Impact of Computational Histology AI Biomarkers on Clinical Management Decisions in Non-Muscle Invasive Bladder Cancer: A Multi-Center Real-World Study.
Simple Summary: Non-muscle invasive bladder cancer is a common malignancy with a high rate of recurrence or progression. A supply shortage of the standard treatment and a wave of new treatment options have increased the need for precision medicine in this disease. Artificial intelligence-powered his...
| Publicado en: | Cancers Vol. 18; no. 2; pp. 249 - 261 |
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| Autores principales: | , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
MDPI
Jan2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=191220840&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 191220840 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20726694 B74B jtl: Cancers issn: 20726694 maglogo: N pubinfo: dt: Jan2026 vid: 18 iid: 2 pid: 97109 pub: MDPI artinfo: ui: 191220840 191220840 191220840 10.3390/cancers18020249 191220840 ppf: 249 ppct: 12 formats: tig: atl: Impact of Computational Histology AI Biomarkers on Clinical Management Decisions in Non-Muscle Invasive Bladder Cancer: A Multi-Center Real-World Study. aug: au: Packiam, Vignesh T. Ghodoussipour, Saum Konety, Badrinath R. Ahmadi, Hamed Agarwal, Gautum Kiedrowski, Lesli A. Krishna, Viswesh Joshi, Anirudh Williams, Stephen B. Smith, Armine K. affil: Rutgers Cancer Institute, New Brunswick, NJ 08901, USA sug: subj: Non-Muscle Invasive Bladder Neoplasms Diagnosis Non-Muscle Invasive Bladder Neoplasms Therapy Artificial Intelligence Utilization Tumor Markers, Biological Decision Making, Clinical Individualized Medicine Disease Progression Risk Factors Histology BCG Vaccine Therapeutic Use BCG Vaccine Supply and Distribution Risk Assessment Human Male Female Nonexperimental Studies United States Multicenter Studies Urology Academic Medical Centers Adult Middle Age Aged Aged, 80 and Over Specimen Handling Surveys Descriptive Statistics Cystectomy Predictive Value of Tests Drug Substitution Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Male Female ab: Simple Summary: Non-muscle invasive bladder cancer is a common malignancy with a high rate of recurrence or progression. A supply shortage of the standard treatment and a wave of new treatment options have increased the need for precision medicine in this disease. Artificial intelligence-powered histology biomarkers have emerged as promising tools to support risk stratification and treatment selection in bladder cancer. We assessed the impact of this testing through routine care on physicians' decisions for 105 patients and found that results influenced clinical decision-making in two thirds of cases, including changing therapeutic agent and intensifying treatment plans. These AI-powered tools are currently utilized in routine clinical care and can guide precision bladder cancer management. 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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