Evidence map›Paper›PMID 42466426›Full record

ArticleResearch square2026

GenoGlyph: Pan-cancer genomic mutation inference and risk stratification from diagnostic histopathology slides.

Abdul Rehman Akbar, Usama Sajjad, Alejandro Leyva, Elshad Hassanov, Arya Mariam Roy, Daniel Stover, Wencheng Li, Ashish Manne, Wei Chen, Anil Parwani and 1 more

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PubMed Full text at PMC Publisher
In one paragraph

Article in Research square, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Abdul Rehman AkbarDepartment of Pathology, College of Medicine, The Ohio State University Wexner Medical Center, Columbus, OH, USA.ORCID 0009-0004-2352-6360
Usama SajjadDepartment of Pathology, College of Medicine, The Ohio State University Wexner Medical Center, Columbus, OH, USA.
Alejandro LeyvaDepartment of Pathology, College of Medicine, The Ohio State University Wexner Medical Center, Columbus, OH, USA.
Elshad HassanovDepartment of Internal Medicine, Division of Medical Oncology, The Ohio State University Wexner Medical Center, Columbus, OH, USA.
Arya Mariam RoyDepartment of Internal Medicine, Division of Medical Oncology, The Ohio State University Wexner Medical Center, Columbus, OH, USA.
Daniel StoverDepartment of Internal Medicine, Division of Medical Oncology, The Ohio State University Wexner Medical Center, Columbus, OH, USA.
Wencheng LiDepartment of Pathology, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
Ashish ManneDepartment of Internal Medicine, Division of Medical Oncology, The Ohio State University Wexner Medical Center, Columbus, OH, USA.
Wei ChenDepartment of Pathology, College of Medicine, The Ohio State University Wexner Medical Center, Columbus, OH, USA.
Anil ParwaniDepartment of Pathology, College of Medicine, The Ohio State University Wexner Medical Center, Columbus, OH, USA.
Muhammad Khalid Khan NiaziDepartment of Pathology, College of Medicine, The Ohio State University Wexner Medical Center, Columbus, OH, USA.

Funding

An ensemble deep learning model for tumor bud detection and risk stratification in colorectal carcinoma.R01CA276301 · NCI · OHIO STATE UNIVERSITY · PI Wei Chen, Muhammad Khalid Khan Niazi · 2023 to 2026
$2.0M
NCI NIH HHS R01 CA276301
6 · The paper itself

Abstract

Genomic alterations drive therapeutic decisions in solid tumors, yet next-generation sequencing remains inaccessible in a substantial fraction of clinical settings worldwide. We present GenoGlyph, an interpretable deep learning framework designed to decode the grammar of histopathology for pan-cancer genomic mutational inference and clinical risk stratification. Across 6,391 whole-slide images spanning 14 solid tumor types, GenoGlyph robustly predicted actionable alterations (e.g.,

Identifiers

PMID42466426
PMCPMC13370653

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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.