Evidence map›Paper›PMID 41786798›Full record

ArticleScientific data2026

CLWD: a Chinese histopathology dataset for lung adenocarcinoma subtype classification.

Yang Chen, Haoyun Zhao, Li Wang, Li Li, Rongsheng Liu, Yinghan Jiang, Peiren Tang, Ying Li, Jun Ni, Dapeng Tao and 2 more

Abstract readDataset
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

12 authors.

Yang Chen *Department of Pathology, The First People's Hospital of Yunnan Province/The Affiliated Hospital of Kunming University of Science and Technology, Kunming, 650032, Yunnan, China.ORCID http://orcid.org/0000-0002-3547-5773
Haoyun Zhao *Department of Information Science & Engineering, Yunnan University, Kunming, 650032, Yunnan, China.
Li Wang *Department of Pathology, The First People's Hospital of Yunnan Province/The Affiliated Hospital of Kunming University of Science and Technology, Kunming, 650032, Yunnan, China.
Li Li *Department of Surgery, The First People's Hospital of Yunnan Province/The Affiliated Hospital of Kunming University of Science and Technology, Kunming, 650032, Yunnan, P.R. China.
Rongsheng LiuDepartment of Surgery, The First People's Hospital of Yunnan Province/The Affiliated Hospital of Kunming University of Science and Technology, Kunming, 650032, Yunnan, P.R. China.
Yinghan JiangDepartment of Pathology, The First People's Hospital of Yunnan Province/The Affiliated Hospital of Kunming University of Science and Technology, Kunming, 650032, Yunnan, China.
Peiren TangDepartment of Pathology, The First People's Hospital of Yunnan Province/The Affiliated Hospital of Kunming University of Science and Technology, Kunming, 650032, Yunnan, China.
Ying LiDepartment of Pathology, The First People's Hospital of Yunnan Province/The Affiliated Hospital of Kunming University of Science and Technology, Kunming, 650032, Yunnan, China.
Jun NiDepartment of Information Science & Engineering, Yunnan University, Kunming, 650032, Yunnan, China. jun.ni@ynu.edu.cn.
Dapeng TaoDepartment of Information Science & Engineering, Yunnan University, Kunming, 650032, Yunnan, China. dptao@ynu.edu.cn.
Jie LiAcademy of Biomedical Engineering, Kunming Medical University, Kunming, 650500, Yunnan, China. lijie@kmmu.edu.cn.
Jun PengDepartment of Surgery, The First People's Hospital of Yunnan Province/The Affiliated Hospital of Kunming University of Science and Technology, Kunming, 650032, Yunnan, P.R. China. 9y140211@kust.edu.cn.

Funding

National Natural Science Foundation of China (National Science Foundation of China) 62302429National Natural Science Foundation of China (National Science Foundation of China) 82404091National Natural Science Foundation of China (National Science Foundation of China) 82560572
6 · The paper itself

Abstract

Effective diagnosis and treatment of lung adenocarcinoma depends on accurate typing, subtyping, and grading. Herein, we present the CLWD dataset, a valuable resource for the lung cancer pathology community, comprising 408 whole-slide images (WSIs) from 210 patients specifically curated for the study of lung adenocarcinoma subtypes. Scanned at 80 × magnification, it is one of the largest datasets in Asia, with a particular emphasis on Chinese patient demographics. Notably, the dataset includes comprehensive clinical information, such as age, sex, and diagnosis, providing a robust foundation for diverse research needs. Publicly accessible, it supports a range of applications, including machine learning model development and validation. An initial evaluation of lung adenocarcinoma subtype classification using a multi-instance learning framework demonstrated that this dataset can substantially advance global research and improve the accuracy of subtype diagnosis.

Indexed as

Adenocarcinoma of LungLung NeoplasmsChinaEast Asian PeopleHumansMachine LearningMultiple-Instance Learning Algorithms

Identifiers

PMID41786798
PMCPMC13079772

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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.