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Objective: To compare the predictive ability of the modified Frailty Index (mFI) and the revised Risk Analysis Index (RAI-Rev) for perioperative outcomes in patients undergoing major urologic oncologic surgery, aiming to identify the optimal frailty screening tool for surgical risk stratification., Methods: NSQIP was queried to identify patients undergoing radical prostatectomy, partial or radical nephrectomy, or radical cystectomy between 2013 and 2017. We investigated the association of mFI and RAI-Rev with the following 30-day perioperative outcomes using multivariable logistic regression: major complications, Clavien grade ≥4 complications, non-home discharge, 30-day readmission, and all-cause mortality. Receiver-operating characteristic curve analysis compared the predictive performances of the 2 frailty instruments, with differences between the C-statistics assessed using DeLong's test., Results: Among 101,739 patients, 30-day major complication rates varied from 2.40% in prostatectomy to 26.86% in cystectomy, non-home discharge rates ranged from 1.92% to 13.54%, and mortality rates were between 0.16% and 1.43%. RAI-Rev showed higher discriminatory ability for mortality (C-statistic: 0.688-0.798) and non-home discharge (C-statistic: 0.638-0.734) compared to mFI (C-statistic: 0.594-0.677 and 0.593-0.639, respectively). Both frailty indices had similar discriminatory ability for major perioperative complications (C-statistic: 0.531-0.607). DeLong's test confirmed statistically significant differences in C-statistics between RAI-Rev and mFI for mortality (P <.001) and non-home discharge (P <.001) across all surgical cohorts., Conclusion: RAI-Rev may have greater utility as a frailty prognostic tool than mFI among patients undergoing major urologic surgery. Prospective studies and clinical trials exploring frailty should consider these results during trial design., Competing Interests: Declaration of Competing Interest The authors declare that they have no conflict of interest., (Copyright © 2024. Published by Elsevier Inc.)

Background: Retained surgical items (RSI) are preventable events that pose a significant risk to patient safety. Current strategies for preventing RSIs rely heavily on manual instrument counting methods, which are prone to human error. This study evaluates the feasibility and performance of a deep learning-based computer vision model for automated surgical tool detection and counting., Methods: A novel dataset of 1,004 images containing 13,213 surgical tools across 11 categories was developed. The dataset was split into training, validation, and test sets at a 60:20:20 ratio. An artificial intelligence (AI) model was trained on the dataset, and the model's performance was evaluated using standard object detection metrics, including precision and recall. To simulate a real-world surgical setting, model performance was also evaluated in a dynamic surgical video of instruments being moved in real-time., Results: The model demonstrated high precision (98.5%) and recall (99.9%) in distinguishing surgical tools from the background. It also exhibited excellent performance in differentiating between various surgical tools, with precision ranging from 94.0 to 100% and recall ranging from 97.1 to 100% across 11 tool categories. The model maintained strong performance on a subset of test images containing overlapping tools (precision range: 89.6-100%, and recall range 97.2-98.2%). In a real-time surgical video analysis, the model maintained a correct surgical tool count in all non-transition frames, with a median inference speed of 40.4 frames per second (interquartile range: 4.9)., Conclusion: This study demonstrates that using a deep learning-based computer vision model for automated surgical tool detection and counting is feasible. The model's high precision and real-time inference capabilities highlight its potential to serve as an AI safeguard to potentially improve patient safety and reduce manual burden on surgical staff. Further validation in clinical settings is warranted., (© 2024. The Author(s).)

Prostate cancer lung metastasis represents a clinical conundrum due to its implications for advanced disease progression and the complexities it introduces in treatment planning. As the disease progresses to distant sites such as the lung, the clinical management becomes increasingly intricate, requiring tailored therapeutic strategies to address the unique characteristics of metastatic lesions. This review seeks to synthesize the current state of knowledge surrounding prostate cancer metastasis to the lung, shedding light on the diverse array of clinical presentations encountered, ranging from subtle radiological findings to overt symptomatic manifestations. By examining the diagnostic modalities utilized in identifying this metastasis, including advanced imaging techniques and histopathological analyses, this review aims to provide insights into the diagnostic landscape and the challenges associated with accurately characterizing lung metastatic lesions in prostate cancer patients. Moreover, this review delves into the nuances of therapeutic interventions employed in managing prostate cancer lung metastasis, encompassing systemic treatments such as hormonal therapies and chemotherapy, as well as metastasis-directed therapies including surgery and radiotherapy.

Context: The evidence supporting multiparametric magnetic resonance imaging (MRI) targeting for biopsy is nearly exclusively based on biopsy pathologic outcomes. This is problematic, as targeting likely allows preferential identification of small high-grade areas of questionable oncologic significance, raising the likelihood of overdiagnosis and overtreatment., Objective: To estimate the impact of MRI-targeted, systematic, and combined biopsies on radical prostatectomy (RP) grade group concordance., Evidence Acquisition: PubMed MEDLINE and Cochrane Library were searched from July 2018 to January 2022. Studies that conducted systematic and MRI-targeted prostate biopsies and compared biopsy results with pathology after RP were included. We performed a meta-analysis to assess whether pathologic upgrading and downgrading were influenced by biopsy type and a net-benefit analysis using pooled risk difference estimates., Evidence Synthesis: Both targeted only and combined biopsies were less likely to result in upgrading (odds ratio [OR] vs systematic of 0.70, 95% confidence interval [CI] 0.63-0.77, p < 0.001, and 0.50, 95% CI 0.45-0.55, p < 0.001), respectively). Targeted only and combined biopsies increased the odds of downgrading (1.24 (95% CI 1.05-1.46), p = 0.012, and 1.96 (95% CI 1.68-2.27, p < 0.001) compared with systematic biopsies, respectively. The net benefit of targeted and combined biopsies is 8 and 7 per 100 if harms of up- and downgrading are considered equal, but 7 and -1 per 100 if the harm of downgrading is considered twice that of upgrading., Conclusions: The addition of MRI-targeting results in lower rates of upgrading as compared to systematic biopsy at RP (27% vs 42%). However, combined MRI-targeted and systematic biopsies are associated with more downgrading at RP (19% v 11% for combined vs systematic). Strong heterogeneity suggests further research into factors that influence the rates of up- and downgrading and that distinguishes clinically relevant from irrelevant grade changes is needed. Until then, the benefits and harms of combined MRI-targeted and systematic biopsies cannot be fully assessed., Patient Summary: We reviewed the ability of magnetic resonance imaging (MRI)-targeted biopsies to predict cancer grade at prostatectomy. We found that combined MRI-targeted and systematic biopsies result in more cancers being downgraded than systematic biopsies., (Copyright © 2023 European Association of Urology. Published by Elsevier B.V. All rights reserved.)

Purpose: Artificial intelligence is part of our daily life and machine learning techniques offer possibilities unknown until now in medicine. This study aims to offer an evaluation of the performance of machine learning (ML) techniques, for predicting bacterial resistance in a urology department., Methods: Data were retrieved from laboratory information system (LIS) concerning 239 patients with urolithiasis hospitalized in the urology department of a tertiary hospital over a 1-year period (2019): age, gender, Gram stain (positive, negative), bacterial species, sample type, antibiotics and antimicrobial susceptibility. In our experiments, we compared several classifiers following a tenfold cross-validation approach on 2 different versions of our dataset; the first contained only information of Gram stain, while the second had knowledge of bacterial species., Results: The best results in the balanced dataset containing Gram stain, achieve a weighted average receiver operator curve (ROC) area of 0.768 and F-measure of 0.708, using a multinomial logistic regression model with a ridge estimator. The corresponding results of the balanced dataset, that contained bacterial species, achieve a weighted average ROC area of 0.874 and F-measure of 0.783, with a bagging classifier., Conclusions: Artificial intelligence technology can be used for making predictions on antibiotic resistance patterns when knowing Gram staining with an accuracy of 77% and nearly 87% when identifying specific microorganisms. This knowledge can aid urologists prescribing the appropriate antibiotic 24-48 h before test results are known., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Plasma and tumor caveolin-1 (Cav-1) are linked with disease progression in prostate cancer. Here we report that metabolomic profiling of longitudinal plasmas from a prospective cohort of 491 active surveillance (AS) participants indicates prominent elevations in plasma sphingolipids in AS progressors that, together with plasma Cav-1, yield a prognostic signature for disease progression. Mechanistic studies of the underlying tumor supportive onco-metabolism reveal coordinated activities through which Cav-1 enables rewiring of cancer cell lipid metabolism towards a program of 1) exogenous sphingolipid scavenging independent of cholesterol, 2) increased cancer cell catabolism of sphingomyelins to ceramide derivatives and 3) altered ceramide metabolism that results in increased glycosphingolipid synthesis and efflux of Cav-1-sphingolipid particles containing mitochondrial proteins and lipids. We also demonstrate, using a prostate cancer syngeneic RM-9 mouse model and established cell lines, that this Cav-1-sphingolipid program evidences a metabolic vulnerability that is targetable to induce lethal mitophagy as an anti-tumor therapy.

All-trans-retinoic acid (ATRA) can enhance iodine uptake capability of thyroid tumors, but the mechanisms remain poorly understood. The aim of the present study was to investigate the effects of ATRA on isotope susceptibility, proliferation and invasion of anaplastic thyroid carcinoma (ATC) and potential mechanisms. SW1736 cells were treated with 1 µmol/l ATRA or 1% ethanol for 5 days. A cell line stably expressing β-catenin-shRNA was established. An iodine uptake assay was performed using 125 I. Proliferation and invasiveness were tested using MTT and Transwell assays, respectively. Western blotting was used to assess the expression of β-catenin, glycogen synthase kinase-3β (GSK-3β), sodium/iodine symporter (NIS) and proteins involved in epithelial-mesenchymal transition. Cells pretreated with ATRA were injected subcutaneously into SCID mice. Mice were intraperitoneally injected with 131 I once on the first day of treatment, and tumor growth was then assessed. After 35 days of 131 I treatment, ATRA-pretreated tumor volume and weight were decreased compared with the 131 I alone group (163.32±19.57 vs. 332.06±21.37 mm 3 ; 0.35±0.14 vs. 0.67±0.23 g, both P<0.05). Similar results were observed in the β-catenin shRNA-pretreated tumors. ATRA also increased the uptake of iodine by SW1736 cells (P<0.01), and similar results were observed in β-catenin shRNA cells. ATRA treatment decreased the cell proliferation and invasion compared with control cells (all P<0.05), similar to β-catenin shRNA. ATRA treatment decreased the expression of phosphorylated (p-)β-catenin, p-GSK-3β, vimentin, and fibronectin, and increased the expression of NIS and E-cadherin, compared with the control. ATRA increased the iodine uptake and inhibited the proliferation and invasion of SW1736 cells, involving β-catenin phosphorylation. In conclusion, ATRA could be used to improve the isotope sensitivity of ATC.

Cancers with loss-of-function mutations in BRCA1 or BRCA2 are deficient in the DNA damage repair pathway called homologous recombination (HR), rendering these cancers exquisitely vulnerable to poly(ADP-ribose) polymerase (PARP) inhibitors. This functional state and therapeutic sensitivity is referred to as "BRCAness" and is most commonly associated with some breast cancer types. Pharmaceutical induction of BRCAness could expand the use of PARP inhibitors to other tumor types. For example, BRCA mutations are present in only ~20% of prostate cancer patients. We found that castration-resistant prostate cancer (CRPC) cells showed increased expression of a set of HR-associated genes, including BRCA1 , RAD54L , and RMI2 Although androgen-targeted therapy is typically not effective in CRPC patients, the androgen receptor inhibitor enzalutamide suppressed the expression of those HR genes in CRPC cells, thus creating HR deficiency and BRCAness. A "lead-in" treatment strategy, in which enzalutamide was followed by the PARP inhibitor olaparib, promoted DNA damage-induced cell death and inhibited clonal proliferation of prostate cancer cells in culture and suppressed the growth of prostate cancer xenografts in mice. Thus, antiandrogen and PARP inhibitor combination therapy may be effective for CRPC patients and suggests that pharmaceutically inducing BRCAness may expand the clinical use of PARP inhibitors., (Copyright © 2017, American Association for the Advancement of Science.)

The present study investigated whether the efficacy of radioiodine therapy towards aggressive thyroid cancer cells was affected by β-catenin activity and associated with sodium/iodine symporter (NIS) localization. Human thyroid cancer cell line follicular thyroid carcinoma (FTC) 133 was endowed with aggressiveness by HIF-1α or β-catenin overexpression. The protein amount and subcellular localization of NIS, and the radioiodine uptake capacity were detected in the cells, as well as in cells subsequently undergoing β-catenin knockdown. Xenograft experiments were conducted to compare the tumor growth ability and responsiveness to radioactive treatment among HIF-1α and β-catenin overexpressing FTC cells, respectively with or without β-catenin knockdown. β-catenin increased upon HIF-1α overexpression, but not vice versa. This signal axis would prompt metastatic propensity in FTC cells, and translocate NIS from cytomembrane to cytoplasm. Consistently the radioiodine uptake capacity in the cells decreased obviously. Knockdown of β-catenin reversed all these changes. Furthermore, the xenograft experiments showed that radioiodine treatment could thoroughly suppress tumor growth ability of aggressive FTC cells only if the HIF-1α-induced β-catenin activation was disrupted by β-catenin knockdown. β-catenin nuclear translocation in tumor cells was accompanied by abnormal subcellular localization of NIS. Moreover, we found that only after inhibiting β-catenin expression, can the radioiodine treatment promote apoptosis other than repress proliferation and survival in xenograft tumor cells. In conclusion, aggressive FTC cells overexpressing HIF-1α will be fully cracked down by radioiodine therapy once β-catenin expression is inhibited, and regulated localization of NIS may account for underlying mechanisms.

In patients diagnosed with prostate cancer, the selection of treatment, including the type of therapy and its aggressiveness, is often based on a patient's age and life expectancy. Life expectancy estimates are too often calculated solely on the patient's chronological age, overlooking comorbid conditions and their severity, which can greatly affect life expectancy. If, in addition to chronological age, comorbid conditions are used to assess a patient's life expectancy, the most appropriate treatment options are more likely to be selected. Older, healthy patients might be able to tolerate more aggressive treatment than would be administered on the basis of their age alone, and younger patients with numerous comorbid conditions could avoid harsh therapy that might not be appropriate given their current state of health. The key idea to consider in treatment selection is what a patient's quality of life would be like with or without a particular treatment option. In an era of precision medicine, decisions regarding the provision of health care should be made rationally and on the basis of objective estimates of the threat of disease and the benefits and costs of intervention and within the context of the patient's characteristics and desires.