Evidence map›Paper›PMID 36276058›Full record

ArticleFrontiers in oncology2022

Feasibility of a CT-based lymph node radiomics nomogram in detecting lymph node metastasis in PDAC patients.

Qian Li, Zuhua Song, Dan Zhang, Xiaojiao Li, Qian Liu, Jiayi Yu, Zongwen Li, Jiayan Zhang, Xiaofang Ren, Youjia Wen and 1 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
0.3field-weighted citation impact, top 40% 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

3 citing papers in PubMed, 1 synthesis or guideline pooled it, 3 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. 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 at 3 institutions in 1 country.

Qian LiDepartment of Radiology, Chongqing Medical University, Chongqing, China.
Zuhua SongDepartment of Radiology, Chongqing General Hospital, Chongqing, China.
Dan ZhangDepartment of Radiology, Chongqing General Hospital, Chongqing, China.
Xiaojiao LiDepartment of Radiology, Chongqing General Hospital, Chongqing, China.
Qian LiuDepartment of Radiology, Chongqing General Hospital, Chongqing, China.
Jiayi YuDepartment of Radiology, Chongqing General Hospital, Chongqing, China.
Zongwen LiDepartment of Radiology, Chongqing General Hospital, Chongqing, China.
Jiayan ZhangDepartment of Radiology, Chongqing General Hospital, Chongqing, China.
Xiaofang RenDepartment of Radiology, Chongqing General Hospital, Chongqing, China.
Youjia WenDepartment of Radiology, Chongqing General Hospital, Chongqing, China.
Zhuoyue TangDepartment of Radiology, Chongqing Medical University, Chongqing, China.
First People's Hospital of Chongqing · CNChongqing Medical University · CNUniversity of Chinese Academy of Sciences · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: To investigate the potential value of a contrast enhanced computed tomography (CECT)-based radiological-radiomics nomogram combining a lymph node (LN) radiomics signature and LNs' radiological features for preoperative detection of LN metastasis in patients with pancreatic ductal adenocarcinoma (PDAC). Materials and methods: In this retrospective study, 196 LNs in 61 PDAC patients were enrolled and divided into the training (137 LNs) and validation (59 LNs) cohorts. Radiomic features were extracted from portal venous phase images of LNs. The least absolute shrinkage and selection operator (LASSO) regression algorithm with 10-fold cross-validation was used to select optimal features to determine the radiomics score (Rad-score). The radiological-radiomics nomogram was developed by using significant predictors of LN metastasis by multivariate logistic regression (LR) analysis in the training cohort and validated in the validation cohort independently. Its diagnostic performance was assessed by receiver operating characteristic curve (ROC), decision curve (DCA) and calibration curve analyses. Results: The radiological model, including LN size, and margin and enhancement pattern (three significant predictors), exhibited areas under the curves (AUCs) of 0.831 and 0.756 in the training and validation cohorts, respectively. Nine radiomic features were used to construct a radiomics model, which showed AUCs of 0.879 and 0.804 in the training and validation cohorts, respectively. The radiological-radiomics nomogram, which incorporated the LN Rad-score and the three LNs' radiological features, performed better than the Rad-score and radiological models individually, with AUCs of 0.937 and 0.851 in the training and validation cohorts, respectively. Calibration curve analysis and DCA revealed that the radiological-radiomics nomogram showed satisfactory consistency and the highest net benefit for preoperative diagnosis of LN metastasis. Conclusions: The CT-based LN radiological-radiomics nomogram may serve as a valid and convenient computer-aided tool for personalized risk assessment of LN metastasis and help clinicians make appropriate clinical decisions for PADC patients.

Indexed as

computed tomographylymph node metastasisnomogrampancreatic ductal adenocarcinomaradiomics

Identifiers

PMID36276058
PMCPMC9579427
OpenAlexW4301401188

What OpenQuestion holds

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LicenceCC BY
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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.