Focused UTUC pathways with a risk-stratified approach to diagnostic ureteroscopy: is it the need of the hour? A retrospective cohort analysis.

INTRODUCTION: Upper urinary tract urothelial cancer is a rare, aggressive variant of urinary tract cancer. There is often delay to diagnosis and management for this entity in view of diagnostic and staging challenges needing additional investigations and risk stratifications for improved outcomes. In this article, we share our experience in developing a dedicated diagnostic and treatment pathway for UTUC and assess its impact on time lines to radical nephroureterectomy (RNU). We also evaluate the impact of diagnostic ureteroscopy (DUR) on UTUC care pathways timelines. MATERIALS AND METHODS: A prospective database was maintained for all patients who underwent a RNU from January 2015 to August 2022 in a high-volume single tertiary care centre in the UK. In 2019, a Focused UTUC pathway (FUP) was implemented at the centre to streamline diagnostic and RNU pathways. A retrospective analysis of the database was conducted to compare time lines and diagnostic trends between the pre-FUP and FUP cohorts. Primary outcome measures were time to RNU from MDT. Secondary outcome measures were: impact of DUR on time to RNU from MDT and negative UTUC rates between DUR and non-DUR cohorts. Differences in continuous variables across categories were assessed using the independent sample t test. Categorical variables between cohorts were analysed using the chi-square (χ2). Statistical significance in this study was set as p < 0.05. RESULTS: A total of 500 patients with complete data were included in the analysis. The pre-FUP and FUP cohorts consisted of 313 patients and 187 patients, respectively. The overall cohort had a mean age (SD) of 70 years (9.3). 66% of the overall cohort were males. The median time to RNU from MDT in the FUP was significantly lower compared to the pre-FUP cohort; 62 days (IQR 59) vs. 48 days (IQR 41.5), p < 0.0001. The median time to RNU from MDT in patients who underwent a diagnostic URS in the FUP cohort was significantly lower compared to the pre-FUP cohort; 78.5 days (IQR 54.8) vs. 68 days (IQR 48), p-NS. The non-UTUC rates in the DUR and non-DUR cohorts were 6/248 (2.4%) and 14/251 (5.6%), respectively (NS). CONCLUSION: In this series, we illustrate the effectiveness of integrating a multidisciplinary approach with specialised personnel, ring-fenced clinics, efficient diagnostic assessment and optimised theatre capacity. By adopting a risk-stratified approach to diagnostic ureteroscopy, we have achieved a significant reduction in time to RNU.

Catalyzing Precision Medicine: Artificial Intelligence Advancements in Prostate Cancer Diagnosis and Management.

BACKGROUND: The aim was to analyze the current state of deep learning (DL)-based prostate cancer (PCa) diagnosis with a focus on magnetic resonance (MR) prostate reconstruction; PCa detection/stratification/reconstruction; positron emission tomography/computed tomography (PET/CT); androgen deprivation therapy (ADT); prostate biopsy; associated challenges and their clinical implications. METHODS: A search of the PubMed database was conducted based on the inclusion and exclusion criteria for the use of DL methods within the abovementioned areas. RESULTS: A total of 784 articles were found, of which, 64 were included. Reconstruction of the prostate, the detection and stratification of prostate cancer, the reconstruction of prostate cancer, and diagnosis on PET/CT, ADT, and biopsy were analyzed in 21, 22, 6, 7, 2, and 6 studies, respectively. Among studies describing DL use for MR-based purposes, datasets with magnetic field power of 3 T, 1.5 T, and 3/1.5 T were used in 18/19/5, 0/1/0, and 3/2/1 studies, respectively, of 6/7 studies analyzing DL for PET/CT diagnosis which used data from a single institution. Among the radiotracers, [68Ga]Ga-PSMA-11, [18F]DCFPyl, and [18F]PSMA-1007 were used in 5, 1, and 1 study, respectively. Only two studies that analyzed DL in the context of DT met the inclusion criteria. Both were performed with a single-institution dataset with only manual labeling of training data. Three studies, each analyzing DL for prostate biopsy, were performed with single- and multi-institutional datasets. TeUS, TRUS, and MRI were used as input modalities in two, three, and one study, respectively. CONCLUSION: DL models in prostate cancer diagnosis show promise but are not yet ready for clinical use due to variability in methods, labels, and evaluation criteria. Conducting additional research while acknowledging all the limitations outlined is crucial for reinforcing the utility and effectiveness of DL-based models in clinical settings.