Evidence map›Paper›PMID 41266481›Full record

ArticleScientific data2025

A large dataset of bone marrow cells in myelodysplastic syndrome for classification systems.

Dan Shen, Xiangli Gao, Jixiang Tong, Junlin Feng, Zhen Huang, Ting Zhang, Jianghu Li, Shuqi Zhao, Yijing Zhu, Yinqing Xu and 3 more

Abstract readDataset
In one paragraph

Article in Scientific data, 2025. 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

13 authors.

Dan Shen *The First Affiliated Hospital of Zhejiang University Medical College, Hangzhou, China.
Xiangli Gao *The First Affiliated Hospital of Zhejiang University Medical College, Hangzhou, China.
Jixiang TongThe First Affiliated Hospital of Zhejiang University Medical College, Hangzhou, China.
Junlin FengHangzhou Kinsight Biotech Co. Ltd, Hangzhou, China.
Zhen HuangHangzhou Kinsight Biotech Co. Ltd, Hangzhou, China.
Ting ZhangThe First Affiliated Hospital of Zhejiang University Medical College, Hangzhou, China.
Jianghu LiThe First Affiliated Hospital of Zhejiang University Medical College, Hangzhou, China.
Shuqi ZhaoThe First Affiliated Hospital of Zhejiang University Medical College, Hangzhou, China.
Yijing ZhuThe First Affiliated Hospital of Zhejiang University Medical College, Hangzhou, China.
Yinqing XuThe First Affiliated Hospital of Zhejiang University Medical College, Hangzhou, China.
Shuchong YuanThe First Affiliated Hospital of Zhejiang University Medical College, Hangzhou, China.
Cheng JinThe First Affiliated Hospital of Zhejiang University Medical College, Hangzhou, China.
Hongyan TongThe First Affiliated Hospital of Zhejiang University Medical College, Hangzhou, China. hongyantong@aliyun.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Bone marrow smear cytomorphology serves as the cornerstone for diagnosing hematological malignancies. It is particularly complex in myelodysplastic syndrome (MDS), which brings challenges to automated image analysis approaches. This dataset provides 25,009 images of the bone marrow cells from 125 patients with MDS. Image tags consist of 27 categories, including normal cells and abnormal cells in MDS that are characterized by pathological hematopoiesis, such as micro megakaryocytes. Each label underwent rigorous review by up to three independent experts. Cells were cropped after converting their bounding boxes into squares based on the longest side and expansion by 10% to guarantee the integrity of the cell boundaries and morphological features in the cropped images while minimum surrounding cells were included. Through training using this database, an AI model could attain the ability to differentiate dysplastic cells.

Indexed as

Bone Marrow CellsMyelodysplastic SyndromesHumansImage Processing, Computer-Assisted

Identifiers

PMID41266481
PMCPMC12635297

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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.