Evidence map›Paper›PMID 42552267›Full record

ArticleJournal of imaging informatics in medicine2026

AI-Assisted Papillary Thyroid Carcinoma Localization on Cervical Non-contrast CT: A Multicenter Validation and Two-Reader Pilot Study of a Standalone Executable nnU-Net Software.

Hao Wang, Xuan Wang, Zhi-Lin Chen, Yu-Sheng Du, You Wang, Zhuo-Jie Bai, Yun-Tian Wan, Ting-Ting Feng, Jian He, Jing Tao

Abstract read
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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.

Hao Wang *Department of Radiology, The Fourth Affiliated Hospital of Nanjing Medical University, Nanjing, 210031, People's Republic of China.ORCID http://orcid.org/0000-0003-2659-7231
Xuan Wang *Department of Radiology, Zhongda Hospital Southeast University, Nanjing, 210048, People's Republic of China.
Zhi-Lin ChenDepartment of Radiology, Zhongda Hospital Southeast University, Nanjing, 210048, People's Republic of China.
Yu-Sheng DuDepartment of Radiology, The Fourth Affiliated Hospital of Nanjing Medical University, Nanjing, 210031, People's Republic of China.
You WangDepartment of Radiology, The Fourth Affiliated Hospital of Nanjing Medical University, Nanjing, 210031, People's Republic of China.
Zhuo-Jie BaiDepartment of Radiology, The Fourth Affiliated Hospital of Nanjing Medical University, Nanjing, 210031, People's Republic of China.
Yun-Tian WanDepartment of Radiology, The Fourth Affiliated Hospital of Nanjing Medical University, Nanjing, 210031, People's Republic of China.
Ting-Ting FengDepartment of Central Laboratory, The Fourth Affiliated Hospital of Nanjing Medical University, 210031, Nanjing, People's Republic of China.
Jian HeDepartment of Nuclear Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Medicine school, Nanjing University, 210008, Nanjing, People's Republic of China. hjxueren@126.com.ORCID http://orcid.org/0000-0001-8140-4610
Jing TaoDepartment of General Surgery, The Fourth Affiliated Hospital of Nanjing Medical University, 210031, Nanjing, People's Republic of China. chnn123@163.com.ORCID http://orcid.org/0000-0002-5288-8369

Funding

Nanjing Municipal Health Science and Technology Development Special Fund ZDXX25197
6 · The paper itself

Abstract

Accurate preoperative localization and 3D anatomical assessment of papillary thyroid carcinoma (PTC) are critical for surgical decision-making. Cervical non-contrast CT (NCCT) avoids iodinated contrast-related risks but has limited PTC lesion conspicuity, while first-line ultrasound suffers from operator dependence and incomplete 3D anatomical information. We developed a 3D full-resolution (fullres) nnU-Net model for joint thyroid and PTC segmentation on cervical NCCT, and conducted a pilot reader study to evaluate its auxiliary value for a junior and a senior radiologist. This multicenter retrospective study included 300 cases for model training, 120 temporal and 100 external test cases. Model performance was assessed via standard segmentation metrics, and the optimal model was packaged as a standalone executable Windows software. The 3D fullres model significantly outperformed 2D and single-label models for both PTC and thyroid segmentation (all P < 0.001). AI assistance significantly improved the diagnostic sensitivity of the participating junior radiologist (P < 0.001) and reduced active human reading time by > 40% for both readers, while the human-in-the-loop workflow effectively mitigated the model's inherent false-positive risk. This first NCCT-based 3D nnU-Net joint segmentation model improves the PTC diagnostic efficiency and sensitivity of the two participating radiologists, with an open-source standalone executable software providing a novel contrast-free tool to assist in regional PTC localization prior to surgery. Trial Registration: retrospectively registered in the Chinese Clinical Trial Registry (ChiCTR2500097848) on 2025-02-26.

Indexed as

Clinical translationNnU-NetNon-contrast CTPapillary thyroid carcinomaRadiologist–AI collaboration

Identifiers

PMID42552267

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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.