Evidence map›Paper›PMID 33408793›Full record

ArticleTheranostics2021

Fully end-to-end deep-learning-based diagnosis of pancreatic tumors.

Ke Si, Ying Xue, Xiazhen Yu, Xinpei Zhu, Qinghai Li, Wei Gong, Tingbo Liang, Shumin Duan

Open access · goldAbstract read
In one paragraph

Article in Theranostics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
46citing papers in PubMed, 4 pooled it
6.6field-weighted citation impact, top 2% of its field
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

46 citing papers in PubMed, 4 syntheses or guidelines pooled it, 117 citations in OpenAlex.

  1. Pooled it
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  4. Pooled it
  5. Article
  6. Article
  7. Article
  8. Pancreatic cancer in 2025: Have we found a solution?World journal of gastroenterology · 2025
    Review
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  15. Article
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  20. Data privacy-aware machine learning approach in pancreatic cancer diagnosis.BMC medical informatics and decision making · 2024
    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

8 authors at 1 institution in 1 country.

Ke SiDepartment of Hepatobiliary and Pancreatic Surgery of the First Affiliated Hospital, State Key Laboratory of Modern Optical Instrumentation, Zhejiang University School of Medicine, Hangzhou 310003, China.
Ying XueCollege of Optical Science and Engineering, Zhejiang University, Hangzhou 310027, China.
Xiazhen YuDepartment of Hepatobiliary and Pancreatic Surgery of the First Affiliated Hospital, State Key Laboratory of Modern Optical Instrumentation, Zhejiang University School of Medicine, Hangzhou 310003, China.
Xinpei ZhuResearch Units for Emotion and Emotion Disorders, Chinese Academy of Medical Sciences, MOE Frontier Science Center for Brain Research and Brain-Machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou 310058, China.
Qinghai LiDepartment of Radiology of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China.
Wei GongResearch Units for Emotion and Emotion Disorders, Chinese Academy of Medical Sciences, MOE Frontier Science Center for Brain Research and Brain-Machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou 310058, China.
Tingbo LiangDepartment of Hepatobiliary and Pancreatic Surgery of the First Affiliated Hospital, State Key Laboratory of Modern Optical Instrumentation, Zhejiang University School of Medicine, Hangzhou 310003, China.
Shumin DuanResearch Units for Emotion and Emotion Disorders, Chinese Academy of Medical Sciences, MOE Frontier Science Center for Brain Research and Brain-Machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou 310058, China.
Zhejiang University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence can facilitate clinical decision making by considering massive amounts of medical imaging data. Various algorithms have been implemented for different clinical applications. Accurate diagnosis and treatment require reliable and interpretable data. For pancreatic tumor diagnosis, only 58.5% of images from the First Affiliated Hospital and the Second Affiliated Hospital, Zhejiang University School of Medicine are used, increasing labor and time costs to manually filter out images not directly used by the diagnostic model.

Indexed as

AlgorithmsDeep LearningAdultAgedAged, 80 and overCarcinoma, Pancreatic DuctalFemaleHumansImage Processing, Computer-AssistedMaleMiddle AgedPancreatic NeoplasmsROC CurveTomography, X-Ray Computedartificial intelligence (AI)computed tomography (CT)convolutional neural network (CNN)deep learningtumor

Identifiers

PMID33408793
PMCPMC7778580
OpenAlexW3112046093

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.