|
|
|
|
|
|
|
PEER-TO-PEER CLINICAL CONVERSATIONS |
|
|
|
|
|
| Harnessing Artificial Intelligence in Bladder Cancer: Digital Tools for Enhanced Diagnosis and Treatment |
| David McConkey, PhD |
| David McConkey discusses the application of artificial intelligence (AI) in bladder cancer research, focusing on AI pathology. He outlines ongoing collaborations between cooperative groups and industry partners to develop and validate AI tools for cancer diagnosis and treatment prediction. |
|
|
|
|
|
|
|
|
|
|
|
| Intermediate-Risk Bladder Cancer: New Substratification Model Validated |
| Francesco Soria, MD, FEBU |
| Francesco Soria discusses a study validating the IBCG scoring system for intermediate-risk bladder cancer. He explains the heterogeneity of this risk group and the need for better stratification. |
|
|
|
|
|
|
|
|
|
|
|
| EV-302 Trial Analysis Evaluating Nectin-4 in Urothelial Cancer |
| Thomas Powles, MBBS, MRCP, MD |
| Thomas Powles discusses the EV-302 trial data, focusing on biomarker analyses for EV plus pembrolizumab in urothelial cancer. Dr. Powles discusses the lack of discriminatory effect for PD-L1 and nectin-4 expression as biomarkers, noting that nectin-4 is nearly universally expressed in urothelial cancer. |
|
|
|
|
|
|
|
|
|
|
|
| Development and External Validation of an Artificial Intelligence-Based Tool for PROGression Risk Assessment in Non-Muscle Invasive Bladder Cancer: PROGRxN-BCa |
| Jethro Kwong, MSc |
| Jethro Kwong presents the development and validation of PROGRxN-BCa, an AI-based tool for predicting progression in NMIBC. Trained on data from over 7,000 patients across multiple institutions, PROGRxN-BCa outperformed current prediction models like the EAU risk calculator, with a c-index between 0.76 to 0.83. The tool was well-calibrated and identified subgroups of patients who may benefit from treatment intensification, offering better risk stratification for NMIBC progression. |
|
|
|
|
|
| Development of the Bladder Utility Symptom Scale (BUSS Utility): A Novel Tool to Measure Utilities and Quality of Life in Bladder Cancer Patients
|
| Girish Kulkarni, MD, PhD, FRCPC
|
| Girish Kulkarni presents the Bladder Utility Symptom Scale (BUSS), a novel tool for measuring utilities and quality of life in bladder cancer patients. The BUSS tool, built on a 10-item questionnaire, provides two algorithms for calculating utility scores based on patient and general public responses. These utilities, grounded in time tradeoff methodology, can be used across all phases of bladder cancer care for comparative effectiveness research, cost-effectiveness, and policy development, making it a valuable instrument for advancing patient-centered decision-making.
|
|
|
|
|
|
|
|
|
|
|
| Efficacy of Intravesical Nadofaragene Firadenovec-Vncg for Patients with BCG-Unresponsive Non-Muscle-Invasive Bladder Cancer: 36-Month Follow-up from a Phase 3 Trial |
| Colin Dinney, MD |
| Colin Dinney presents 36-month follow-up results from a phase 3 trial evaluating intravesical nadofaragene firadenovec for BCG-unresponsive non-muscle-invasive bladder cancer. The gene therapy showed a durable complete response duration of 9.7 months, with 25% of patients with carcinoma in situ (CIS) remaining high-grade recurrence-free at 36 months, and 31% of patients with high-grade Ta/T1 papillary disease achieving the same outcome. |
|
|
|
|
|
| Biomarkers and Prediction of Clinical Complete Response in Bladder Cancer |
| Eva Schaake, MD, PhD |
| Eva Schaake discusses the potential of biomarkers such as DNA damage response gene mutations, molecular subtypes, and ctDNA in predicting treatment response in bladder cancer, with an emphasis on bladder-sparing therapies. While molecular subtyping is not yet reliable for clinical use, plasma and urine ctDNA, alongside imaging tools like mpMRI, are showing promise for assessing treatment outcomes and guiding personalized treatment strategies. |
|
|
|
|
|
| AI-Enabled Bladder Cancer Grading: Externally Validating Quantitative Nuclear Features and Demonstrating Their Potential to Better Predict Time to Recurrence
|
| David Berman, MD, PhD
|
| David Berman presents findings on AI-enabled bladder cancer grading, highlighting the external validation of quantitative nuclear features that improve predictions for time to recurrence. By employing quantitative nuclear features derived from histopathology images, their models outperformed traditional grading by pathologists, achieving a C-index of 0.73 for recurrence-free survival, compared to 0.55 with pathologist consensus.
|
|
|
|
|
|
| Dynamic Monitoring of Circulating Tumor DNA to Predict Prognosis in Muscle-Invasive Bladder Cancer Patients After Radical Cystectomy
|
| Lourdes Mengual, PhD
|
| Lourdes Mengual presents the findings on the dynamic monitoring of ctDNA as a prognostic biomarker in muscle-invasive bladder cancer patients post-radical cystectomy. In two cohorts, the study demonstrated that ctDNA status was a significant indicator of tumor progression and cancer-specific survival, with changes in ctDNA correlating with patient outcomes.
|
|
|
|
|
|
| Identification of Bladder Cancer Patients That Could Benefit from Early Post-Cystectomy Immunotherapy Based on Serial Circulating Tumour DNA Testing: Preliminary Results from the TOMBOLA Trial |
| Jørgen Bjerggaard Jensen, MD |
| Jørgen Bjerggaard Jensen presented preliminary results from the TOMBOLA trial, which evaluated the use of serial ctDNA testing to identify bladder cancer patients who could benefit from early post-cystectomy immunotherapy. The trial involved muscle-invasive bladder cancer patients who underwent neoadjuvant chemotherapy followed by radical cystectomy, revealing that a significant proportion had positive ctDNA status post-surgery, with 65% of high-risk patients and nearly half of low-risk patients testing positive. |
|
|
|
|
|
|
|
|
|
|
