Tumor distribution affects bladder recurrence but not survival outcome of multifocal upper tract urothelial carcinoma treated with radical nephroureterectomy

European Association of Urology Guidelines on Upper Urinary Tract Urothelial Carcinoma: 2023 Update

CONTEXT

The European Association of Urology (EAU) guidelines panel on upper urinary tract urothelial carcinoma (UTUC) has updated the guidelines to aid clinicians in evidence-based management of UTUC.

OBJECTIVE

To provide an overview of the EAU guidelines on UTUC as an aid to clinicians.

EVIDENCE ACQUISITION

The recommendations provided in these guidelines are based on a review of the literature via a systematic search of the PubMed, Ovid, EMBASE, and Cochrane databases. Data were searched using the following keywords: urinary tract cancer, urothelial carcinomas, renal pelvis, ureter, bladder cancer, chemotherapy, ureteroscopy, nephroureterectomy, neoplasm, (neo)adjuvant treatment, instillation, recurrence, risk factors, metastatic, immunotherapy, and survival. The results were assessed by a panel of experts.

EVIDENCE SYNTHESIS

Even though data are accruing, for many areas there is still insufficient high-level evidence to provide strong recommendations. Patient stratification on the basis of histology and clinical examination (including imaging) and assessment of patients at risk of Lynch syndrome will aid management. Kidney-sparing management should be offered as a primary treatment option to patients with low-risk UTUC and two functional kidneys. In particular, for patients with high-risk or metastatic UTUC, new treatment options have become available. In high-risk UTUC, platinum-based chemotherapy after radical nephroureterectomy, and adjuvant nivolumab for unfit or patients who decline chemotherapy, are options. For metastatic disease, gemcitabine/carboplatin chemotherapy is recommended as first-line treatment for cisplatin-ineligible patients. Patients with PD-1/PD-L1-positive tumours should be offered a checkpoint inhibitor (pembrolizumab or atezolizumab).

CONCLUSIONS

These guidelines contain information on the management of individual patients according to the current best evidence. Urologists should take into account the specific clinical characteristics of each patient when determining the optimal treatment regimen according to the risk stratification of these tumours.

PATIENT SUMMARY

Cancer of the upper urinary tract is rare, but because 60% of these tumours are invasive at diagnosis, timely and appropriate diagnosis is most important. A number of known risk factors exist.

Clinical Characteristics and Current Status of Treatment for Recurrent Bladder Cancer after Surgeries on Upper Tract Urothelial Carcinoma

Upper tract urothelial carcinoma (UTUC) is a relatively rare, but highly malignant, disease with an estimated annual incidence of 2 cases per 100,000 people. The main surgical treatment modalities for UTUC are radical nephroureterectomy (RNU) with bladder cuff resection. After surgery, intravesical recurrence (IVR) can occur in up to 47% of patients, and 75% of them present with non-muscle invasive bladder cancer (NMIBC). However, there are few studies focused on the diagnosis and treatment of postoperatively recurrent bladder cancer for patients with previous UTUC history (UTUC-BC), and many of the influencing factors are still controversial. In this article, we performed a narrative review of the recent literature, mainly summarizing the factors influencing postoperative IVR in patients with UTUC and discussing the subsequent prevention, monitoring, and treatment tools for it.

Automated predictive analytics tool for rainfall forecasting

Australia faces a dryness disaster whose impact may be mitigated by rainfall prediction. Being an incredibly challenging task, yet accurate prediction of rainfall plays an enormous role in policy making, decision making and organizing sustainable water resource systems. The ability to accurately predict rainfall patterns empowers civilizations. Though short-term rainfall predictions are provided by meteorological systems, long-term prediction of rainfall is challenging and has a lot of factors that lead to uncertainty. Historically, various researchers have experimented with several machine learning techniques in rainfall prediction with given weather conditions. However, in places like Australia where the climate is variable, finding the best method to model the complex rainfall process is a major challenge. The aim of this paper is to: (a) predict rainfall using machine learning algorithms and comparing the performance of different models. (b) Develop an optimized neural network and develop a prediction model using the neural network (c) to do a comparative study of new and existing prediction techniques using Australian rainfall data. In this paper, rainfall data collected over a span of ten years from 2007 to 2017, with the input from 26 geographically diverse locations have been used to develop the predictive models. The data was divided into training and testing sets for validation purposes. The results show that both traditional and neural network-based machine learning models can predict rainfall with more precision.