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TRV-2026-1150Version 1 · Certified

Written 2026-09-20 06:53:35 UTC · current record

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TRUVACE RECORD VERSION
record: TRV-2026-1150
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-09-20T06:53:35.277628Z
status: published
lens: p_space
sector: health
headline: Development and Validation of a Picture Archiving and Communication System-Integrated Artificial Intelligence for Predicting Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma
dek: The objective of this study is to develop and validate a Picture Archiving and Communication System-integrated artificial intelligence (PACS-AI) tool for automated neck-level localization, three-dimensional (3D) segmentation, and metastasis risk prediction of cervical lymph nodes (LNs) in patients with papillary thyroid carcinoma (PTC). CT images from 710 patients with PTC, including 4942 LNs, were retrospectively collected from two medical centers and divided into training, internal test, and external test coho…
gain_title: (none)
problem_title: The objective of this study is to develop and validate a Picture Archiving and Communication System-integrated artificial intelligence (PACS-AI) tool for automated neck-level localization, three-dimensional (3D) segmentation, and metastasis risk prediction of cervical lymph nodes (LNs) in patients with papillary thyroid carcinoma (PTC).
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: The objective of this study is to develop and validate a Picture Archiving and Communication System-integrated artificial intelligence (PACS-AI) tool for automated neck-level localization, three-dimensional (3D) segmentation, and metastasis risk prediction of cervical lymph nodes (LNs) in patients with papillary thyroid carcinoma (PTC).
problem_evidence: (none)
quick_read: The objective of this study is to develop and validate a Picture Archiving and Communication System-integrated artificial intelligence (PACS-AI) tool for automated neck-level localization, three-dimensional (3D) segmentation, and metastasis risk prediction of cervical lymph nodes (LNs) in patients with papillary thyroid carcinoma (PTC). CT images from 710 patients with PTC, including 4942 LNs, were retrospectively collected from two medical centers and divided into training, internal test, and external test cohorts.

The AI model consisted of a 3D TransUNet partition network, a Swin UNETR segmentation network, and a 3D U-Net classification network, all of which were integrated into the PACS. Additional data from 202 patients with PTC (including 202 LNs) were collected from the aforementioned two centers to evaluate the improvement in radiologists' diagnostic performance with PACS-AI assistance.
limitation: 
tag: Evidence-backed problem
key_points: The objective of this study is to develop and validate a Picture Archiving and Communication System-integrated artificial intelligence (PACS-AI) tool for automated neck-level localization, three-dimensional (3D) segmentation, and metastasis risk prediction of cervical lymph nodes (LNs) in patients with papillary thyroid carcinoma (PTC). | CT images from 710 patients with PTC, including 4942 LNs, were retrospectively collected from two medical centers and divided into training, internal test, and external test cohorts. | The AI model consisted of a 3D TransUNet partition network, a Swin UNETR segmentation network, and a 3D U-Net classification network, all of which were integrated into the PACS.
rundown: The objective of this study is to develop and validate a Picture Archiving and Communication System-integrated artificial intelligence (PACS-AI) tool for automated neck-level localization, three-dimensional (3D) segmentation, and metastasis risk prediction of cervical lymph nodes (LNs) in patients with papillary thyroid carcinoma (PTC). CT images from 710 patients with PTC, including 4942 LNs, were retrospectively collected from two medical centers and divided into training, internal test, and external test cohorts.

The AI model consisted of a 3D TransUNet partition network, a Swin UNETR segmentation network, and a 3D U-Net classification network, all of which were integrated into the PACS. Additional data from 202 patients with PTC (including 202 LNs) were collected from the aforementioned two centers to evaluate the improvement in radiologists' diagnostic performance with PACS-AI assistance.
sources:
- peer_reviewed | Journal of Imaging Informatics in Medicine | https://doi.org/10.1007/s10278-026-02275-6 | 2026-09-18
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