Evidence map›Paper›PMID 41473240›Full record

ArticleFrontiers in endocrinology2025

Prediction of

Hongjun Song, Manman Fei, Haoyi Tao, Zhongling Qiu, Chentian Shen, Xiaoyue Chen, Qiong Luo, Huajun She, Qian Wang, Lichi Zhang and 1 more

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in endocrinology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
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.

Hongjun Song *Department of Nuclear Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Manman Fei *School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Haoyi TaoSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Zhongling QiuDepartment of Nuclear Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Chentian ShenDepartment of Nuclear Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Xiaoyue ChenDepartment of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Qiong LuoDepartment of Nuclear Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China.
Huajun SheSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Qian WangSchool of Biomedical Engineering, ShanghaiTech University, Shanghai, China.
Lichi Zhang *School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Quanyong Luo *Department of Nuclear Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: An accurate assessment of Methods: In this retrospective, multicenter, population-based cohort study, we collected chest CT image datasets for DTC patients with lung metastases from three hospitals in China. Pulmonary metastases were classified into two categories based on the post therapeutic Results: Compared to Inception V3 and ResNet50, our model, ResNeSt50, demonstrated the highest prediction performance in the internal (area under the curve [AUC] = 0.722, 95% confidence interval [CI] = 0.716-0.725), external validation dataset 1 (AUC = 0.720, 95% CI = 0.691-0.749), and external validation dataset 2 (AUC = 0.731, 95% CI = 0.713-0.748). Conclusion: We developed a simple and robust DCNN model for predicting the Trial registration: Chinese Clinical Trial Registry (ChiCTR), ChiCTR1800018047. Registered on 28 August 2018.

Indexed as

Deep LearningIodine RadioisotopesLung NeoplasmsThyroid NeoplasmsAdultAgedFemaleHumansMaleMiddle AgedRetrospective StudiesIodine-131Iodine RadioisotopesCNN - convolutional neural networkdeep learninglung metastasesradioiodine therapythyroid cancer

Identifiers

PMID41473240
PMCPMC12745282

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.