Evidence map›Paper›PMID 39934810›Full record

ArticleBMC medical informatics and decision making2025

A novel method for screening malignant hematological diseases by constructing an optimal machine learning model based on blood cell parameters.

Dehua Sun, Wei Chen, Jun He, Yongjian He, Haoqin Jiang, Hong Jiang, Dandan Liu, Lu Li, Min Liu, Zhigang Mao and 14 more

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Article in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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0cells of the map it votes in
4citing papers in PubMed
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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

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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

4 citing papers in PubMed.

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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

24 authors.

Dehua SunDepartment of Clinical Laboratory, Nanfang Hospital, Guangzhou, 516006, China.
Wei ChenDepartment of Clinical Laboratory, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China.
Jun HeDepartment of Clinical Laboratory, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China.
Yongjian HeDepartment of Clinical Laboratory, Nanfang Hospital, Guangzhou, 516006, China.
Haoqin JiangDepartment of Clinical Laboratory, Huashan Hospital Fudan University, Shanghai, 200040, China.
Hong JiangDepartment of Clinical Laboratory, West China Hospital of Sichuan University, Chengdu, 610044, China.
Dandan LiuDepartment of Clinical Laboratory, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China.
Lu LiClinical Department (IVD), Shenzhen Mindray Bio-Medical Electronics Co, Ltd, Shenzhen, 518057, China.
Min LiuDepartment of Clinical Laboratory, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510062, China.
Zhigang MaoDepartment of Clinical Laboratory, West China Hospital of Sichuan University, Chengdu, 610044, China.
Chenxue QuDepartment of Clinical Laboratory, Peking University First Hospital, Beijing, 100034, China.
Linlin QuDepartment of Clinical Laboratory, The First Bethune Hospital of Jilin University, Jilin, 130061, China.
Ziyong SunDepartment of Clinical Laboratory, Tongji Hospital, Tongji Medical College of Hust, Wuhan, 430030, China.
Jianbiao WangDepartment of Clinical Laboratory, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, 200025, China.
Wenjing WuDepartment of Clinical Laboratory, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China.
Xuefeng WangDepartment of Clinical Laboratory, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, 200025, China.
Wei XuDepartment of Clinical Laboratory, The First Bethune Hospital of Jilin University, Jilin, 130061, China.
Ying XingDepartment of Clinical Laboratory, Peking University First Hospital, Beijing, 100034, China.
Chi ZhangDepartment of Clinical Laboratory, Tongji Hospital, Tongji Medical College of Hust, Wuhan, 430030, China.
Jingxian ZhangClinical Department (IVD), Shenzhen Mindray Bio-Medical Electronics Co, Ltd, Shenzhen, 518057, China.
Lei ZhengDepartment of Clinical Laboratory, Nanfang Hospital, Guangzhou, 516006, China.
Shihong ZhangDepartment of Clinical Laboratory, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510062, China.
Bo Ye *Clinical Department (IVD), Shenzhen Mindray Bio-Medical Electronics Co, Ltd, Shenzhen, 518057, China. yebo@mindray.com.
Ming Guan *Department of Clinical Laboratory, Huashan Hospital Fudan University, Shanghai, 200040, China. guanming88@yahoo.com.

Funding

Wu Jieping Medical Foundation 300.6750.2021-6-40
6 · The paper itself

Abstract

backgroundScreening of malignant hematological diseases is of great importance for their diagnosis and subsequent treatment. This study constructed an optimal screening model for malignant hematological diseases based on routine blood cell parameters.

methodsThe venous blood samples of 1751 patients collected from 10 tertiary hospitals in China were divided into a training set (1223 cases) and a validation set (528 cases). In addition to the clinical diagnostic information of the samples in the training set, 26 blood cell parameters including morphological parameters were selected using manual screening and filtering to construct eight machine learning models. These models were used to identify hematological malignancies among the validation set.

resultsComparison of the discrimination, calibration and clinical detection performance of the eight machine learning models revealed that the artificial neural network (ANN) model performed the optimal in identifying malignant haematological diseases in the validation set (528 cases), with an area under the receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity of 0.906, 0.857, 0.832 and 0.884, respectively.

conclusionThe ANN model constructed can be used for screening of malignant hematological diseases, especially in primary hospitals that lack comprehensive diagnosis, and this ANN model will help patients to get diagnosis and treatment of malignant hematological diseases as early as possible.

Indexed as

Hematologic NeoplasmsMachine LearningNeural Networks, ComputerAdultAgedChinaFemaleHumansMaleMiddle AgedSensitivity and SpecificityArtificial intelligenceBlood cell parametersMachine learningMalignant hematological diseases

Identifiers

PMID39934810
PMCPMC11816569

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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.