Evidence map›Paper›PMID 36066369›Full record

ArticleRadiology2023

Artificial Intelligence to Predict Lymph Node Metastasis at CT in Pancreatic Ductal Adenocarcinoma.

Yun Bian, Zhilin Zheng, Xu Fang, Hui Jiang, Mengmeng Zhu, Jieyu Yu, Haiyan Zhao, Ling Zhang, Jiawen Yao, Le Lu and 2 more

Registry-linked trialAbstract read
PubMed Publisher
In one paragraph

Article in Radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06421844 (A Prospective Study), which is not on this map. Cited by 65 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
65citing papers in PubMed, 7 pooled it
12.0field-weighted citation impact, top 1% 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.

NCT06421844 unknown statusnot on this mapstarted 2024, after this paper: background citation

A Prospective Study: A Computer Vision Model on a Smartphone Platform for Real-time Evaluation of Serum Bilirubin Through Sclera Images

Typeobservational_patient_registrySponsorAir Force Military Medical University, ChinaRan2024 to 2024Enrolled270ConditionsJaundice, Deep Learning, Hyperbilirubinemia, Sclera
3 · Its place in the literature

Who cites it

65 citing papers in PubMed, 7 syntheses or guidelines pooled it, 121 citations in OpenAlex.

  1. Pooled it
  2. Artificial Intelligence in Pancreatic Imaging: A Systematic Review.United European gastroenterology journal · 2025
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  12. [Cancer imaging : the official publication of the International Cancer Imaging Society · 2026
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5 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors at 1 institution in 1 country.

Yun BianFrom the Departments of Radiology (Y.B., X.F., M.Z., J. Yu, H.Z., J.L., C.S.) and Pathology (H.J.), Changhai Hospital, 168 Changhai Road, Shanghai 200433, China; Ping An Technology, Shanghai, China (Z.Z.); and PAII Inc, Bethesda, Md (L.Z., J. Yao, L.L.).ORCID 0000-0002-4863-4956
Zhilin ZhengFrom the Departments of Radiology (Y.B., X.F., M.Z., J. Yu, H.Z., J.L., C.S.) and Pathology (H.J.), Changhai Hospital, 168 Changhai Road, Shanghai 200433, China; Ping An Technology, Shanghai, China (Z.Z.); and PAII Inc, Bethesda, Md (L.Z., J. Yao, L.L.).ORCID 0000-0002-2439-9162
Xu FangFrom the Departments of Radiology (Y.B., X.F., M.Z., J. Yu, H.Z., J.L., C.S.) and Pathology (H.J.), Changhai Hospital, 168 Changhai Road, Shanghai 200433, China; Ping An Technology, Shanghai, China (Z.Z.); and PAII Inc, Bethesda, Md (L.Z., J. Yao, L.L.).ORCID 0000-0001-5795-9843
Hui JiangFrom the Departments of Radiology (Y.B., X.F., M.Z., J. Yu, H.Z., J.L., C.S.) and Pathology (H.J.), Changhai Hospital, 168 Changhai Road, Shanghai 200433, China; Ping An Technology, Shanghai, China (Z.Z.); and PAII Inc, Bethesda, Md (L.Z., J. Yao, L.L.).ORCID 0000-0003-3972-1297
Mengmeng ZhuFrom the Departments of Radiology (Y.B., X.F., M.Z., J. Yu, H.Z., J.L., C.S.) and Pathology (H.J.), Changhai Hospital, 168 Changhai Road, Shanghai 200433, China; Ping An Technology, Shanghai, China (Z.Z.); and PAII Inc, Bethesda, Md (L.Z., J. Yao, L.L.).ORCID 0000-0001-9690-1150
Jieyu YuFrom the Departments of Radiology (Y.B., X.F., M.Z., J. Yu, H.Z., J.L., C.S.) and Pathology (H.J.), Changhai Hospital, 168 Changhai Road, Shanghai 200433, China; Ping An Technology, Shanghai, China (Z.Z.); and PAII Inc, Bethesda, Md (L.Z., J. Yao, L.L.).ORCID 0000-0002-8701-7440
Haiyan ZhaoFrom the Departments of Radiology (Y.B., X.F., M.Z., J. Yu, H.Z., J.L., C.S.) and Pathology (H.J.), Changhai Hospital, 168 Changhai Road, Shanghai 200433, China; Ping An Technology, Shanghai, China (Z.Z.); and PAII Inc, Bethesda, Md (L.Z., J. Yao, L.L.).ORCID 0000-0002-5144-0255
Ling ZhangFrom the Departments of Radiology (Y.B., X.F., M.Z., J. Yu, H.Z., J.L., C.S.) and Pathology (H.J.), Changhai Hospital, 168 Changhai Road, Shanghai 200433, China; Ping An Technology, Shanghai, China (Z.Z.); and PAII Inc, Bethesda, Md (L.Z., J. Yao, L.L.).ORCID 0000-0001-8371-5252
Jiawen YaoFrom the Departments of Radiology (Y.B., X.F., M.Z., J. Yu, H.Z., J.L., C.S.) and Pathology (H.J.), Changhai Hospital, 168 Changhai Road, Shanghai 200433, China; Ping An Technology, Shanghai, China (Z.Z.); and PAII Inc, Bethesda, Md (L.Z., J. Yao, L.L.).ORCID 0000-0003-3236-5410
Le LuFrom the Departments of Radiology (Y.B., X.F., M.Z., J. Yu, H.Z., J.L., C.S.) and Pathology (H.J.), Changhai Hospital, 168 Changhai Road, Shanghai 200433, China; Ping An Technology, Shanghai, China (Z.Z.); and PAII Inc, Bethesda, Md (L.Z., J. Yao, L.L.).ORCID 0000-0002-6799-9416
Jianping Lu *From the Departments of Radiology (Y.B., X.F., M.Z., J. Yu, H.Z., J.L., C.S.) and Pathology (H.J.), Changhai Hospital, 168 Changhai Road, Shanghai 200433, China; Ping An Technology, Shanghai, China (Z.Z.); and PAII Inc, Bethesda, Md (L.Z., J. Yao, L.L.).ORCID 0000-0003-1393-9212
Chengwei Shao *From the Departments of Radiology (Y.B., X.F., M.Z., J. Yu, H.Z., J.L., C.S.) and Pathology (H.J.), Changhai Hospital, 168 Changhai Road, Shanghai 200433, China; Ping An Technology, Shanghai, China (Z.Z.); and PAII Inc, Bethesda, Md (L.Z., J. Yao, L.L.).ORCID 0000-0003-1640-9663
Second Military Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background Although deep learning has brought revolutionary changes in health care, reliance on manually selected cross-sectional images and segmentation remain methodological barriers. Purpose To develop and validate an automated preoperative artificial intelligence (AI) algorithm for tumor and lymph node (LN) segmentation with CT imaging for prediction of LN metastasis in patients with pancreatic ductal adenocarcinoma (PDAC). Materials and Methods In this retrospective study, patients with surgically resected, pathologically confirmed PDAC underwent multidetector CT from January 2015 to April 2020. Three models were developed, including an AI model, a clinical model, and a radiomics model. CT-determined LN metastasis was diagnosed by radiologists. Multivariable logistic regression analysis was conducted to develop the clinical and radiomics models. The performance of the models was determined on the basis of their discrimination and clinical utility. Kaplan-Meier curves, the log-rank test, or Cox regression were used for survival analysis. Results Overall, 734 patients (mean age, 62 years ± 9 [SD]; 453 men) were evaluated. All patients were split into training (

Indexed as

Carcinoma, Pancreatic DuctalPancreatic NeoplasmsArtificial IntelligenceHumansLymphatic MetastasisLymph NodesMaleMiddle AgedMultidetector Computed TomographyRetrospective Studies

Identifiers

PMID36066369
OpenAlexW4294755849

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

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.