Evidence map›Paper›PMID 42443649›Full record

ArticleJournal of imaging informatics in medicine2026

Pilot Study on Contrast-Enhanced CT-Based 3D Cascaded Segmentation of Hypopharyngeal Cancer and the Stability of the Extracted Radiomics Features.

Wu Du, Bingyi Wu, Zheng Wang, Ruoshi Liu, Shiwan Zhang, Wei Li, Yan Wang, He Ma, Jia Gu

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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.

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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

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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.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Wu Du *College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110819, Liaoning, China.
Bingyi Wu *Department of Otolaryngology, The First Affiliated Hospital of China Medical University, Shenyang, 110001, Liaoning, China.
Zheng Wang *Department of Otolaryngology, The First Affiliated Hospital of China Medical University, Shenyang, 110001, Liaoning, China.
Ruoshi Liu *Department of Chinese Medicine, The First Affiliated Hospital of China Medical University, Shenyang, 110001, Liaoning, China.
Shiwan ZhangCollege of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110819, Liaoning, China.
Wei LiDepartment of Otolaryngology, The First Affiliated Hospital of China Medical University, Shenyang, 110001, Liaoning, China.
Yan WangDepartment of Otolaryngology, The First Affiliated Hospital of China Medical University, Shenyang, 110001, Liaoning, China. wangyanoto@aliyun.com.
He MaCollege of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110819, Liaoning, China. mahe@bmie.neu.edu.cn.ORCID http://orcid.org/0000-0002-5054-3586
Jia GuDepartment of Chinese Medicine, The First Affiliated Hospital of China Medical University, Shenyang, 110001, Liaoning, China. gujia19840128@aliyun.com.

Funding

Promoting Newborn Public Welfare Project GYLZH41the Liaoning Provincial Department of Science Technology Fund 2022YGJC43the National Natural Science Foundation of China 82172032the Natural Science Foundation of Liaoning Province 2021MS183the Shenyang Science and Technology Bureau Project 2350630130
6 · The paper itself

Abstract

Hypopharyngeal cancer (HPC), a malignant head and neck tumor with poor prognosis, requires accurate pretreatment segmentation for surgical planning and radiomics analysis. However, no prior research has reported automated segmentation of hypopharyngeal cancer on CECT images or investigated the impact of such segmentation on radiomics features. This study developed a 3D cascaded HPCNet framework for automated HPC segmentation on CECT images and evaluated the stability of extracted radiomics features. Pretreatment CECT images of 180 patients with HPC were retrospectively acquired, and a 5-fold cross-validation strategy was adopted for dataset division. The cascaded HPCNet framework significantly outperformed the nnUNet in the segmentation of HPC, achieving the highest median DSC (0.793 vs. 0.768, p

Indexed as

Cascaded segmentationComputed tomographyDeep learningHypopharyngeal cancer

Identifiers

PMID42443649

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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.