Presence of an Artificial Intelligence-powered Predictive Biomarker Is Associated with a Poor Response to Intravesical Bacillus Calmette-Guerin but Not to Intravesical Sequential Gemcitabine/Docetaxel in Patients with High-grade Non-muscle-invasive Bladder Cancer

Vignesh T Packiam; Ian M McElree; Saum Ghodoussipour; Vivek Nimgaonkar; Viswesh Krishna; Joon Kyung Kim; Derek B Allison; Jordan R Richards; K D Anand Rajan; Stephanie J Chen; Yair Lotan; Stephen B Williams; Haochen Zhang; Drew Watson; Damir Vrabac; Waleed M Abuzeid; Anirudh Joshi; Ashish M Kamat; Michael A O'Donnell; Patrick J Hensley

doi:10.1016/j.euo.2025.04.006

Back

Presence of an Artificial Intelligence-powered Predictive Biomarker Is Associated with a Poor Response to Intravesical Bacillus Calmette-Guerin but Not to Intravesical Sequential Gemcitabine/Docetaxel in Patients with High-grade Non-muscle-invasive Bladder Cancer

Journal article

Open access

Peer reviewed

Presence of an Artificial Intelligence-powered Predictive Biomarker Is Associated with a Poor Response to Intravesical Bacillus Calmette-Guerin but Not to Intravesical Sequential Gemcitabine/Docetaxel in Patients with High-grade Non-muscle-invasive Bladder Cancer

Vignesh T Packiam, Ian M McElree, Saum Ghodoussipour, Vivek Nimgaonkar, Viswesh Krishna, Joon Kyung Kim, Derek B Allison, Jordan R Richards, K D Anand Rajan, Stephanie J Chen, …

European urology oncology, Vol.8(6), pp.1461-1465

12/2025

DOI: 10.1016/j.euo.2025.04.006

PMCID: PMC12907750

PMID: 40287344

Files and links (1)

url

https://doi.org/10.1016/j.euo.2025.04.006View

Published (Version of record) Open Access

Abstract

Intravesical bacillus Calmette-Guerin (BCG) is considered first-line adjuvant therapy for high-risk or high-grade non-muscle-invasive bladder cancer (NMIBC). Recently, sequential intravesical gemcitabine and docetaxel (Gem/Doce) has emerged as a promising alternative to intravesical BCG. Biomarkers to select the optimal treatment regimen could facilitate clinical decision-making. The Computational Histologic Artificial Intelligence (CHAI) platform was previously used to develop an artificial intelligence-augmented histologic assay (CHAI biomarker) that identified patients with NMIBC at an increased risk of recurrence and progression events following BCG treatment. In this study, we assessed use of the CHAI biomarker among patients with treatment-naive high-grade NMIBC who received intravesical BCG or Gem/Doce. Among patients with the presence of the CHAI biomarker, those treated with BCG had a 24-mo high-grade recurrence-free survival (HG-RFS) rate of 56% (95% confidence interval [CI] 43-73%) and those treated with Gem/Doce had an HG-RFS rate of 90% (95% CI 79-100%; hazard ratio [HR] 5.4, 95% CI 1.6-18.3, p = 0.007). Among patients with an absence of the CHAI biomarker, those treated with BCG or Gem/Doce had no significant difference in HG-RFS (HR 1.3, 95% CI 0.6-2.6, p = 0.5). The interaction term between the CHAI biomarker and the treatment type was significant (p = 0.029), indicating an association between the biomarker and the clinical outcome that is dependent on the treatment received. This study suggests that the CHAI biomarker predicts which specific high-grade NMIBC patients are less likely to benefit from BCG and may benefit from alternative treatments including, potentially, Gem/Doce.

Machine Learning

Bacillus Calmette-Guerin

Non–muscle-invasive bladder cancer

Biomarker

Gemcitabine

Docetaxel

Artificial intelligence

Details

Title: Subtitle: Presence of an Artificial Intelligence-powered Predictive Biomarker Is Associated with a Poor Response to Intravesical Bacillus Calmette-Guerin but Not to Intravesical Sequential Gemcitabine/Docetaxel in Patients with High-grade Non-muscle-invasive Bladder Cancer
Creators: Vignesh T Packiam - Rutgers, The State University of New Jersey
Ian M McElree - University of Iowa
Saum Ghodoussipour - Rutgers, The State University of New Jersey
Vivek Nimgaonkar - Valar Labs, Palo Alto, CA, USA
Viswesh Krishna - Valar Labs, Palo Alto, CA, USA
Joon Kyung Kim - University of Kentucky
Derek B Allison - University of Kentucky
Jordan R Richards - University of Iowa
K D Anand Rajan - University of Iowa, Pathology
Stephanie J Chen - University of Iowa
Yair Lotan - The University of Texas Southwestern Medical Center
Stephen B Williams - The University of Texas Medical Branch at Galveston
Haochen Zhang - Valar Labs, Palo Alto, CA, USA
Drew Watson - Valar Labs, Palo Alto, CA, USA
Damir Vrabac - Valar Labs, Palo Alto, CA, USA
Waleed M Abuzeid - Valar Labs, Palo Alto, CA, USA
Anirudh Joshi - Valar Labs, Palo Alto, CA, USA
Ashish M Kamat - The University of Texas MD Anderson Cancer Center
Michael A O'Donnell - University of Iowa
Patrick J Hensley - University of Kentucky
Resource Type: Journal article
Publication Details: European urology oncology, Vol.8(6), pp.1461-1465
DOI: 10.1016/j.euo.2025.04.006
PMID: 40287344
PMCID: PMC12907750
NLM abbreviation: Eur Urol Oncol
ISSN: 2588-9311
eISSN: 2588-9311
Language: English
Electronic publication date: 04/25/2025
Date published: 12/2025
Academic Unit: Pathology; Urology
Record Identifier: 9984816010602771

Metrics

3 Record Views