Evidence map›Paper›PMID 40890335›Full record

ArticleScientific reports2025

Interpretable AI-assisted diagnosis of papillary thyroid cancer cytopathology using graph neural networks and knowledge graphs.

Li-Xue Wu, Yong Jiang, Tian-You Luo, Jia-Xin Hou, Yang Deng, Lu-Xin Han, Ting-Feng Jiang, Ji Bao

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 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

8 authors.

Li-Xue WuDepartment of Pathology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China.
Yong JiangDepartment of Pathology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China.
Tian-You LuoDepartment of Pathology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China.
Jia-Xin HouWest China Clinical Medical College of Sichuan University, Chengdu, 610041, Sichuan, China.
Yang DengDepartment of Pathology, Key Laboratory of Transplant Engineering and Immunology, West China Hospital, Institute of Clinical Pathology, Sichuan University, Chengdu, 610041, Sichuan, China.
Lu-Xin HanSichuan KgCure Co., Ltd., Sichuan University, Chengdu, 610041, Sichuan, China.
Ting-Feng JiangSichuan KgCure Co., Ltd., Sichuan University, Chengdu, 610041, Sichuan, China.
Ji BaoDepartment of Pathology, Key Laboratory of Transplant Engineering and Immunology, West China Hospital, Institute of Clinical Pathology, Sichuan University, Chengdu, 610041, Sichuan, China. baoji@scu.edu.cn.

Funding

the 1·3·5 project for disciplines of excellence-Clinical Research Incubation Project 2020HXFH029the Key R&D Project of Sichuan Science and Technology Department 22ZDYF1406West China Hospital Sichuan University, the Technology Innovation R&D Project of Chengdu Science and Technology Bureau 2022-YF05-01255-SN
6 · The paper itself

Abstract

This study presents an interpretable AI-assisted diagnostic approach for papillary thyroid carcinoma (PTC) cytopathology by combining graph neural networks (GNNs) with knowledge graphs (KGs). Routine cytology smears from 281 PTC cases were scanned, labeled, and processed using the Cascade RCNN model to detect pathological cell features, including 45,680 ground-glass nuclei, 712 nuclear grooves, and 116 intranuclear inclusions. By integrating GNNs, the model achieved a mean intersection over union (mIoU) of 56.14% and a mean average precision (mAP) of 0.87. The GINet model further improved classification accuracy to 88.84%. Our approach also incorporates a clinical decision support system (CDSS) for querying KGs, providing explainable diagnostic outputs. This method offers an interpretable and reliable AI tool for PTC diagnosis, enhancing the transparency of AI-assisted pathology systems.

Indexed as

Artificial IntelligenceCytodiagnosisDiagnosis, Computer-AssistedNeural Networks, ComputerThyroid Cancer, PapillaryThyroid NeoplasmsGraph Neural NetworksHumansArtificial intelligenceCytologyFine needle aspirationGraph neural networksInterpretable artificial intelligenceKnowledge graphPapillary thyroid cancer

Identifiers

PMID40890335
PMCPMC12402155

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.