Cystoscopy

Purpose:The aim of this study is to determine the intrathecal Bupivacaine concentration(0.5% vs 0.75%) with better anesthesia and little hemodynamic disturbance during cystoscopic procedures.In comparing these bupivacaine concentrations ,the result would be expected to enhance safer spinal anesthesia practices ,minimizing the use of vasopressors in urological surgeries.OBJECTIVE:To compare and analyze the frequency and average chages of post spinal hemodynamic instability in terms of hypotension and bradycardia with 0.75% hyperbaric bupivacaine and 0.55 hyperbaric bupivacaine in patients presenting for cystoscopic procedures.

Non-muscle-invasive bladder cancer (NMIBC) is usually treated with surgery to remove the tumor (transurethral resection of bladder tumor, or TURBT), often followed by bladder-instilled medications to reduce the chance of the cancer coming back. Even with this treatment, high-grade NMIBC can return or progress, so patients need regular check-ups, usually with cystoscopy (a camera examination of the bladder) and urine cytology. Cystoscopy is effective but invasive, can cause discomfort, and carries risks such as infection and bleeding. This makes follow-up costly and sometimes burdensome for patients. This study is testing whether a urine tumor DNA (utDNA) test - a type of "liquid biopsy" that detects cancer-related DNA changes in urine - can help guide the timing of cystoscopy for people with high-risk or very high-risk NMIBC. utDNA testing is non-invasive and has shown high accuracy in detecting bladder cancer, sometimes spotting signs of recurrence earlier than standard methods. By combining utDNA testing with cystoscopy, we hope to safely reduce the number of unnecessary cystoscopies without missing cancer recurrences. The study will evaluate whether this approach can make bladder cancer follow-up more comfortable, more precise, and more efficient.

The goal of this observational study is to further validate the sensitivity and specificity of Bladder EpiCheck in primary detection of urothelial carcinoma in participants aged 45 years or older presenting with haematuria, compared to cystoscopy and pathology, if performed. Participants will provide a voided urine sample, and data from standard of care haematuria work-up will be collected.

Main objectives: Qualitative and quantitative monitoring of recurrences in patients with a previous diagnosis of high-grade bladder cancer at high risk of persistence/recurrence. Endpoints: Presence or absence of mRNA in urine with a dichotomous result; concordance between Xpert BM and histopathological examination Clinical relevance: reduces by half the number of (invasive) cystoscopies during follow-up. The non-invasive nature of the test could improve patient compliance with follow-up. Interventional study because it would reduce by half the number of cystoscopies during follow-up of bladder cancer which is considered the gold standard in the follow-up of this tumor. However, these markers are already CE validated and described in the European guidelines and for this reason the risk would be low.

Today the standard for the diagnosis and monitoring of bladder tumors is bladder endoscopy. The performance of this exam is not perfect. With this work, based on artificial intelligence, the investigators wish to combine endoscopy with a complementary diagnostic tool in order to improve patient care. The main objective will be to reduce diagnostic errors / wanderings in patients treated or followed for bladder tumors, by imposing a new standard of diagnostic bladder mapping (high PPV and VPN, high precision)(primary purpose diagnostic). The secondary objective will be to homogenize and systematize the descriptive part of the lesions, and to use AI to better characterize tumor aggressiveness. The final objective being to validate a new precision tool (diagnostic companion) essential for developing and standardizing the therapeutic management of bladder tumors (correcting inter-observer heterogeneity). In this project, video frame will be first extracted from our dataset of cystoscopy videos hosted in in the Next Cloud Recherche. Selected medical image will be segmented and analyzed using our pre-trained CNN model with a feature detection algorithm to obtain features. Data will be analyzed on both patient and lesion levels. The study will assess the Bladder-PAD accuracy on the detection of bladder tumors, and its ability to predict tumor risk of recurrence and progression.