Evidence map›Paper›PMID 40254654›Full record

ArticleAnnals of surgical oncology2025

A Deep Learning Survival Model for Evaluating the Survival Prognosis of Papillary Thyroid Cancer: A Population-Based Cohort Study.

Guibin Zheng, Peng Wei, Danxia Li, Xinna Li, Mark Zafereo, Chao Li, Wenbin Yu, Xiaohong Chen, Haitao Zheng, Xicheng Song and 1 more

Abstract read
PubMed Publisher
In one paragraph

Article in Annals of surgical oncology, 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

11 authors.

Guibin ZhengDepartment of Thyroid Surgery, The Affiliated Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, China.
Peng WeiDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Danxia LiDepartment of Thyroid Surgery, The Affiliated Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, China.
Xinna LiDepartment of Pathology, The Affiliated Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, China.
Mark ZafereoDepartment of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Chao LiDepartment of Head and Neck Surgery, Schoolof Medicine, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, University of ElectronicScience and Technology of China, Chengdu, 610041, China.
Wenbin YuDepartment of Hand and Neck Surgery, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China.
Xiaohong ChenDepartment of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Haitao ZhengDepartment of Thyroid Surgery, The Affiliated Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, China.
Xicheng SongDepartment of Otorhinolaryngology, Head and Neck Surgery, the Affiliated Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, China. songxicheng@126.com.
Guojun LiDepartment of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. gli@mdanderson.org.

Funding

Key R&D Project of Shandong Province 2022CXPT023Taishan Scholars Foundation of Shandong Province NO.ts20190991the Science and Technology Project of Medicine and Health Care in Shandong Province 202304081359
6 · The paper itself

Abstract

backgroundDeep learning can assess the individual survival prognosis in sizeable datasets with intricate underlying processes. However, studies exploring the performance of deep learning survival in papillary thyroid cancer (PTC) are lacking. This study aimed to construct a deep learning model based on clinical risk factors for survival prediction in patients with PTC.

methodsA Cox proportional hazards deep neural network (DeepSurv) was developed and validated by using consecutive patients with PTC from 17 US Surveillance, Epidemiology, and End Results Program (SEER) cancer registries (2000-2020). The performance of the DeepSurv model was further validated on two external test datasets from the University of Texas MD Anderson Cancer Center (MDACC) and The Cancer Genome Atlas (TCGA). Using the survival risk scores at 10 years predicted by the DeepSurv model, we classified patients with PTC into low-risk and high-risk groups and explored their overall survival (OS).

resultsThe concordance index of the DeepSurv model for predicting OS was 0.798 in the SEER test dataset, 0.893 in the MDACC dataset, and 0.848 in the TCGA dataset. The DeepSurv model was capable of classifying patients with PTC into low-risk and high-risk groups according to the survival risk scores at 10 years. Patients in the high-risk group had significantly worse OS than patients in the low-risk group in all three test datasets (all P < 0.001).

conclusionThe DeepSurv model was capable of classifying patients with PTC into low-risk and high-risk groups, which may provide important prognostic information for personalized treatment in patients with PTC.

Indexed as

Deep LearningThyroid Cancer, PapillaryThyroid NeoplasmsAdultAgedFemaleFollow-Up StudiesHumansMaleMiddle AgedPrognosisRisk FactorsSEER ProgramSurvival RateDeep learning survival modelOverall survivalPapillary thyroid cancerRisk stratification

Identifiers

PMID40254654

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.