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BACKGROUND: To assess cancer-specific mortality (CSM) and other-cause mortality (OCM) rates in patients with rare histological prostate cancer subtypes. METHODS: Using the Surveillance, Epidemiology, and End Results database (2004-2020), we applied smoothed cumulative incidence plots and competing risks regression (CRR) models. RESULTS: Of 827,549 patients, 1510 (0.18%) harbored ductal, 952 (0.12%) neuroendocrine, 462 (0.06%) mucinous, and 95 (0.01%) signet ring cell carcinoma. In the localized stage, five-year CSM vs. OCM rates ranged from 2 vs. 10% in acinar and 3 vs. 8% in mucinous, to 55 vs. 19% in neuroendocrine carcinoma patients. In the locally advanced stage, five-year CSM vs. OCM rates ranged from 5 vs. 6% in acinar, to 14 vs. 16% in ductal, and to 71 vs. 15% in neuroendocrine carcinoma patients. In the metastatic stage, five-year CSM vs. OCM rates ranged from 49 vs. 15% in signet ring cell and 56 vs. 16% in mucinous, to 63 vs. 9% in ductal and 85 vs. 12% in neuroendocrine carcinoma. In multivariable CRR, localized neuroendocrine (HR 3.09), locally advanced neuroendocrine (HR 9.66), locally advanced ductal (HR 2.26), and finally metastatic neuroendocrine carcinoma patients (HR 3.57; all p < 0.001) exhibited higher CSM rates relative to acinar adenocarcinoma patients. CONCLUSIONS: Compared to acinar adenocarcinoma, patients with neuroendocrine carcinoma of all stages and locally advanced ductal carcinoma exhibit higher CSM rates. Conversely, CSM rates of mucinous and signet ring cell adenocarcinoma do not differ from those of acinar adenocarcinoma.
ABSTRACT
The overall survival (OS) improvement after the advent of several novel systemic therapies, designed for treatment of metastatic urothelial carcinoma of the urinary bladder (mUCUB), is not conclusively studied in either contemporary UCUB patients and/or non-UCUB patients. Within the Surveillance, Epidemiology, and End Results database, contemporary (2017-2020) and historical (2000-2016) systemic therapy-exposed metastatic UCUB and, subsequently, non-UCUB patients were identified. Separate Kaplan-Meier and multivariable Cox regression (CRM) analyses first addressed OS in mUCUB and, subsequently, in metastatic non-UCUB (mn-UCUB). Of 3443 systemic therapy-exposed patients, 2725 (79%) harbored mUCUB versus 709 (21%) harbored mn-UCUB. Of 2725 mUCUB patients, 582 (21%) were contemporary (2017-2020) versus 2143 (79%) were historical (2000-2016). In mUCUB, median OS was 11 months in contemporary versus 8 months in historical patients (Δ = 3 months; p < .0001). After multivariable CRM, contemporary membership status (2017-2020) independently predicted lower overall mortality (OM; hazard ratio [HR] = 0.68, 95% confidence interval [CI] = 0.60-0.76; p < .001). Of 709 mn-UCUB patients, 167 (24%) were contemporary (2017-2020) and 542 (76%) were historical (2000-2016). In mn-UCUB, median OS was 8 months in contemporary versus 7 months in historical patients (Δ = 1 month; p = .034). After multivariable CRM, contemporary membership status (2017-2020) was associated with HR of 0.81 (95% CI = 0.66-1.01; p = .06). In conclusion, contemporary systemic therapy-exposed metastatic patients exhibited better OS in UCUB. However, the magnitude of survival benefit was threefold higher in mUCUB and approximated the survival benefits recorded in prospective randomized trials of novel systemic therapies.
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BACKGROUND: Radiotherapy (RT) represents an alternative treatment option for patients with T1 squamous cell carcinoma of the penis (SCCP), with proven feasibility and tolerability. However, it has never been directly compared with partial penectomy (PP) using cancer-specific mortality (CSM) as an end point. METHODS: In the Surveillance, Epidemiology, and End Results database (2000-2020), T1N0M0 SCCP patients treated with RT or PP were identified. This study relied on 1:4 propensity score-matching (PSM) for age at diagnosis, tumor stage, and tumor grade. Subsequently, cumulative incidence plots as well as multivariable competing risks regression (CRR) models addressed CSM. Additionally, the study accounted for the confounding effect of other-cause mortality (OCM). RESULTS: Of 895 patients with T1N0M0 SCCP, 55 (6.1%) underwent RT and 840 (93.9%) underwent PP. The RT and PP patients had a similar age distribution (median age, 70 vs 70 years) and more frequently harbored grade I or II tumors (67.3% vs 75.8%) as well as T1a-stage disease (67.3% vs 74.3%). After 1:4 PSM, 55 (100%) of the 55 RT patients versus 220 (26.2%) of the 840 PP patients were included in the study. The 10-year CSM derived from the cumulative incidence plots was 25.4% for RT and 14.4% for PP. In the multivariable CRR models, RT independently predicted a higher CSM than PP (hazard ratio, 1.99; 95% confidence interval, 1.05-3.80; p = 0.04). CONCLUSION: For the T1N0M0 SCCP patients treated in the community, RT was associated with nearly a twofold higher CSM than PP. Ideally, a validation study based on tertiary care institution data should be conducted to test whether this CSM disadvantage is operational only in the community or not.
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BACKGROUND: Histopathological examination, a cornerstone in diagnosing cancer, faces challenges due to its time-consuming nature. This review explores the potential of ex-vivo fluorescent confocal microscopy (FCM) in urology, addressing the need for real-time pathological assessment, particularly in prostate cancer. This systematic review aims to assess the applications of FCM in urology, including its role in prostate cancer diagnosis, surgical margin assessment, and other urological fields. METHODS: Following Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, a systematic search of PubMed and SCOPUS was conducted, focusing on English written original articles published after January 1, 2018, discussing the use of FCM in urological practice. The search included keywords related to FCM and urological terms. The risk of bias assessment was performed using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. RESULTS: A total of 17 relevant studies were included in the review that focuses on three main urological issues: prostate cancer (15 articles), bladder cancer (1 article), and renal biopsy (1 article). FCM exhibited significant promise in diagnosing prostate cancer. These studies reported an accuracy range of 85.33% to 95.1% in distinguishing between cancerous and non-cancerous prostate tissues. Moreover, FCM proved valuable for assessing surgical margins in real-time during radical prostatectomy, reducing the need for frozen section analysis. In some investigations, researchers explored the integration of artificial intelligence (AI) with FCM to automate diagnostic processes. Concerning bladder cancer, FCM played a beneficial role in evaluating urethral and ureteral margins during radical cystectomy. Notably, it showed substantial agreement with conventional histopathology and frozen section examination. In the context of renal biopsy, FCM demonstrated the potential to differentiate normal renal parenchyma from cancerous tissue, although the available evidence is limited in this area. The main limitation of the current study is the scarcity of data regarding the topic of interest. CONCLUSIONS: Ex-vivo FCM holds promise in urology, particularly in prostate cancer diagnosis and surgical margin assessment. Its real-time capabilities may reduce diagnostic delays and patient stress. However, most studies remain experimental, requiring further research to validate clinical utility.
ABSTRACT
The prevalence of renal cell carcinoma (RCC) is increasing due to advanced imaging techniques. Surgical resection is the standard treatment, involving complex radical and partial nephrectomy procedures that demand extensive training and planning. Furthermore, artificial intelligence (AI) can potentially aid the training process in the field of kidney cancer. This review explores how artificial intelligence (AI) can create a framework for kidney cancer surgery to address training difficulties. Following PRISMA 2020 criteria, an exhaustive search of PubMed and SCOPUS databases was conducted without any filters or restrictions. Inclusion criteria encompassed original English articles focusing on AI's role in kidney cancer surgical training. On the other hand, all non-original articles and articles published in any language other than English were excluded. Two independent reviewers assessed the articles, with a third party settling any disagreement. Study specifics, AI tools, methodologies, endpoints, and outcomes were extracted by the same authors. The Oxford Center for Evidence-Based Medicine's evidence levels were employed to assess the studies. Out of 468 identified records, 14 eligible studies were selected. Potential AI applications in kidney cancer surgical training include analyzing surgical workflow, annotating instruments, identifying tissues, and 3D reconstruction. AI is capable of appraising surgical skills, including the identification of procedural steps and instrument tracking. While AI and augmented reality (AR) enhance training, challenges persist in real-time tracking and registration. The utilization of AI-driven 3D reconstruction proves beneficial for intraoperative guidance and preoperative preparation. Artificial intelligence (AI) shows potential for advancing surgical training by providing unbiased evaluations, personalized feedback, and enhanced learning processes. Yet challenges such as consistent metric measurement, ethical concerns, and data privacy must be addressed. The integration of AI into kidney cancer surgical training offers solutions to training difficulties and a boost to surgical education. However, to fully harness its potential, additional studies are imperative.
