Evidence map›Paper›PMID 41646358›Full record

ArticleResearch square2026

Learning the Language of Histopathology Images reveals Prognostic Subgroups in Invasive Lung Adenocarcinoma Patients.

Abdul Rehman Akbar, Usama Sajjad, Ziyu Su, Wencheng Li, Fei Xing, Jimmy Ruiz, Wei Chen, Muhammad Khalid Khan Niazi

Abstract readPreprint
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

8 authors.

Abdul Rehman AkbarDepartment of Pathology, College of Medicine, The Ohio State University Wexner Medical Center, Columbus, OH, USA.
Usama SajjadDepartment of Pathology, College of Medicine, The Ohio State University Wexner Medical Center, Columbus, OH, USA.
Ziyu SuDepartment of Pathology, College of Medicine, The Ohio State University Wexner Medical Center, Columbus, OH, USA.
Wencheng LiDepartment of Pathology, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
Fei XingDepartment of Cancer Biology, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
Jimmy RuizDepartment of Medicine (Hematology & Oncology), Wake Forest University School of Medicine, Winston-Salem, NC, USA.
Wei ChenDepartment 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

Computer-assisted diagnosis of ear pathologies by combining digital otoscopy with complementary data using machine learningR01DC020715 · NIDCD · VANDERBILT UNIVERSITY MEDICAL CENTER · PI Metin Nafi Gurcan, Aaron C Moberly · 2023 to 2026
$2.5M
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
Incorporating Donor Lung CT Images into Machine Learning Models to Predict Severe Primary Graft Dysfunction after Lung TransplantationR01HL177046 · NHLBI · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · PI Metin Nafi Gurcan, Ramsey Hachem · 2025 to 2026
$1.5M
NCI NIH HHS R01 CA276301NHLBI NIH HHS R01 HL177046NIDCD NIH HHS R01 DC020715
6 · The paper itself

Abstract

Recurrence remains a major clinical challenge in surgically resected invasive lung adenocarcinoma, where existing grading and staging systems fail to capture the cellular complexity that underlies tumor aggressiveness. We present

Identifiers

PMID41646358
PMCPMC12869682

What OpenQuestion holds

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Registered trials

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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.