Evidence map›Paper›PMID 40998194›Full record

ArticleCancer letters2025

AI-informed computational pathology classifier predicts outcomes across treatment modalities in muscle-invasive urothelial carcinoma.

Kamal Hammouda, Naoto Tokuyama, Germán Corredor, Tilak Pathak, Rishi Dakarapu, Elizabeth Genega, Omar Y Mian, Paul G Pavicic, C Marcela Diaz-Montero, Tuomas Mirtti and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancer letters, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Review
  2. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Kamal HammoudaThe Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University, Atlanta, GA, USA; Mathematics Department, Faculty of Science, Mansoura University, Mansoura, Egypt; Atlanta Veterans Affairs Medical Center, Atlanta, GA, USA.
Naoto TokuyamaThe Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University, Atlanta, GA, USA; Tokyo Medical University, Department of Urology, Tokyo, Japan.
Germán CorredorThe Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University, Atlanta, GA, USA; Atlanta Veterans Affairs Medical Center, Atlanta, GA, USA.
Tilak PathakThe Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University, Atlanta, GA, USA.
Rishi DakarapuThe Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University, Atlanta, GA, USA.
Elizabeth GenegaThe Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University, Atlanta, GA, USA.
Omar Y MianFred Hutchinson Cancer Center, Seattle, WA, USA.
Paul G PavicicCleveland Clinic, Cleveland, OH, USA.
C Marcela Diaz-MonteroCleveland Clinic, Cleveland, OH, USA.
Tuomas MirttiHelsinki University Hospital, Helsinki, Finland.
Xavier FarréPublic Health Agency of Catalonia, Lleida, Spain.
Shilpa GuptaCleveland Clinic, Cleveland, OH, USA.
Anant MadabhushiThe Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University, Atlanta, GA, USA; Atlanta Veterans Affairs Medical Center, Atlanta, GA, USA. Electronic address: anantm@emory.edu.

Funding

Pathology CoreU54CA254566 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI MADABHUSHI, ANANT · 2020 to 2024
$5.0M
Computer-Assisted Histologic Evaluation of Cardiac Allograft RejectionR01HL151277 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI MADABHUSHI, ANANT, MARGULIES, KENNETH BER · 2020 to 2023
$3.2M
Oral Cavity Quantitative Histomorphometric Risk Classifier (OHbIC) in Oral Cavity Squamous Cell Carcinoma (OC-SCC)R01CA249992 · NCI · EMORY UNIVERSITY · PI LEWIS, JAMES, MADABHUSHI, ANANT · 2021 to 2025
$3.2M
Computerized histologic image predictor of cancer outcomeR01CA202752 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI FELDMAN, MICHAEL D, GANESAN, SHRIDAR · 2016 to 2020
$3.1M
Prostate cancer risk stratification via computational 3D pathologyR01CA268207 · NCI · UNIVERSITY OF WASHINGTON · PI Jonathan T.C. Liu, Anant Madabhushi · 2022 to 2026
$3.1M
Quantitative Histomorphometric Risk Classifier (QuHbIC) in HPV + Oropharyngeal CarcinomaR01CA220581 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI KOYFMAN, SHLOMO, LEWIS, JAMES · 2018 to 2023
$3.1M
Computerized Histologic Risk Predictor (CHiRP) for Early Stage Lung CancersR01CA216579 · NCI · EMORY UNIVERSITY · PI FU, PINGFU, LLOYD, MARK · 2018 to 2023
$3.1M
Prognostic and Predictive Digital Tissue Image Assay for Prostate CancerR01CA268287 · NCI · EMORY UNIVERSITY · PI GUPTA, SHILPA, LAL, PRITI · 2022 to 2025
$3.0M
MR Fingerprinting and Computerized Decision Support for Prostate CancerR01CA208236 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI GULANI, VIKAS, PONSKY, LEE EVAN · 2017 to 2022
$3.0M
RADIOMIC APPROACHES TO IMPROVE TARGETING FOR ATRIAL FIBRILLATION CATHETER ABLATIONR01HL158071 · NHLBI · CLEVELAND CLINIC LERNER COM-CWRU · PI BARNARD, JOHN, CHUNG, MINA KAY · 2021 to 2024
$2.9M
Novel Radiomics for Predicting Response to Immunotherapy for Lung CancerR01CA257612 · NCI · EMORY UNIVERSITY · PI Anant Madabhushi, Vamsidhar Velcheti · 2021 to 2026
$2.7M
Research Training in Translational Gastroenterology and HepatologyT32DK108735 · NIDDK · EMORY UNIVERSITY · PI SUBRA KUGATHASAN · 2016 to 2026
$2.6M
BLRD VA I01 BX004121BLRD VA IK6 BX006185CSRD VA I01 CX002622CSRD VA I01 CX002776NCI NIH HHS R01 CA202752NCI NIH HHS R01 CA208236NCI NIH HHS R01 CA216579NCI NIH HHS R01 CA220581NCI NIH HHS R01 CA249992NCI NIH HHS R01 CA257612NCI NIH HHS R01 CA268207NCI NIH HHS R01 CA268287NCI NIH HHS U01 CA239055NCI NIH HHS U01 CA248226NCI NIH HHS U01 CA269181NCI NIH HHS U54 CA254566NHLBI NIH HHS R01 HL151277NHLBI NIH HHS R01 HL158071NIDDK NIH HHS T32 DK108735
6 · The paper itself

Abstract

Urothelial carcinoma (UC) is one of the leading causes of cancer-related mortality, and effective, scalable biomarkers for treatment planning remain limited. We present UC-TIL, an artificial intelligence (AI)-based model that quantifies spatial patterns of tumor-infiltrating lymphocytes (TILs) from routine H&E-stained slides to predict survival and immunotherapy response. We analyzed 558 whole-slide images across three cohorts: TCGA (D

Indexed as

Artificial IntelligenceCarcinoma, Transitional CellLymphocytes, Tumor-InfiltratingUrinary Bladder NeoplasmsAgedFemaleHumansImmune Checkpoint InhibitorsMaleMiddle AgedNeoplasm InvasivenessPrognosisTreatment OutcomeImmune Checkpoint InhibitorsArtificial intelligenceDigital pathologyMetastatic urothelial carcinomaMuscle-invasive bladder cancerPredictive biomarkerTumor-infiltrating lymphocytes

Identifiers

PMID40998194
PMCPMC12496026

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.