Being Transparent About Brilliant Failures:An Attempt to Use Real-World Data in a Disease Model for Patients with Castration-Resistant Prostate Cancer

Background: Real-world disease models spanning multiple treatment lines can provide insight into the (cost) effectiveness of treatment sequences in clinical practice. Objective: Our objective was to explore whether a disease model based solely on real-world data (RWD) could be used to estimate the effectiveness of treatments for patients with castration-resistant prostate cancer (CRPC) that could then be suitably used in a cost-effectiveness analysis. Methods: We developed a patient-level simulation model using patient-level data from the Dutch CAPRI registry as input parameters. Time to event (TTE) and overall survival (OS) were estimated with multivariate regression models, and type of event (i.e., next treatment or death) was estimated with multivariate logistic regression models. To test internal validity, TTE and OS from the simulation model were compared with the observed outcomes in the registry. Results: Although patient characteristics and survival outcomes of the simulated data were comparable to those in the observed data (median OS 20.6 vs. 19.8 months, respectively), the disease model was less accurate in estimating differences between treatments (median OS simulated vs. observed population: 18.6 vs. 17.9 [abiraterone acetate plus prednisone], 24.0 vs. 25.0 [enzalutamide], 20.2 vs. 18.7 [docetaxel], and 20.0 vs. 23.8 months [radium-223]). Conclusions: Overall, the disease model accurately approximated the observed data in the total CRPC population. However, the disease model was unable to predict differences in survival between treatments due to unobserved differences. Therefore, the model is not suitable for cost-effectiveness analysis of CRPC treatment. Using a combination of RWD and data from randomised controlled trials to estimate treatment effectiveness may improve the model

Third-line Life-prolonging Drug Treatment in a Real-world Metastatic Castration-resistant Prostate Cancer Population:Results from the Dutch Castration-resistant Prostate Cancer Registry

Third-line life-prolonging drugs (LPDs) might not be appropriate for all metastatic castration-resistant prostate cancer patients. We developed a prognostic model and identified a high-risk subgroup in which no meaningful benefit from third-line LPDs is derived in clinical practice

Real-world Outcomes of Sequential Androgen-receptor Targeting Therapies with or Without Interposed Life-prolonging Drugs in Metastatic Castration-resistant Prostate Cancer:Results from the Dutch Castration-resistant Prostate Cancer Registry

BACKGROUND: Cross resistance between androgen-receptor targeting therapies (ARTs) (abiraterone acetate plus prednisone [ABI+P] or enzalutamide [ENZ]) for treatment of metastatic castration-resistant prostate cancer (mCRPC) may affect responses to second ART (ART2). OBJECTIVE: To establish treatment duration and prostate-specific antigen (PSA) response of ART2 in real-world mCRPC patients treated with or without other life-prolonging drugs (LPDs; ie, docetaxel, cabazitaxel, or radium-223) between ART1 and ART2. DESIGN, SETTING, AND PARTICIPANTS: Castration-resistant prostate cancer patients, diagnosed between 2010 and 2016 were retrospectively registered in Castration-resistant Prostate Cancer Registry (CAPRI). Patients treated with both ARTs were clustered into two subgroups: ART1>ART2 or ART1>LPD>ART2. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Outcomes were ≥50% PSA response and treatment duration of ART2. Descriptive statistics and binary logistic regression after multiple imputations were performed. RESULTS AND LIMITATIONS: A total of 273 patients were included with a median follow-up of 8.4 mo from ART2. Patients with ART1>ART2 were older and had favourable prognostic characteristics at ART2 baseline compared with patients with ART1>LPD>ART2. No differences between ART1>ART2 and ART1>LPD>ART2 were found in PSA response and treatment duration. Multivariate analysis suggested that PSA response of ART2 was less likely in patients with visceral metastases (odds ratio [OR] 0.143, p=0.04) and more likely in patients with a relatively longer duration of androgen-deprivation treatment (OR 1.028, p=0.01) and with ABI + P before ENZ (OR 3.192, p=0.02). A major limitation of this study was missing data, a common problem in retrospective observational research. CONCLUSIONS: The effect of ART2 seems to be low, with a low PSA response rate and a short treatment duration irrespective of interposed chemotherapy or radium-223, especially in patients with short time on castration, visceral disease, and ENZ before ABI+P. PATIENT SUMMARY: We observed no differences in outcomes of patients treated with sequential abiraterone acetate plus prednisone (ABI+P) and enzalutamide (ENZ) with or without interposed chemotherapy or radium-223. In general, outcomes were lower than those in randomised trials, questioning the additional effect of second treatment with ABI+P or ENZ in daily practice

Dual-Phase PET-CT to Differentiate [F-18]Fluoromethylcholine Uptake in Reactive and Malignant Lymph Nodes in Patients with Prostate Cancer

PURPOSE: To investigate whether time-trends of enhanced [(18)F]Fluoromethylcholine ([(18)F]FCH) in lymph nodes (LN) of prostate cancer (PCa) patients can help to discriminate reactive from malignant ones, and whether single time point standardized uptake value (SUV) measurements also suffice. PROCEDURES: 25 PCa patients with inguinal (presumed benign) and enlarged pelvic LN (presumed malignant) showing enhanced [(18)F]FCH uptake at dual-phase PET-CT were analyzed. Associations between LN status (benign versus malignant) and SUV(max) and SUV(meanA50), determined at 2 min (early) and 30 min (late) post injection, were assessed. We considered two time-trends of [(18)F]FCH uptake: type A (SUV early > SUV late) and type B (SUV late ≥ SUV early). Histopathology and/or follow-up were used to confirm the assumption that LN with type A pattern are benign, and LN with type B pattern malignant. RESULTS: Analysis of 54 nodes showed that LN status, time-trends, and 'late' (30 min p.i.) SUV(max) and SUV(meanA50) parameters were strongly associated (P<0.0001). SUV(max) relative difference was the best LN status predictor. All but one inguinal LN showed a decreasing [(18)F]FCH uptake over time (pattern A), while 95% of the pelvic nodes presented a stable or increasing uptake (pattern B) type. CONCLUSIONS: Time-trends of enhanced [(18)F]FCH uptake can help to characterize lymph nodes in prostate cancer patients. Single time-point SUV measurements, 30 min p.i., may be a reasonable alternative for predicting benign versus malignant status of lymph nodes, but this remains to be validated in non-enlarged pelvic lymph nodes

Patient reported outcome measures concerning urinary incontinence after robot assisted radical prostatectomy: development and validation of an online prediction model using clinical parameters, lower urinary tract symptoms and surgical experience

The prediction of post-prostatectomy incontinence (PPI) after robot-assisted radical prostatectomy (RARP) depends on multiple clinical, anatomical and surgical factors. There are only few risk formulas, tables or nomograms predicting PPI that may assist clinicians and their patients in adequate risk counseling on postoperative side-effects. Prospective data collection of 1814 patients who underwent RARP between 2009 and 2017 was done. Pre-operative parameters were age, body mass index (BMI), prostate volume, the American Society of Anesthesiologists (ASA) score, severity of Lower Urinary Tract Symptoms (LUTS), type of planned nerve-sparing surgery and surgical experience. The continence status was reported using Patient Reported Outcome Measurements (PROMs) using the validated pad-use questionnaire EPIC26. Continence was defined as either the use of zero pads or one safety pad. Multivariable logistic regression analysis was performed to identify predictors of PPI within one year after RARP. An online prediction tool was developed and validated. The median follow-up was 36 months (range 12–108). The response rate was high at 85.2%. A total of 85% (1537/1814) of patients was continent on follow-up. One-year continence rate was 80.1% (95% CI 78.3–81.9%) (1453/1814) and increased to 87.4% (95% CI 85.4–89.4%) after 5 years. On multivariable analysis, severity of LUTS (OR = 0.56 p = 0.004), higher age (OR = 0.73 p = 0.049), extend of nerve-sparing surgery (OR = 0.60 p = 0.001) and surgeon experience (OR = 1.48 p = 0.025) were significant independent predictors for PPI. The online prediction model performed well in predicting continence status with poor discrimination and good calibration. An intuitive online tool was developed to predict PPI after RARP that may assist clinicians and their patients in counseling of treatment

Adding multiparametric MRI to the MSKCC and Partin nomograms for primary prostate cancer: Improving local tumor staging?

INTRODUCTION AND OBJECTIVES: As a single diagnostic modality, multiparametric MRI (mpMRI) has imperfect accuracy to detect locally advanced prostate cancer (T-stages 3-4). In this study we evaluate if combining mpMRI with preoperative nomograms (Memorial Sloan Kettering Cancer Center [MSKCC] and Partin) improves the prediction of locally advanced tumors. MATERIALS AND METHODS: Preoperative mpMRI results of 430 robot-assisted radical prostatectomy patients were analyzed. MSKCC and Partin nomogram scores predicting extraprostatic growth were calculated. Logistic regression analysis was performed, combining the nomogram prediction scores with mpMRI results. The diagnostic value of the combined models was evaluated by creating receiver operator characteristics curves and comparing the area under the curve (AUC). RESULTS: mpMRI was a significant predictor of locally advanced disease in addition to both the MSKCC and Partin nomogram, despite its low sensitivity (45.3%). However, overall predictive accuracy increased by only 1% when mpMRI was added to the MSKCC nomogram (AUC MSKCC 0.73 vs MSKCC + mpMRI 0.74). Predictive accuracy for the Partin Tables increased 4% (AUC Partin 0.62 vs Partin + mpMRI 0.66). CONCLUSION: The addition of mpMRI to the preoperative MSKCC and Partin nomograms did not increase diagnostic accuracy for the prediction of locally advanced prostate cancer

Optimization and validation of 18F-DCFPyL PET radiomics-based machine learning models in intermediate- to high-risk primary prostate cancer.

IntroductionRadiomics extracted from prostate-specific membrane antigen (PSMA)-PET modeled with machine learning (ML) may be used for prediction of disease risk. However, validation of previously proposed approaches is lacking. We aimed to optimize and validate ML models based on 18F-DCFPyL-PET radiomics for the prediction of lymph-node involvement (LNI), extracapsular extension (ECE), and postoperative Gleason score (GS) in primary prostate cancer (PCa) patients.MethodsPatients with intermediate- to high-risk PCa who underwent 18F-DCFPyL-PET/CT before radical prostatectomy with pelvic lymph-node dissection were evaluated. The training dataset included 72 patients, the internal validation dataset 24 patients, and the external validation dataset 27 patients. PSMA-avid intra-prostatic lesions were delineated semi-automatically on PET and 480 radiomics features were extracted. Conventional PET-metrics were derived for comparative analysis. Segmentation, preprocessing, and ML methods were optimized in repeated 5-fold cross-validation (CV) on the training dataset. The trained models were tested on the combined validation dataset. Combat harmonization was applied to external radiomics data. Model performance was assessed using the receiver-operating-characteristics curve (AUC).ResultsThe CV-AUCs in the training dataset were 0.88, 0.79 and 0.84 for LNI, ECE, and GS, respectively. In the combined validation dataset, the ML models could significantly predict GS with an AUC of 0.78 (p0.05) and ECE (0.66, p>0.05), but a lower AUC for GS (0.73, pConclusionIn internal and external validation, 18F-DCFPyL-PET radiomics-based ML models predicted high postoperative GS but not LNI or ECE in intermediate- to high-risk PCa. Therefore, the clinical benefit seems to be limited. These results underline the need for external and/or multicenter validation of PET radiomics-based ML model analyses to assess their generalizability

Detection of Recurrent Prostate Cancer Using Prostate-specific Membrane Antigen Positron Emission Tomography in Patients not Meeting the Phoenix Criteria for Biochemical Recurrence After Curative Radiotherapy

Biochemical recurrence of prostate cancer (PCa) after curative radiotherapy is defined as a prostate-specific antigen (PSA) rise of ≥2 ng/ml above the nadir ("Phoenix criteria", 2005). With the introduction of prostate-specific membrane antigen positron emission tomography (PSMA-PET), the ability to localise PCa recurrences has increased markedly. Here, we reviewed 315 patients scanned with PSMA-PET after curative radiotherapy in the Prostate Cancer Network Amsterdam (2015-2018). Sixty-three patients (20.3%) were scanned below the Phoenix threshold (PSA rise <2.0 ng/ml). In 53 of these patients (84.1%), PSMA-PET-avid lesions were detected nonetheless: 21 patients (33.3%) revealed a local recurrence as a single site of disease, 32 patients (50.8%) harboured metastatic PCa. Besides rising PSA, no predictors were identified that prompted early PSMA-PET imaging. In this communication, we report on the frequent detection of metastatic PCa with PSMA-PET in men below the Phoenix PSA threshold. These findings are a plea for re-evaluation of current diagnostic work-up for rising PSA values after radiotherapy, as early detection of recurrences might refine salvage and/or adjuvant therapies. PATIENT SUMMARY: This study reports on the unexpected detection of prostate cancer (PCa) recurrences with prostate-specific membrane antigen positron emission tomography in patients treated with radiotherapy. This calls for re-evaluation of the current criteria for recurrent PCa after radiotherapy