Evidence map›Paper›PMID 41673850›Full record

ArticleRespiratory research2026

A deep learning model based on ultrasound imaging to differentiate malignant from benign pleural effusion: a multicenter cohort study.

Chang-Wei Wu, Chia-Suan Yu, Yen-Lin Chen, Po-Chih Kuo, Meng-Rui Lee, Jann-Yuan Wang, Chao-Chi Ho, Jin-Yuan Shih, Hao-Chien Wang

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

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

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Chang-Wei WuDivision of Pulmonary and Critical Care Medicine, Department of Internal Medicine, National Taiwan University Hospital, Hsin-Chu branch, Taipei, Hsin-Chu, Taiwan.
Chia-Suan YuDepartment of Computer Science, National Tsing Hua University, No. 101, Kuang Fu Rd, Sec.2, , Hsinchu, Hsinchu 300, Taiwan.
Yen-Lin ChenDivision of Pulmonary and Critical Care Medicine, Department of Internal Medicine, National Taiwan University Hospital, #7, Zhongshan South Rd., Zhongzheng Dist., Taipei, 100226, Taiwan.
Po-Chih Kuo *Department of Computer Science, National Tsing Hua University, No. 101, Kuang Fu Rd, Sec.2, , Hsinchu, Hsinchu 300, Taiwan. kuopc@cs.nthu.edu.tw.
Meng-Rui Lee *Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, National Taiwan University Hospital, Hsin-Chu branch, Taipei, Hsin-Chu, Taiwan. leemr@ntu.edu.tw.
Jann-Yuan WangDivision of Pulmonary and Critical Care Medicine, Department of Internal Medicine, National Taiwan University Hospital, #7, Zhongshan South Rd., Zhongzheng Dist., Taipei, 100226, Taiwan.
Chao-Chi HoDivision of Pulmonary and Critical Care Medicine, Department of Internal Medicine, National Taiwan University Hospital, Hsin-Chu branch, Taipei, Hsin-Chu, Taiwan.
Jin-Yuan ShihDivision of Pulmonary and Critical Care Medicine, Department of Internal Medicine, National Taiwan University Hospital, #7, Zhongshan South Rd., Zhongzheng Dist., Taipei, 100226, Taiwan.
Hao-Chien WangDivision of Pulmonary and Critical Care Medicine, Department of Internal Medicine, National Taiwan University Hospital, #7, Zhongshan South Rd., Zhongzheng Dist., Taipei, 100226, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThoracentesis is required for malignant pleural effusion (MPE) diagnosis. However, it is an invasive procedure and carries risks. It remains unknown whether deep learning using ultrasound images could become a non-invasive diagnostic approach.

methodsPatients with pleural effusion detected by thoracic ultrasound and received diagnostic thoracentesis were retrospectively collected from two sites. The internal cohort collected patients from the National Taiwan University Hospital (NTUH) Hsin-Chu branch (2014–2021), whereas the external cohort collected patients from NTUH (2020–2021). The MPE was confirmed by cytopathology reports, while benign pleural effusion was ascertained by negative cytology and compatible clinical courses. A convolutional deep learning model was used to identify MPE. Performance metrics included accuracy, F1 score, sensitivity, specificity and the area under the receiver operating characteristic curve (AUC).

resultsA total of 601 and 144 patients from the internal cohort and the external cohort were used for model development. The model achieved promising results in internal testing (accuracy = 0.750 [95% CI: 0.689–0.811], sensitivity = 0.710 [95% CI: 0.619–0.798], specificity = 0.803 [95% CI: 0.704–0.893], F1 = 0.763 [95% CI: 0.691–0.826], AUC = 0.814 [95% CI: 0.746─0.873]). After fine-tuning with small number of external images, the model achieved the following performance on the external testing set: accuracy = 0.774 [95% CI: 0.679–0.857], sensitivity = 0.818 [95% CI: 0.723–0.905)], specificity = 0.611 [95% CI: 0.389–0.846], F1 = 0.850 [95% CI: 0.776–0.913], AUC = 0.753 [95% CI: 0.596–0.885].

conclusionsOur deep learning model holds promise as a non-invasive point-of-care modality for assistance in pleural effusion diagnosis.

Indexed as

Deep LearningPleural Effusion, MalignantAgedAged, 80 and overCohort StudiesDiagnosis, DifferentialFemaleHumansMaleMiddle AgedPleural EffusionRetrospective StudiesTaiwanThoracentesisUltrasonographyDeep-learningDiagnosisMalignant pleural effusionThoracic ultrasound

Identifiers

PMID41673850
PMCPMC12997993

What Socratic holds

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LicenceCC BY
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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 Socratic graph.