Evidence map›Paper›PMID 40810801›Full record

ArticleEuropean journal of nuclear medicine and molecular imaging2026

Deep learning-based non-invasive prediction of PD-L1 status and immunotherapy survival stratification in esophageal cancer using [

Fei Xie, Mobei Zhang, Chunhong Zheng, Zhenwei Zhao, Jiarou Wang, Yufan Li, Kai Wang, Wenzhuo Wang, JunJie Lin, Tingting Wu and 7 more

Abstract read
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In one paragraph

Article in European journal of nuclear medicine and molecular imaging, 2026. 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. Article
  2. Review
  3. Dual-tracer PET/CT for lung and prostate cancer: 18F-FDG and 18F-PSMA-1007.European journal of nuclear medicine and molecular imaging · 2025
    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

17 authors.

Fei XieNanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China.
Mobei ZhangDepartment of Electronic engineering, School of Electronic engineering, Xidian University, Xi'an, Shaanxi Province, China.
Chunhong ZhengDepartment of Electronic engineering, School of Electronic engineering, Xidian University, Xi'an, Shaanxi Province, China.
Zhenwei ZhaoNanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China.
Jiarou WangDepartment of Nuclear Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Beijing Key Laboratory of Molecular Targeted Diagnosis and Therapy in Nuclear Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.
Yufan LiDepartment of Electronic engineering, School of Electronic engineering, Xidian University, Xi'an, Shaanxi Province, China.
Kai WangNanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China.
Wenzhuo WangNanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China.
JunJie LinNanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China.
Tingting WuNanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China.
Yifan WangNanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China.
Xi ChenNanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China.
Yongchao LiNanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China.
Zhaohui ZhuDepartment of Nuclear Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Beijing Key Laboratory of Molecular Targeted Diagnosis and Therapy in Nuclear Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China. 13611093752@163.com.
Hubing WuNanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China. wuhbym@163.com.
Yinfeng LiDepartment of Electronic engineering, School of Electronic engineering, Xidian University, Xi'an, Shaanxi Province, China. liyinfengcn@163.com.
Qingxing LiuNanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China. smu_lqx@163.com.ORCID 0000-0003-1079-0299

Funding

Guangdong Basic and Applied Basic Research Foundation 2023A1515111181Guangdong Basic and Applied Basic Research Foundation 2024A1515010093Nanfang Hospital Talent Introduction Foundation of Southern Medical University 2023G003National Natural Science Foundation of China 82402321President Foundation of Nanfang Hospital, Southern Medical University 2022B029
6 · The paper itself

Abstract

purposeThis study aimed to develop and validate deep learning models using [

methodsIn this retrospective study, we included 331 EC patients from two centers, dividing them into training, internal validation, and external validation cohorts. Fifty patients who received immunotherapy were followed up. We developed four 3D ResNet10-based models-PET + CT + clinical factors (CPC), PET + CT (PC), PET (P), and CT (C)-using pre-treatment [

resultsThe models demonstrated accurate prediction of PD-L1 status. The areas under the curve (AUCs) for predicting PD-L1 status were as follows: CPC (0.927), PC (0.904), P (0.886), C (0.934), and the clinical model (0.603) in the training cohort; CPC (0.882), PC (0.848), P (0.770), C (0.745), and the clinical model (0.524) in the internal validation cohort; and CPC (0.843), PC (0.806), P (0.759), C (0.667), and the clinical model (0.671) in the external validation cohort. The CPC and PC models exhibited superior predictive performance. Survival analysis revealed that the DLS from most models effectively stratified overall survival and progression-free survival at appropriate cut-off points (P < 0.05), outperforming stratification based on PD-L1 status (combined positive score ≥ 10). Furthermore, incorporating model scores with clinical factors in nomograms enhanced the predictive probability of survival after immunotherapy.

conclusionDeep learning models based on [

Indexed as

B7-H1 AntigenDeep LearningEsophageal NeoplasmsFluorodeoxyglucose F18ImmunotherapyPositron Emission Tomography Computed TomographyAgedFemaleHumansMaleMiddle AgedRetrospective StudiesSurvival AnalysisB7-H1 AntigenCD274 protein, humanFluorodeoxyglucose F18Deep learningEsophageal cancerImmunotherapyPositron emission tomography/Computed tomographyProgrammed death-ligand 1

Identifiers

PMID40810801

What OpenQuestion holds

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Registered trials

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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.