common; implementation studies are needed to understand the implications and to avoid exacerbating healthcare disparities. Men with a high genetic risk of PCa can reasonably be encouraged to adhere to a healthy lifestyle.

PATIENT SUMMARY

Prostate cancer risk is inherited through rare mutations and through the combination of hundreds of common genetic markers. Some men with a high genetic risk (especially BRCA2 mutations) likely benefit from early screening for prostate cancer. The risk of lethal prostate cancer can be reduced through a healthy lifestyle.

Multi-candidate immunohistochemical markers to assess radiation response and prognosis in prostate cancer: results from the CHHiP trial of radiotherapy fractionation

BACKGROUND

Protein markers of cellular proliferation, hypoxia, apoptosis, cell cycle checkpoints, growth factor signalling and inflammation in localised prostate tumours have previously shown prognostic ability. A translational substudy within the CHHiP trial of radiotherapy fractionation evaluated whether these could improve prediction of prognosis and assist treatment stratification following either conventional or hypofractionated radiotherapy.

METHODS

Using case:control methodology, patients with biochemical or clinical failure after radiotherapy (BCR) were matched to patients without recurrence according to established prognostic factors (Gleason score, presenting PSA, tumour-stage) and fractionation schedule. Immunohistochemical (IHC) staining of diagnostic biopsy sections was performed and scored for HIF1α, Bcl-2, Ki67, Geminin, p16, p53, p-chk1 and PTEN. Univariable and multivariable conditional logistic regression models, adjusted for matching strata and age, estimated the prognostic value of each IHC biomarker, including interaction terms to determine BCR prediction according to fractionation.

FINDINGS

IHC results were available for up to 336 tumours. PTEN, Geminin, mean Ki67 and max Ki67 were prognostic after adjusting for multiple comparisons and were fitted in a multivariable model (n = 212, 106 matched pairs). Here, PTEN and Geminin showed significant prediction of prognosis. No marker predicted BCR according to fractionation.

INTERPRETATION

Geminin or Ki67, and PTEN, predicted response to radiotherapy independently of established prognostic factors. These results provide essential independent external validation of previous findings and confirm a role for these markers in treatment stratification.

FUNDING

Cancer Research UK (BIDD) grant (A12518), Cancer Research UK (C8262/A7253), Department of Health, Prostate Cancer UK, Movember Foundation, NIHR Biomedical Research Centre at Royal Marsden/ICR.

Contrast-enhanced ultrasound targeted versus conventional ultrasound guided systematic prostate biopsy for the accurate diagnosis of prostate cancer: A meta-analysis

BACKGROUND

Conventional transrectal ultrasonography (TRUS) guided prostate biopsy is the standard method for accurate diagnosis of prostate cancer (PCa). However, the limitations of this technique in terms of missed diagnosis cannot be ignored. Based on previous studies, contrast-enhanced ultrasound (CEUS) may be able to more distinctly detect malignant lesions with increased microvessels. Therefore, to evaluate the diagnostic efficiency and clinical application prospects of CEUS-guided prostate biopsy for patients with suspected PCa, we performed a meta-analysis comparing CEUS-targeted with TRUS-guided systematic biopsy.

METHODS

A systematic search of PubMed, Web of Science, Embase and CNKI was performed up to March, 2022 for the relevant published studies. After data extraction and quality assessment, meta-analysis was performed using the RevMan 5.3 software.

RESULTS

The results showed that the overall sensitivity was higher for CEUS targeted biopsy than systematic biopsy (P = .03), so was the accuracy (P = .03). However, significant heterogeneity and inconsistent results from certain subgroup analyses challenged the validity of the results. Meanwhile, CEUS yielded a much higher sensitivity in patients with prostate specific antigen (PSA) level of 4 to 10 ng/mL (P = .007). On the other hand, the positive rate of each core (P < .001) and the detection rate of clinically significant PCa (P = .006) were significantly improved using CEUS.

CONCLUSION

CEUS showed the advantage of a higher detection rate of clinically significant PCa, which might provide more specific indications for subsequent treatment. More feasible, real-time data are required to confirm our findings.

Survival analysis of localized prostate cancer with deep learning

In recent years, data-driven, deep-learning-based models have shown great promise in medical risk prediction. By utilizing the large-scale Electronic Health Record data found in the U.S. Department of Veterans Affairs, the largest integrated healthcare system in the United States, we have developed an automated, personalized risk prediction model to support the clinical decision-making process for localized prostate cancer patients. This method combines the representative power of deep learning and the analytical interpretability of parametric regression models and can implement both time-dependent and static input data. To collect a comprehensive evaluation of model performances, we calculate time-dependent C-statistics [Formula: see text] over 2-, 5-, and 10-year time horizons using either a composite outcome or prostate cancer mortality as the target event. The composite outcome combines the Prostate-Specific Antigen (PSA) test, metastasis, and prostate cancer mortality. Our longitudinal model Recurrent Deep Survival Machine (RDSM) achieved [Formula: see text] 0.85 (0.83), 0.80 (0.83), and 0.76 (0.81), while the cross-sectional model Deep Survival Machine (DSM) attained [Formula: see text] 0.85 (0.82), 0.80 (0.82), and 0.76 (0.79) for the 2-, 5-, and 10-year composite (mortality) outcomes, respectively. In addition to estimating the survival probability, our method can quantify the uncertainty associated with the prediction. The uncertainty scores show a consistent correlation with the prediction accuracy. We find PSA and prostate cancer stage information are the most important indicators in risk prediction. Our work demonstrates the utility of the data-driven machine learning model in prostate cancer risk prediction, which can play a critical role in the clinical decision system.

Assessing the impact of MRI based diagnostics on pre-treatment disease classification and prognostic model performance in men diagnosed with new prostate cancer from an unscreened population

Introduction

Pre-treatment risk and prognostic groups are the cornerstone for deciding management in non-metastatic prostate cancer. All however, were developed in the pre-MRI era. Here we compared categorisation of cancers using either only clinical parameters or with MRI enhanced information in men referred for suspected prostate cancer from an unscreened population.

Patient and methods

Data from men referred from primary care to our diagnostic service and with both clinical (digital rectal examination [DRE] and systematic biopsies) and MRI enhanced attributes (MRI stage and combined systematic/targeted biopsies) were used for this study. Clinical vs MRI data were contrasted for clinico-pathological and risk group re-distribution using the European Association of Urology (EAU), American Urological Association (AUA) and UK National Institute for Health Care Excellence (NICE) Cambridge Prognostic Group (CPG) models. Differences were retrofitted to a population cohort with long-term prostate cancer mortality (PCM) outcomes to simulate impact on model performance. We further contrasted individualised overall survival (OS) predictions using the Predict Prostate algorithm.

Results

Data from 370 men were included (median age 66y). Pre-biopsy MRI stage reassignments occurred in 7.8% (versus DRE). Image-guided biopsies increased Grade Group 2 and ≥ Grade Group 3 assignments in 2.7% and 2.9% respectively. The main change in risk groups was more high-risk cancers (6.2% increase in the EAU and AUA system, 4.3% increase in CPG4 and 1.9% CPG5). When extrapolated to a historical population-based cohort (n = 10,139) the redistribution resulted in generally lower concordance indices for PCM. The 5-tier NICE-CPG system outperformed the 4-tier AUA and 3-tier EAU models (C Index 0.70 versus 0.65 and 0.64). Using an individualised prognostic model, changes in predicted OS were small (median difference 1% and 2% at 10- and 15-years’ respectively). Similarly, estimated treatment survival benefit changes were minimal (1% at both 10- and 15-years’ time frame).

Conclusion

MRI guided diagnostics does change pre-treatment risk groups assignments but the overall prognostic impact appears modest in men referred from unscreened populations. Particularly, when using more granular tiers or individualised prognostic models. Existing risk and prognostic models can continue to be used to counsel men about treatment option until long term survival outcomes are available.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09955-w.

Can we use Ki67 expression to predict prostate cancer aggressiveness?

INTRODUCTION

specialists have an urge for biomarkers that can discriminate indolent prostate cancer from aggressive tumors. Ki67 is a proliferation marker, and its expression is associated with the aggressiveness of several cancers.

OBJECTIVE

analyze the expression of Ki67 in prostate cancer samples correlating with the aggressiveness of the disease.

METHODS

Ki67 mRNA levels were determined utilizing data from a TCGA cohort (Tumor(n)=492 and control(n)=52). The protein expression was determined on 94 biopsies from patients by immunohistochemical assay.

RESULTS

in mRNA, the Ki67 upregulation is associated with cancer tissue (p<0.0001) and worst disease-free survival (p=0.035). The protein upregulation is associated with increase of the ISUP score (p<0.0001), cancer stage (p=0.05), biochemical recurrence (p=0.0006) and metastasis (p<0.0001). We also show a positive correlation between Ki67 expression and ISUP score (r=0.5112, p<0.0001) and disease risk stratification (r=0.3388, p=0.0009). Ki67 expression is a factor independently associated with biochemical recurrence (p=0.002) and metastasis (p<0.0001). Finally, the patients with high Ki67expression shows better survival regarding biochemical recurrence (p=0.008) and metastasis (p=0.056). Patients with high Ki67 expression are 2.62 times more likely to develop biochemical recurrence (p=0.036).

CONCLUSION

Ki67 upregulation is associated with prostate cancer aggressiveness.

Prognostic differences among Grade Group 4 subgroups in robotic‐assisted radical prostatectomy

Objectives

To investigate whether the International Society of Urological Pathology Grade Group 4 (GG 4) subgroups have different oncological outcomes in Japanese prostate cancer (PCa) patients undergoing robotic‐assisted radical prostatectomy (RARP).

Patients and Methods

We conducted a retrospective multicentre cohort study in PCa patients undergoing RARP at 10 institutions in Japan. Pre‐ and post‐operative variables were collected from enrolled patients. We evaluated biochemical recurrence and clinical and pathological variables in the different GG 4 subgroups.

Results

A total of 3195 patients were enrolled in the study. Among them, 298 patients with GG 4 tumours (pathological Gleason scores [GSs] of 3 + 5 [N = 37], 4 + 4 [N = 257] and 5 + 3 [N = 4]) based on RARP specimens were analysed. The median follow‐up period was 25.2 months. The 3‐year biochemical recurrence (BCR)‐free survival (BCRFS) rate in the overall population was 74.5%. The 3‐year BCRFS rates in the pathological GS 3 + 5, GS 4 + 4 and GS 5 + 3 subgroups were 93.8%, 71.9% and 50.0%, respectively (P = 0.01). In multivariate analysis, pathological GS based on RARP specimens, PSA levels at surgery, pathological T stage, pathological N stage and surgical margins were independent risk factors significantly associated with BCRFS. In particular, patients with pathological GSs 4 + 4 and 5 + 3 were at higher risk of BCR than patients with pathological GS 3 + 5 (hazard ratio 4.54, P = 0.03 and hazard ratio 11.2, P = 0.01, respectively). The study limitations include the lack of central pathological specimen evaluation.

Conclusions

For patients with localized PCa undergoing RARP, pathological GS 4 + 4 and GS 5 + 3 were significantly associated with worse BCRFS than pathological GS 3 + 5. Pathological GS 3 + 5 may be overrated in GG 4. This observation emphasizes that primary and secondary GS should be considered to accurately stratify the risk of BCR after RARP.

Incidence and Risk Factors for Cerebrovascular-Specific Mortality in Patients with Colorectal Cancer: A Registry-Based Cohort Study Involving 563,298 Patients

Simple Summary

Previous studies have shown that the occurrence of cerebrovascular-specific diseases was common in cancer patients. However, the association between colorectal cancer and cerebrovascular-specific diseases remains to be fully elucidated. In this large-population cohort study, we found that the mortality of cerebrovascular-specific diseases mortality in colorectal cancer patients was significantly higher than the general US population. In addition, we investigated several potential predictors of cerebrovascular-specific diseases mortality in colorectal cancer. This study may be useful for the prevention, risk stratification and therapeutic optimization of cerebrovascular-specific diseases in colorectal cancer patients.

Abstract

Background: Colorectal cancer (CRC) is one of the most prevalent diseases and the second leading cause of death worldwide. However, the relationship between CRC and cerebrovascular-specific mortality (CVSM) remains elusive, and less is known about the influencing factors associated with CVSM in CRC. Here, we aimed to analyze the incidence as well as the risk factors of CVSM in CRC. Methods: Patients with a primary CRC diagnosed between 1973 and 2015 were identified from the Surveillance Epidemiology and End Results database, with follow-up data available until 31 December 2016. Conditional standardized mortality ratios were calculated to compare the incidence of CVSM between CRC patients and the general U.S. population. Univariate and multivariate survival analyses with a competing risk model were used to interrogate the risk factors for CVSM. Results: A total of 563,298 CRC individuals were included. The CVSM in CRC patients was significantly higher than the general population in all age subgroups. Among the competing causes of death in patients, the cumulative mortality caused by cerebrovascular-specific diseases steadily increased during the study period. While age, surgery, other/unknown race and tumors located at the transverse colon positively influenced CVSM on both univariate and multivariate analyses, male patients and those who had radiotherapy, chemotherapy, a more recent year (2001–2015) of diagnosis, a grade II or III CRC, rectal cancer, or multiple primary or distant tumors experienced a lower risk of CVSM. Interpretation: Our data suggest a potential role for CRC in the incidence of CVSM and also identify several significant predictors of CVSM that may be helpful for risk stratification and the therapeutic optimization of cerebrovascular-specific diseases in CRC patients.

Single-cell proteomics defines the cellular heterogeneity of localized prostate cancer

Localized prostate cancer exhibits multiple genomic alterations and heterogeneity at the proteomic level. Single-cell technologies capture important cell-to-cell variability responsible for heterogeneity in biomarker expression that may be overlooked when molecular alterations are based on bulk tissue samples. This study aims to identify prognostic biomarkers and describe the heterogeneity of prostate cancer and the associated microenvironment by simultaneously quantifying 36 proteins using single-cell mass cytometry analysis of over 1.6 million cells from 58 men with localized prostate cancer. We perform this task, using a high-dimensional clustering pipeline named Franken to describe subpopulations of immune, stromal, and prostate cells, including changes occurring in tumor tissues and high-grade disease that provide insights into the coordinated progression of prostate cancer. Our results further indicate that men with localized disease already harbor rare subpopulations that typically occur in castration-resistant and metastatic disease.

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Single-cell proteomics of localized prostate cancer defines disease heterogeneity

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Malignant and benign prostate tissues differ in rare cell-type proportional shifts

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T cells and proliferating macrophages are associated with high-grade PCa

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Rare CD15⁺ epithelial cells are amplified in high-grade PCa

Moderate hypofractionated helical tomotherapy for older patients with localized prostate cancer: long-term outcomes of a phase I-II trial

BACKGROUND

Our previous study showed that two different regimens of moderate hypofractionated radiotherapy (HFRT) delivered with helical tomotherapy (HT) are well tolerated in older prostate cancer patients. We provide a longterm efficacy and toxicity after > 7 years of follow-up.

PATIENTS AND METHODS

The study recruited 33 patients from February 2009 to July 2011 (76 Gy/34F; Group-1); and 34 from July 2011 to February 2014 (71.6 Gy/28F; 50.4 Gy/25F for the risk of pelvic lymph nodes involvement (LNI) >15%; Group-2). The primary outcomes were biochemical failure (BF), biochemical failure and clinical disease failure (BCDF), progression-free survival (PFS), overall survival (OS), late genitourinary (GU) and gastrointestinal (GI) toxicity.

RESULTS

The average ages of two groups were 80 and 77 years and the proportions of patients with LNI > 15% were 69.7% and 73.5%, respectively. At the final follow-up in February 2020, 27.3% and 20.6% cases experienced BF, with a median time until BF of 3.3 years. A total of 38.8% patients reached primary endpoints, in which 18 deaths were reported BCDF events (45.5% vs. 32.4%, p = 0.271). There was no significant difference in 7-year PFS (68.6% vs. 74.8%, p = 0.591), BCDF (45.5% vs. 32.4%, p = 0.271) and OS (71.9% vs. 87.5%, p = 0.376) for full set analysis and for subgroup analysis (all p > 0.05). The incidence of grade ≥ 2 late GU (6.2% vs. 6.3%, p = 0.127) and GI toxicities (9.4% vs. 15.6%, p = 0.554) was comparable.

CONCLUSIONS

In older patients with localized prostate cancer, two moderate hypofractionated regimens were all well tolerated with similar, mild late toxicities and satisfactory survival, without necessity of prophylactic pelvic node irradiation.

High Keratin-7 Expression in Benign Peri-Tumoral Prostatic Glands Is Predictive of Bone Metastasis Onset and Prostate Cancer-Specific Mortality

BACKGROUND

New predictive biomarkers are needed to accurately predict metastasis-free survival (MFS) and cancer-specific survival (CSS) in localized prostate cancer (PC). Keratin-7 (KRT7) overexpression has been associated with poor prognosis in several cancers and is described as a novel prostate progenitor marker in the mouse prostate.

METHODS

KRT7 expression was evaluated in prostatic cell lines and in human tissue by immunohistochemistry (IHC, on advanced PC, n = 91) and immunofluorescence (IF, on localized PC, n = 285). The KRT7 mean fluorescence intensity (MFI) was quantified in different compartments by digital analysis and correlated to clinical endpoints in the localized PC cohort.

RESULTS

KRT7 is expressed in prostatic cell lines and found in the basal and supra-basal compartment from healthy prostatic glands and benign peri-tumoral glands from localized PC. The KRT7 staining is lost in luminal cells from localized tumors and found as an aberrant sporadic staining (2.2%) in advanced PC. In the localized PC cohort, high KRT7 MFI above the 80th percentile in the basal compartment was significantly and independently correlated with MFS and CSS, and with hypertrophic basal cell phenotype.

CONCLUSION

High KRT7 expression in benign glands is an independent biomarker of MFS and CSS, and its expression is lost in tumoral cells. These results require further validation on larger cohorts.

piRNAs and PIWI Proteins as Diagnostic and Prognostic Markers of Genitourinary Cancers

piRNAs (PIWI-interacting RNAs) are small non-coding RNAs capable of regulation of transposon and gene expression. piRNAs utilise multiple mechanisms to affect gene expression, which makes them potentially more powerful regulators than microRNAs. The mechanisms by which piRNAs regulate transposon and gene expression include DNA methylation, histone modifications, and mRNA degradation. Genitourinary cancers (GC) are a large group of neoplasms that differ by their incidence, clinical course, biology, and prognosis for patients. Regardless of the GC type, metastatic disease remains a key therapeutic challenge, largely affecting patients’ survival rates. Recent studies indicate that piRNAs could serve as potentially useful biomarkers allowing for early cancer detection and therapeutic interventions at the stage of non-advanced tumour, improving patient’s outcomes. Furthermore, studies in prostate cancer show that piRNAs contribute to cancer progression by affecting key oncogenic pathways such as PI3K/AKT. Here, we discuss recent findings on biogenesis, mechanisms of action and the role of piRNAs and the associated PIWI proteins in GC. We also present tools that may be useful for studies on the functioning of piRNAs in cancers.

Podemos usar a expressão de Ki67 para prever a agressividade do câncer de próstata?

RESUMO Introdução: especialistas precisam biomarcadores que podem discriminar o câncer de próstata indolente de tumores agressivos. Ki67 é um marcador de proliferação, e sua expressão está associada à agressividade de vários tumores. Objetivo: analisar a expressão do Ki67 em amostras de câncer de próstata correlacionando com a agressividade da doença. Métodos: os níveis de mRNA de Ki67 foram determinados utilizando dados de uma coorte de TCGA (Tumor(n)=492 e controle(n)=52). A expressão da proteína foi determinada em 94 biópsias de pacientes por ensaio imuno-histoquímica. Resultados: no mRNA, a superexpressão Ki67 está associada ao tecido canceroso (p<0,0001) e à pior sobrevida livre de doença (p=0,035). A superexpressão proteica está associada ao aumento do escore ISUP (p<0,0001), estágio de câncer (p=0,05), recorrência bioquímica (p=0,0006) e metástase (p<0,0001). Também mostramos uma correlação positiva entre a expressão Ki67 e o escore ISUP (r=0,5112, p<0,0001) e a estratificação de risco de doença (r=0,3388, p=0,0009). A expressão Ki67 é um fator independentemente associado à recorrência bioquímica (p=0,002) e metástase (p<0,0001). Finalmente, os pacientes com alta expressão de Ki67 expression mostram melhor sobrevivência em relação à recorrência bioquímica (p=0,008) e metástase (p=0,056). Os pacientes com alta expressão de Ki67 são 2,62 vezes mais propensos a desenvolver recorrência bioquímica (p=0,036). Conclusão: a superexpressão Ki67 está associada à agressividade do câncer de próstata.

Impact of age on treatment response in men with prostate cancer treated with radiotherapy

Objective

To analyse the effect of age at diagnosis on clinical outcomes of localized prostate cancer (PCa) treated with radiation therapy.

Subjects and methods

We identified 12 784 patients with intermediate‐ or high‐risk localized PCa treated with radiation therapy (RT) and neoadjuvant androgen deprivation therapy (ADT) between 2000 and 2015 from nationwide Veterans Affairs data. Patients were grouped into three age categories (≤59, 60–69, and ≥70 years old). Outcomes included immediate PSA response (3‐month post‐RT PSA and 2‐year PSA nadir, grouped into <0.10 ng/ml, 0.10–0.49 ng/ml, and ≥0.50 ng/ml), biochemical recurrence, and PCa‐specific mortality. Multivariable regression models included ordinal logistic regression for short‐term PSA outcomes, Cox regression for biochemical recurrence, and Fine‐Gray competing risks regression for PCa‐specific mortality.

Results

A total of 2136 patients (17%) were ≤59 years old at diagnosis, 6107 (48%) were 60–69 years old, and 4541 (36%) were ≥70 years old. Median follow‐up was 6.3 years. Younger age was associated with greater odds of higher 3‐month PSA group (≤59 vs. ≥70: adjusted odds ratio [aOR] 1.90, 95% CI 1.64–2.20; p < 0.001) and higher 2‐year PSA nadir group (≤59 vs. ≥70: aOR 1.89, 95% CI 1.62–2.19, p < 0.001). Younger age was associated with greater risk of biochemical recurrence (≤59 vs. ≥70: adjusted hazard ratio 1.45, 95% CI 1.26–1.67, p < 0.001) but not PCa‐specific mortality (p = 0.16).

Conclusion

In a large nationwide sample of US veterans treated with ADT and RT for localized PCa, younger age was associated with inferior short‐term PSA response and higher risk of biochemical recurrence.

Timing of the Pubertal Growth Spurt and Prostate Cancer

Simple Summary

Men’s pubertal timing lacks distinct markers that are easily available retrospectively. Therefore, the association between objectively assessed pubertal timing and the risk of prostate cancer is unknown. Our aim was to evaluate the association between the age at the pubertal growth spurt, an objective assessment of pubertal timing, and the risk of prostate cancer and high-risk prostate cancer. We used a population-based cohort including over 30,000 men with age at the pubertal growth spurt available and with follow-up in high quality national registers. During 1.4 million years of follow up, 1759 cases of prostate cancer were diagnosed. We demonstrate that late pubertal timing is a protective factor for prostate cancer, and especially for the clinically important high-risk or metastatic prostate cancer. Identification of early life risk- and protective factors for prostate cancer could provide new opportunities to unravel the underlying biological mechanism of the origins of prostate cancer.

Abstract

Previous studies of pubertal timing and the risk of prostate cancer have used self-reported markers of pubertal development, recalled in mid-life, and the results have been inconclusive. Our aim was to evaluate the age at the pubertal growth spurt, an objective marker of pubertal timing, and the risk of prostate cancer and high-risk prostate cancer. This population-based cohort study included 31,971 men with sufficient height measurements to calculate age at peak height velocity (PHV). Outcomes were accessed through national registers. Hazard ratios (HR) and 95% confidence intervals (CI) were estimated by Cox regressions with follow up starting at 20 years of age. In total, 1759 cases of prostate cancer including 449 high-risk were diagnosed during follow up. Mean follow up was 42 years (standard deviation 10.0). Compared to quintiles 2–4 (Q2–4), men in the highest age at PHV quintile (Q5) had lower risk of prostate cancer (HR 0.83, 95% CI 0.73–0.94), and of high-risk prostate cancer (0.73; 0.56–0.94). In an exploratory analysis with follow up starting at age at PHV, late pubertal timing was no longer associated with reduced risk of prostate cancer. Later pubertal timing was associated with reduced risk of prostate cancer and especially high-risk prostate cancer. We propose that the risk of prostate cancer might be influenced by the number of years with exposure to adult levels of sex steroids.

Performance of a Prostate-Specific Membrane Antigen Positron Emission Tomography/Computed Tomography-Derived Risk-Stratification Tool for High-risk and Very High-risk Prostate Cancer

IMPORTANCE

Prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) can detect low-volume, nonlocalized (ie, regional or metastatic) prostate cancer that was occult on conventional imaging. However, the long-term clinical implications of PSMA PET/CT upstaging remain unclear.

OBJECTIVES

To evaluate the prognostic significance of a nomogram that models an individual's risk of nonlocalized upstaging on PSMA PET/CT and to compare its performance with existing risk-stratification tools.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study included patients diagnosed with high-risk or very high-risk prostate cancer (ie, prostate-specific antigen [PSA] level >20 ng/mL, Gleason score 8-10, and/or clinical stage T3-T4, without evidence of nodal or metastatic disease by conventional workup) from April 1995 to August 2018. This multinational study was conducted at 15 centers. Data were analyzed from December 2020 to March 2021.

EXPOSURES

Curative-intent radical prostatectomy (RP), external beam radiotherapy (EBRT), or EBRT plus brachytherapy (BT), with or without androgen deprivation therapy.

MAIN OUTCOMES AND MEASURES

PSMA upstage probability was calculated from a nomogram using the biopsy Gleason score, percentage positive systematic biopsy cores, clinical T category, and PSA level. Biochemical recurrence (BCR), distant metastasis (DM), prostate cancer-specific mortality (PCSM), and overall survival (OS) were analyzed using Fine-Gray and Cox regressions. Model performance was quantified with the concordance (C) index.

RESULTS

Of 5275 patients, the median (IQR) age was 66 (60-72) years; 2883 (55%) were treated with RP, 1669 (32%) with EBRT, and 723 (14%) with EBRT plus BT; median (IQR) PSA level was 10.5 (5.9-23.2) ng/mL; 3987 (76%) had Gleason grade 8 to 10 disease; and 750 (14%) had stage T3 to T4 disease. Median (IQR) follow-up was 5.1 (3.1-7.9) years; 1221 (23%) were followed up for at least 8 years. Overall, 1895 (36%) had BCR, 851 (16%) developed DM, and 242 (5%) died of prostate cancer. PSMA upstage probability was significantly prognostic of all clinical end points, with 8-year C indices of 0.63 (95% CI, 0.61-0.65) for BCR, 0.69 (95% CI, 0.66-0.71) for DM, 0.71 (95% CI, 0.67-0.75) for PCSM, and 0.60 (95% CI, 0.57-0.62) for PCSM (P < .001). The PSMA nomogram outperformed existing risk-stratification tools, except for similar performance to Staging Collaboration for Cancer of the Prostate (STAR-CAP) for PCSM (eg, DM: PSMA, 0.69 [95% CI, 0.66-0.71] vs STAR-CAP, 0.65 [95% CI, 0.62-0.68]; P < .001; Memorial Sloan Kettering Cancer Center nomogram, 0.57 [95% CI, 0.54-0.60]; P < .001; Cancer of the Prostate Risk Assessment groups, 0.53 [95% CI, 0.51-0.56]; P < .001). Results were validated in secondary cohorts from the Surveillance, Epidemiology, and End Results database and the National Cancer Database.

CONCLUSIONS AND RELEVANCE

These findings suggest that PSMA upstage probability is associated with long-term, clinically meaningful end points. Furthermore, PSMA upstaging had superior risk discrimination compared with existing tools. Formerly occult, PSMA PET/CT-detectable nonlocalized disease may be the main driver of outcomes in high-risk patients.

Variance of Tumor Grade at Radical Prostatectomy With Assessment of Each Tumor Nodule Versus Global Grading

CONTEXT.—

Multifocal prostate cancer at radical prostatectomy (RP) may be graded with assessment of each individual tumor nodule (TN) or global grading of all TNs in aggregate.

OBJECTIVE.—

To assess case-level grade variability between these 2 grading approaches.

DESIGN.—

We reviewed 776 RPs with multifocal prostate cancer with 2 or more separate TNs of different Grade Groups (GGs). Two separate grades were assigned to each RP: one based on the TN with the highest grade and a global grade based on the Gleason pattern volumes for all TNs. We then compared the results of these 2 methods.

RESULTS.—

The case-level grade changed by 1 or more GGs between the 2 grading methods in 35% (132 of 374) of GG3 through GG5 cases. Twelve percent (37 of 309) of GG2 cases with Gleason pattern 4 more than 5% based on individual TN grading decreased their Gleason pattern 4 to less than 5% based on the global approach. Minor tertiary pattern 5 (Gleason pattern 5 <5%) was observed in 6.8% (11 of 161) of GG4 (Gleason score 3 + 5 = 8 and 5 + 3 = 8) and GG5 cases with global grading. The risk of grade discrepancy between the 2 methods was associated with the highest-grade TN volume (inverse relationship), patient age, and number of TNs (P < .001, P = .003, and P < .001, respectively).

CONCLUSIONS.—

The global grading approach resulted in a lower grade in 35% of GG3 through GG5 cases compared with grading based on the highest-grade TN. Two significant risk factors for this discrepancy with a global grading approach occur when the highest-grade TN has a relatively small tumor volume and with the higher number of TNs per RP. The observed grade variability between the 2 grading schemes most likely limits the interchangeability of post-RP multi-institutional databases if those institutions use different grading approaches.

Factors Associated with Time to Conversion from Active Surveillance to Treatment for Prostate Cancer in a Multi-Institutional Cohort

PURPOSE

We examined the demographic and clinicopathological parameters associated with the time to convert from active surveillance to treatment among men with prostate cancer.

MATERIALS AND METHODS

A multi-institutional cohort of 7,279 patients managed with active surveillance had data and biospecimens collected for germline genetic analyses.

RESULTS

Of 6,775 men included in the analysis, 2,260 (33.4%) converted to treatment at a median followup of 6.7 years. Earlier conversion was associated with higher Gleason grade groups (GG2 vs GG1 adjusted hazard ratio [aHR] 1.57, 95% CI 1.36-1.82; ≥GG3 vs GG1 aHR 1.77, 95% CI 1.29-2.43), serum prostate specific antigen concentrations (aHR per 5 ng/ml increment 1.18, 95% CI 1.11-1.25), tumor stages (cT2 vs cT1 aHR 1.58, 95% CI 1.41-1.77; ≥cT3 vs cT1 aHR 4.36, 95% CI 3.19-5.96) and number of cancerous biopsy cores (3 vs 1-2 cores aHR 1.59, 95% CI 1.37-1.84; ≥4 vs 1-2 cores aHR 3.29, 95% CI 2.94-3.69), and younger age (age continuous per 5-year increase aHR 0.96, 95% CI 0.93-0.99). Patients with high-volume GG1 tumors had a shorter interval to conversion than those with low-volume GG1 tumors and behaved like the higher-risk patients. We found no significant association between the time to conversion and self-reported race or genetic ancestry.

CONCLUSIONS

A shorter time to conversion from active surveillance to treatment was associated with higher-risk clinicopathological tumor features. Furthermore, patients with high-volume GG1 tumors behaved similarly to those with intermediate and high-risk tumors. An exploratory analysis of self-reported race and genetic ancestry revealed no association with the time to conversion.

Nomograms-based prediction of overall and cancer-specific survivals for patients diagnosed with major salivary gland carcinoma

Background

Major salivary glands carcinoma (MSGC) is a relatively rare cancer with diverse histological types and biological behavior. The treatment planning and prognosis prediction are challenging for clinicians. The aim of the current study was to establish a reliable and effective nomogram to predict the overall survival (OS) and cancer-specific survival (CSS) for MSGC patients.

Methods

Patients pathologically diagnosed with MSGC were recruited from Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into training and validation groups (7:3 ratio). Univariate, multivariate Cox proportional hazard models, and least absolute shrinkage and selection operator (LASSO) regression were adopted for the selection of risk factors. Nomograms were developed using R software. The model performance was evaluated by drawing receiver operating characteristic (ROC), overtime C-index curves, and calibration curves. Harrell C-index, areas under the curves (AUC), and Brier score were also calculated. The decision curve analysis (DCA) was conducted to measure the net clinical benefit.

Results

A total of 11,362 patients were identified and divided into training (n=7,953) and validation (n=3,409) dataset. Sex, age, race, marital status, site, differentiation grade, American Joint Committee on Cancer (AJCC) stage, T/N/M stage, tumor size, surgery, and histological type were incorporated into the Cox hazard model for OS prediction after variable selection, while all predictors, except for marital status and site, were selected for CSS prediction. For 5-year prediction, the AUC of the nomogram for OS and CSS was 83.5 and 82.7 in the training and validation dataset, respectively. The C-index was 0.787 for OS and 0.798 for CSS in the validation group. The Brier score was 0.0153 and 0.0130 for OS and CSS, respectively. The calibration curves showed that the nomogram had well prediction accuracy. From the perspective of DCA, a nomogram was superior to the AJCC stage and TNM stage in net benefit. In general, the performance of the nomogram was consistently better compared to the AJCC stage and TNM stage across all settings.

Conclusions

The performance of the novel nomogram for predicting OS and CSS of MSGC patients was further verified, revealing that it could be used as a valuable tool in assisting clinical decision-making.

Assessment of Survival Model Performance Following Inclusion of Epstein-Barr Virus DNA Status in Conventional TNM Staging Groups in Epstein-Barr Virus-Related Nasopharyngeal Carcinoma

IMPORTANCE

Nonanatomic prognostic factors complement the traditional anatomic staging system and could be incorporated into the tumor-node-metastasis (TNM) framework. Several diseases have incorporated nonanatomic prognostic factors into the determination of TNM staging groups.

OBJECTIVE

To refine TNM staging groups for Epstein-Barr virus (EBV)-related nonmetastatic nasopharyngeal carcinoma (NPC) by incorporating EBV DNA status.

DESIGN, SETTING, AND PARTICIPANTS

This multicenter prognostic study included patients with NPC treated with radiotherapy at 2 hospitals in China from January 2008 to December 2016. Progression-free survival and overall survival according to EBV DNA status and the TNM staging system were compared. Recursive partitioning analysis (RPA) combined with supervised clustering was applied to derive prognostic groupings, and then a refined RPA staging schema was developed, validated, and compared with existing staging schemes. Statistical analyses were conducted from October 1, 2020, to June 15, 2021.

EXPOSURES

Curative intensity-modulated radiotherapy with or without platinum-based chemotherapy.

MAIN OUTCOMES AND MEASURES

The primary end point was progression-free survival. The performance of the staging system was assessed using the time-dependent area under the receiver operating characteristic curves and the TNM stage system's evaluation methodology.

RESULTS

A total of 2354 patients (1709 men [72.6%]; median [interquartile range] age, 45 [38-53] years) were split into training (1372 [58.3%]), internal validation (672 [28.5%]), and external validation (310 [13.2%]) cohorts. Pretreatment EBV DNA was detected in 1338 (56.8%) patients. EBV DNA status was an independent prognostic factor: lower survival probability by higher TNM stage was evident in EBV DNA-positive patients but not in those with EBV DNA-negative disease. After integrating EBV DNA status and TNM stage, nonmetastatic NPC cases were categorized into RPA-I (T1-3N0 or EBV DNA-negative T1-3N1 cancers), RPA-II (EBV DNA-positive T1-3N1-2 or EBV DNA-negative T1-3N2-3/T4N0-3 cancers), and RPA-III (EBV DNA-positive T4N0-3/T1-3N3 cancers) groups, each with distinctly different prognosis. This system of RPA staging outperformed the current TNM stage system and 2 reported RPA staging schemes. These results were internally and externally validated.

CONCLUSIONS AND RELEVANCE

An RPA-based staging system for EBV-related NPC cases was associated with improved outcomes. This staging system may facilitate prognostic stratification and clinical trial designs.

Risk Stratification of Patients Candidate to Radical Prostatectomy Based on Clinical and Multiparametric Magnetic Resonance Imaging Parameters: Development and External Validation of Novel Risk Groups

BACKGROUND

Despite the key importance of magnetic resonance imaging (MRI) parameters, risk classification systems for biochemical recurrence (BCR) in prostate cancer (PCa) patients treated with radical prostatectomy (RP) are still based on clinical variables alone.

OBJECTIVE

We aimed at developing and validating a novel classification integrating clinical and radiological parameters.

DESIGN, SETTING, AND PARTICIPANTS

A retrospective multicenter cohort study was conducted between 2014 and 2020 across seven academic international referral centers. A total of 2565 patients treated with RP for PCa were identified.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Early BCR was defined as two prostate-specific antigen (PSA) values of ≥0.2 ng/ml within 3 yr after RP. Kaplan-Meier and Cox regressions tested time and predictors of BCR. Development and validation cohorts were generated from the overall patient sample. A model predicting early BCR based on Cox-derived coefficients represented the basis for a nomogram that was validated externally. Predictors consisted of PSA, biopsy grade group, MRI stage, and the maximum diameter of lesion at MRI. Novel risk categories were then identified. The Harrel's concordance index (c-index) compared the accuracy of our risk stratification with the European Association of Urology (EAU), Cancer of the Prostate Risk Assessment (CAPRA), and International Staging Collaboration for Cancer of the Prostate (STAR-CAP) risk groups in predicting early BCR.

RESULTS AND LIMITATIONS

Overall, 200 (8%), 1834 (71%), and 531 (21%) had low-, intermediate-, and high-risk disease according to the EAU risk groups. The 3-yr overall BCR-free survival rate was 84%. No differences were observed in the 3-yr BCR-free survival between EAU low- and intermediate-risk groups (88% vs 87%; p = 0.1). The novel nomogram depicted optimal discrimination at external validation (c-index 78%). Four new risk categories were identified based on the predictors included in the Cox-based nomogram. This new risk classification had higher accuracy in predicting early BCR (c-index 70%) than the EAU, CAPRA, and STAR-CAP risk classifications (c-index 64%, 63%, and 67%, respectively).

CONCLUSIONS

We developed and externally validated four novel categories based on clinical and radiological parameters to predict early BCR. This novel classification exhibited higher accuracy than the available tools.

PATIENT SUMMARY

Our novel and straightforward risk classification outperformed currently available preoperative risk tools and should, therefore, assist physicians in preoperative counseling of men candidate to radical treatment for prostate cancer.

Clinical outcomes and patterns of failure of head and neck mucosal melanoma treated with multiple treatment modalities

OBJECTIVES

The study aims to analyze the clinical characteristics of head and neck mucosal melanoma (MMHN) and the effects of multiple treatment modalities on distant metastasis, recurrence and survival rates to provide a reference for the individualized treatment of MMHN.

METHODS

We retrospectively reviewed 262 patients with stage III-IVb MMHN treated from March 1986 to November 2018 at our cancer center.

RESULTS

The median follow-up time was 34.0 months (range 1-262 months). The 5-year overall survival (OS), distant metastasis-free survival (DMFS) and disease-free survival (DFS) probabilities were 37.7%, 30.2%, and 20.3%, respectively. The 5-year OS rates for patients with stage III, stage IVA, and stage IVB MMHN were 67.0%, 24.1% and 8.3%, respectively (P < 0.001). A total of 246 (93.9%) patients received surgery, 149 (56.9%) patients received chemotherapy, and 69 (26.3%) patients received immunologic/targeted therapy. A total of 106 (40.5%) patients were treated with radiotherapy: 9 were treated with preoperative radiotherapy, 93 were treated with postoperative radiotherapy, and 4 were treated with radiotherapy alone. In the multivariate Cox regression analysis, primary tumor site, T stage, and immunologic/targeted therapy were independent factors for OS (all P < 0.05). Irradiation technique, T stage, and N stage were independent prognostic factors for DMFS (all P < 0.05). T stage, N stage, and surgery were independent prognostic factors for DFS (all P < 0.05). Distant metastasis was observed in 107 of 262 patients (40.8%), followed by local [74 (28.2%)] and regional [52 (19.8%)] recurrence.

CONCLUSIONS

The main reason for treatment failure in MMHN is distant metastasis. Immunologic/targeted therapy and surgery are recommended to improve the survival of MMHN. The American Joint Committee on Cancer (AJCC) 8th edition staging system for MMHN does stage this disease effectively.

Predicting prostate cancer specific-mortality with artificial intelligence-based Gleason grading

Background

Gleason grading of prostate cancer is an important prognostic factor, but suffers from poor reproducibility, particularly among non-subspecialist pathologists. Although artificial intelligence (A.I.) tools have demonstrated Gleason grading on-par with expert pathologists, it remains an open question whether and to what extent A.I. grading translates to better prognostication.

Methods

In this study, we developed a system to predict prostate cancer-specific mortality via A.I.-based Gleason grading and subsequently evaluated its ability to risk-stratify patients on an independent retrospective cohort of 2807 prostatectomy cases from a single European center with 5–25 years of follow-up (median: 13, interquartile range 9–17).

Results

Here, we show that the A.I.’s risk scores produced a C-index of 0.84 (95% CI 0.80–0.87) for prostate cancer-specific mortality. Upon discretizing these risk scores into risk groups analogous to pathologist Grade Groups (GG), the A.I. has a C-index of 0.82 (95% CI 0.78–0.85). On the subset of cases with a GG provided in the original pathology report (n = 1517), the A.I.’s C-indices are 0.87 and 0.85 for continuous and discrete grading, respectively, compared to 0.79 (95% CI 0.71–0.86) for GG obtained from the reports. These represent improvements of 0.08 (95% CI 0.01–0.15) and 0.07 (95% CI 0.00–0.14), respectively.

Conclusions

Our results suggest that A.I.-based Gleason grading can lead to effective risk stratification, and warrants further evaluation for improving disease management.

Gleason grading is the process by which pathologists assess the morphology of prostate tumors. The assigned Grade Group tells us about the likely clinical course of people with prostate cancer and helps doctors to make decisions on treatment. The process is complex and subjective, with frequent disagreement amongst pathologists. In this study, we develop and evaluate an approach to Gleason grading based on artificial intelligence, rather than pathologists’ assessment, to predict risk of dying of prostate cancer. Looking back at tumors and data from 2,807 people diagnosed with prostate cancer, we find that our approach is better at predicting outcomes compared to grading by pathologists alone. These findings suggest that artificial intelligence might help doctors to accurately determine the probable clinical course of people with prostate cancer, which, in turn, will guide treatment.

Wulczyn et al. utilise a deep learning-based Gleason grading model to predict prostate cancer-specific mortality in a retrospective cohort of radical prostatectomy patients. Their model enables improved risk stratification compared to pathologists’ grading and demonstrates the potential for computational pathology in the management of prostate cancer.

Validation of the STAR-CAP Clinical Prognostic System for Predicting Biochemical Recurrence, Metastasis, and Cancer-specific Mortality After Radical Prostatectomy in a European Cohort

The proposed international staging collaboration for cancer of the prostate (STAR-CAP) clinical prognostic system for prostate cancer predicts cancer-specific mortality (CSM) for patients for whom active treatment, such as radical prostatectomy (RP), is planned. Until now, no validation of STAR-CAP has been performed. We retrospectively analyzed data from our institutional database for 19 552 patients treated with RP between 1992 and 2015. We applied the STAR-CAP point assignment criteria to calculate total individual scores and then classified patients according to the STAR-CAP stage groups ranging from IA (lowest risk) to IIIC (highest risk). We evaluated biochemical recurrence (BCR)-free survival, metastasis-free survival (MFS), and cancer-specific survival (CSS) stratified by STAR-CAP stage groups over 10 yr, calculated the area under the receiver operating characteristics curve (AUC), and performed decision curve analyses to assess the ability of STAR-CAP to predict these outcomes after fitting the data from our single-institution data set. STAR-CAP performed well in stratifying individual survival outcomes for BCR-free survival, MFS, and CSS for each stage group in Kaplan-Meier analyses (p < 0.001 between groups). The AUC for prediction of BCR, metastasis, and CSM at 10 yr was 0.73, 0.84, and 0.75, respectively. Our findings validate the performance of STAR-CAP for European patients treated with RP. PATIENT SUMMARY: We validated the STAR-CAP system for predicting cancer outcomes after removal of the prostate. Our results show that the system performs well and could help in counseling patients with prostate cancer.

Validation of the new STAR-CAP prognostic group staging system in prostate cancer patients treated with radiation therapy

PURPOSE

To externally validate the STAR-CAP prognostic system for prostate cancer (PCa) and compare it to the CAPRA score to predict for biochemical recurrence (BCR) after radiation therapy (RTx).

METHODS

We included patients treated with RTx between 2002 and 2021 for non-metastatic PCa at our institution. BCR was defined based on Phoenix criteria. The 5-year BCR-free survival was assessed by univariable Kaplan-Meier analyses and log-rank test. Multivariable Cox regression models tested the independent association of each model for BCR. Performance of both models to predict 5-year BCR-free survival was assessed using the area under the curve (AUC).

RESULTS

The 2768 patients included were treated with high dose rate brachytherapy (13.3%) as a boost to external beam radiation therapy (EBRT), low dose rate seed brachytherapy (50.4%) or EBRT alone (35.9%). 14.4% of patients received concomitant androgen deprivation therapy (ADT). 222 patients experienced BCR (8%), with a median follow-up of 56 months. The 5-year BCR-free survival ranged from 88 (high risk) to 96% (low risk) in the STAR-CAP classification, and from 87 (high risk) to 97% (low risk) in the CAPRA system (p < 0.0001). Multivariate analyses, adjusted for ADT and type of treatment, confirmed the intrinsic ability of risk stratifications within each system to predict BCR (p < 0.001). Finally, AUC for the 5-year BCR prediction was 0.65 for STAR-CAP and 0.68 for CAPRA.

CONCLUSION

Both CAPRA and STAR-CAP prognostic group staging systems provide sufficient stratification and their predictive ability for 5-year BCR-free survival is comparable, with a small advantage for CAPRA (3%).

Current State of Personalized Genitourinary Cancer Radiotherapy in the Era of Precision Medicine

Radiation therapy plays a crucial role for the management of genitourinary malignancies, with technological advancements that have led to improvements in outcomes and decrease in treatment toxicities. However, better risk-stratification and identification of patients for appropriate treatments is necessary. Recent advancements in imaging and novel genomic techniques can provide additional individualized tumor and patient information to further inform and guide treatment decisions for genitourinary cancer patients. In addition, the development and use of targeted molecular therapies based on tumor biology can result in individualized treatment recommendations. In this review, we discuss the advances in precision oncology techniques along with current applications for personalized genitourinary cancer management. We also highlight the opportunities and challenges when applying precision medicine principles to the field of radiation oncology. The identification, development and validation of biomarkers has the potential to personalize radiation therapy for genitourinary malignancies so that we may improve treatment outcomes, decrease radiation-specific toxicities, and lead to better long-term quality of life for GU cancer survivors.

Population-Based Comparison of Different Risk Stratification Systems Among Prostate Cancer Patients

Background

It is not known which risk stratification system has the best discrimination ability for predicting prostate cancer death.

Methods

We identified patients with non-metastatic primary prostate adenocarcinoma diagnosis between 2004 and 2015 using the Surveillance, Epidemiology, and End Results database. Patients were categorized in different risk groups using the three frequently used risk stratification systems of the National Comprehensive Cancer Network guideline (NCCN-g), American Urological Association guideline (AUA-g), and European Association of Urology guideline (EAU-g), respectively. Associations between risk classification and prostate cancer-specific mortality (PCSM) were determined using Kaplan–Meier analyses and multivariable regression with Cox proportional hazards model. Area under the receiver operating characteristics curve (AUC) analyses were used to test the discrimination ability of the three risk grouping systems.

Results

We analyzed 310,062 patients with a median follow-up of 61 months. A total of 36,368 deaths occurred, including 6,033 prostate cancer deaths. For all the three risk stratification systems, the risk groups were significantly associated with PCSM. The AUC of the model relying on NCCN-g, AUA-g, and EAU-g risk stratification systems for PCSM at specifically 8 years were 0.818, 0.793, and 0.689 in the entire population; 0.819, 0.795, and 0.691 in Whites; 0.802, 0.777, and 0.681 in Blacks; 0.862, 0.818, and 0.714 in Asians; 0.845, 0.806, and 0.728 in Chinese patients. Regardless of the age, marital status, socioeconomic status, and treatment modality, AUC of the model relying on NCCN-g and AUA-g for PCSM was greater than that relying on EAU-g; AUC of the model relying on NCCN-g system was greater than that of the AUA-g system.

Conclusions

The NCCN-g and AUA-g risk stratification systems perform better in discriminating PCSM compared to the EAU-g system. The discrimination ability of the NCCN-g system was better than that of the AUA-g system. It is recommended to use NCCN-g to evaluate risk groups for prostate cancer patients and then provide more appropriate corresponding treatment recommendations.

Prognostic Index for Predicting Prostate Cancer Survival in a Randomized Screening Trial: Development and Validation

We developed and validated a prognostic index to predict survival from prostate cancer (PCa) based on the Finnish randomized screening trial (FinRSPC). Men diagnosed with localized PCa (N = 7042) were included. European Association of Urology risk groups were defined. The follow-up was divided into three periods (0-3, 3-9 and 9-20 years) for development and two corresponding validation periods (3-6 and 9-15 years). A multivariable complementary log-log regression model was used to calculate the full prognostic index. Predicted cause-specific survival at 10 years from diagnosis was calculated for the control arm using a simplified risk score at diagnosis. The full prognostic index discriminates well men with PCa with different survival. The area under the curve (AUC) was 0.83 for both the 3-6 year and 9-15 year validation periods. In the simplified risk score, patients with a low risk score at diagnosis had the most favorable survival, while the outcome was poorest for the patients with high risk scores. The prognostic index was able to distinguish well between men with higher and lower survival, and the simplified risk score can be used as a basis for decision making.