Background/Objectives: Non-muscle invasive bladder cancer (NMIBC) management is increasingly complex due to conflicting guideline-based risk classifications, ongoing Bacillus Calmette–Guérin (BCG) shortages, and emerging alternative therapies. Computational Histology Artificial Intelligence (CHAI) tests are clinically available, providing insights from tumor specimens including predicting BCG responsiveness and individualized recurrence and progression risks, which may support precision medicine. This technology features biomarkers purpose-built for clinically unmet needs and has practical advantages including a fast turnaround time and no need for consumption of tissue or other specimens. We assessed the impact of such tests on physicians’ decision-making in routine, real-world NMIBC management.Methods: Physicians at six centers ordered CHAI tests (Vesta Bladder) at their discretion during routine NMIBC care. Tumor specimens were processed by a CLIA/CAP-accredited laboratory (Valar Labs, Houston, TX, USA) where H&E-stained slides were analyzed with the CHAI assay to extract histomorphic features of the tumor and microenvironment, which were algorithmically assessed to generate biomarker test results. For each case from 24 June 2024 to 18 July 2025, ordering physicians were surveyed to assess pre- and post-test management plans and post-test result usefulness.Results: Among 105 high-grade NMIBC cases with complete survey results available, primary management changed in 67% (70/105). Changes included modality shifts (n = 7; three to radical cystectomy with high prognostic risk scores; four avoiding cystectomy with low scores) and intravesical agent change (n = 63). Surveillance was intensified in 7%, predominantly among those with ≥90th percentile risk scores. The therapeutic agent changed in 80% (40/50) of predictive biomarker-present (indicative of poor response to BCG) tumors vs. 48% (23/48) of biomarker-absent tumors.Conclusions: In two thirds of cases, CHAI biomarker results influenced clinical decision-making during routine care. BCG predictive biomarker results frequently guided intravesical agent selection. These results have implications for optimizing clinical outcomes, especially in the setting of ongoing BCG shortages. Prognostic risk stratification results guided treatment escalation vs. de-escalation, including surveillance intensification and surgical vs. bladder-sparing decisions. CHAI biomarkers are currently utilized in routine clinical care and informing precision NMIBC management.
背景/目的：非肌层浸润性膀胱癌（NMIBC）的管理日益复杂，原因包括基于指南的风险分类存在冲突、卡介苗（BCG）持续短缺以及新兴替代疗法的出现。计算组织学人工智能（CHAI）检测目前已应用于临床，可从肿瘤标本中获取信息，包括预测BCG反应性以及个体化的复发和进展风险，从而可能支持精准医疗。该技术具备针对临床未满足需求而专门构建的生物标志物，并具有实际优势，包括检测周期短、无需消耗组织或其他标本。我们评估了此类检测在常规真实世界NMIBC管理中对医生决策的影响。
方法：六家中心的医生在常规NMIBC诊疗中自行决定申请CHAI检测（Vesta Bladder）。肿瘤标本由CLIA/CAP认证实验室（Valar Labs, 美国德克萨斯州休斯顿）处理，对H&E染色切片进行CHAI分析，提取肿瘤及微环境的组织形态学特征，并通过算法评估生成生物标志物检测结果。针对2024年6月24日至2025年7月18日期间的每个病例，对申请医生进行了调查，以评估检测前后的管理计划以及检测结果的有用性。
结果：在105例具有完整调查结果的高级别NMIBC病例中，67%（70/105）的主要管理方案发生了改变。改变包括治疗模式转变（n=7；其中3例因预后风险评分高转为根治性膀胱切除术；4例因评分低避免了膀胱切除术）和膀胱内灌注药物变更（n=63）。7%的病例加强了监测，主要集中在风险评分≥90百分位数的患者中。在预测性生物标志物阳性（提示对BCG反应不佳）的肿瘤中，80%（40/50）的治疗药物发生了改变，而在生物标志物阴性的肿瘤中，这一比例为48%（23/48）。
结论：在三分之二的病例中，CHAI生物标志物结果影响了常规诊疗中的临床决策。BCG预测性生物标志物结果经常指导膀胱内灌注药物的选择。这些结果对优化临床结局具有重要意义，尤其是在BCG持续短缺的背景下。预后风险分层结果指导了治疗升级与降级决策，包括加强监测以及手术与保膀胱治疗的选择。CHAI生物标志物目前已应用于常规临床实践，并为精准NMIBC管理提供参考。
肿瘤(癌症)患者之家