Evidence map›Paper›PMID 41168629›Full record

ArticleJournal of imaging informatics in medicine2026

Intelligent Diagnosis of Follicular Carcinoma Thyroid Cancer with a Novel Deep Learning Model.

Size Hou, Yunyun Liu, Ming Cai, Jingzhou Xu, Jun Qi, Xiaopan Cai, Qiang Niu, Yifeng Zhang, Huixiong Xu, Jiangang Chen

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of imaging informatics in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Size Hou *Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication & Electronic Engineering, East China Normal University, Shanghai, 200241, China.
Yunyun Liu *Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Shanghai, China.
Ming Cai *Department of Thyroid Oncology, Chongqing Key Laboratory for the Mechanism and Intervention of Cancer Metastasis, Chongqing University Cancer Hospital, Chongqing, China.
Jingzhou XuSchool of Advanced Technology, Xian Jiaotong-Liverpool University, Suzhou, 215123, China.
Jun QiDepartment of Thyroid Oncology, Chongqing Key Laboratory for the Mechanism and Intervention of Cancer Metastasis, Chongqing University Cancer Hospital, Chongqing, China.
Xiaopan CaiJoint Research Center for Musculoskeletal Tumor of Shanghai Changzheng Hospitaland , University of Shanghai for Science and Technology, Shanghai, 200003, China.
Qiang NiuDepartment of Applied Mathematics, School of Science, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China.
Yifeng ZhangDepartment of Medical Ultrasound, Shanghai Tenth People's Hospital, Shanghai, China. zhangyifeng@tongji.edu.cn.
Huixiong XuShanghai Engineering Research Center of Ultrasound Diagnosis and Treatment, Shanghai, China. xu.huixiong@zs-hospital.sh.cn.
Jiangang ChenShanghai Key Laboratory of Multidimensional Information Processing, School of Communication & Electronic Engineering, East China Normal University, Shanghai, 200241, China. jgchen@cee.ecnu.edu.cn.ORCID http://orcid.org/0000-0001-8775-0282

Funding

Jilin Province science and technology development plan project Grant No. 20230204094YYKey Research and Development Program of Zhejiang Province 2024C03240National Natural Science Foundation of China Grant No. 82151318Science and Technology Commission of Shanghai 21Y11902500Science and Technology Commission of Shanghai 22JC1403603Science and Technology Commission of Shanghai Grant No. 22DZ2229004Scientific Development funds for Local Region from the Chinese Government in 2023 Grant No. XZ202301YD0032C
6 · The paper itself

Abstract

Distinguishing follicular thyroid carcinoma (FTC) from follicular adenoma (FTA) preoperatively remains a significant challenge in thyroid nodule management. This study aims to develop and validate a novel, interpretable deep learning model that explicitly leverages the critical diagnostic feature-tumor margins-to address this issue. A total of 577 patients, 435 females and 142 males (mean age, 51.05

Indexed as

Adenocarcinoma, FollicularDeep LearningImage Interpretation, Computer-AssistedThyroid NeoplasmsAdenomaDiagnosis, DifferentialFemaleGraph Neural NetworksHumansMaleMiddle AgedUltrasonographyDeep learningFollicular carcinoma thyroid cancerGraph convolutional neural networksThyroidUltrasound

Identifiers

PMID41168629
PMCPMC13481969

What OpenQuestion holds

Textmetadata
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.