Evidence map›Paper›PMID 38353857›Full record

ArticleInsights into imaging2024

Development and validation of a model for predicting the expression of Ki-67 in pancreatic ductal adenocarcinoma with radiological features and dual-energy computed tomography quantitative parameters.

Youjia Wen, Zuhua Song, Qian Li, Dan Zhang, Xiaojiao Li, Jiayi Yu, Zongwen Li, Xiaofang Ren, Jiayan Zhang, Qian Liu and 3 more

Open access · goldAbstract read
In one paragraph

Article in Insights into imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
3.4field-weighted citation impact, top 7% 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

15 citing papers in PubMed, 12 citations in OpenAlex.

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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

13 authors at 2 institutions in 1 country.

Youjia WenChongqing General Hospital, No.118, Xingguang Avenue, Liangjiang New Area, Chongqing, China.
Zuhua SongChongqing General Hospital, No.118, Xingguang Avenue, Liangjiang New Area, Chongqing, China.
Qian LiChongqing General Hospital, No.118, Xingguang Avenue, Liangjiang New Area, Chongqing, China.
Dan ZhangChongqing General Hospital, No.118, Xingguang Avenue, Liangjiang New Area, Chongqing, China.
Xiaojiao LiChongqing General Hospital, No.118, Xingguang Avenue, Liangjiang New Area, Chongqing, China.
Jiayi YuChongqing General Hospital, No.118, Xingguang Avenue, Liangjiang New Area, Chongqing, China.
Zongwen LiChongqing General Hospital, No.118, Xingguang Avenue, Liangjiang New Area, Chongqing, China.
Xiaofang RenChongqing General Hospital, No.118, Xingguang Avenue, Liangjiang New Area, Chongqing, China.
Jiayan ZhangChongqing General Hospital, No.118, Xingguang Avenue, Liangjiang New Area, Chongqing, China.
Qian LiuChongqing General Hospital, No.118, Xingguang Avenue, Liangjiang New Area, Chongqing, China.
Jie HuangChongqing General Hospital, No.118, Xingguang Avenue, Liangjiang New Area, Chongqing, China.
Dan ZengChongqing General Hospital, No.118, Xingguang Avenue, Liangjiang New Area, Chongqing, China.
Zhuoyue TangChongqing General Hospital, No.118, Xingguang Avenue, Liangjiang New Area, Chongqing, China. zhuoyue_tang@cqmu.edu.cn.
First People's Hospital of Chongqing · CNXinqiao Hospital · CN

Funding

Medical Research Joint Key Project of Chongqing Health Commission and Science and Technology Bureau in China 2019ZDXM010Medical Scientific Research Joint Project of Chongqing Health Commission and Science and Technology Bureau in China 2020FYYX151
6 · The paper itself

Abstract

objectiveTo construct and validate a model based on the dual-energy computed tomography (DECT) quantitative parameters and radiological features to predict Ki-67 expression levels in pancreatic ductal adenocarcinoma (PDAC). MATERIALS AND

methodsData from 143 PDAC patients were analysed. The variables of clinic, radiology and DECT were evaluated. In the arterial phase and portal venous phase (PVP), the normalized iodine concentration (NIC), normalized effective atomic number and slope of the spectral attenuation curves were measured. The extracellular volume fraction (ECVf) was measured in the equilibrium phase. Univariate analysis was used to screen independent risk factors to predict Ki-67 expression. The Radiology, DECT and DECT-Radiology models were constructed, and their diagnostic effectiveness and clinical applicability were obtained through area under the curve (AUC) and decision curve analysis, respectively. The nomogram was established based on the optimal model, and its goodness-of-fit was assessed by a calibration curve.

resultsComputed tomography reported regional lymph node status, NIC of PVP, and ECVf were independent predictors for Ki-67 expression prediction. The AUCs of the Radiology, DECT, and DECT-Radiology models were 0.705, 0.884, and 0.905, respectively, in the training cohort, and 0.669, 0.835, and 0.865, respectively, in the validation cohort. The DECT-Radiology nomogram was established based on the DECT-Radiology model, which showed the highest net benefit and satisfactory consistency.

conclusionsThe DECT-Radiology model shows favourable predictive efficacy for Ki-67 expression, which may be of value for clinical decision-making in PDAC patients. CRITICAL RELEVANCE STATEMENT: The DECT-Radiology model could contribute to the preoperative and non-invasive assessment of Ki-67 expression of PDAC, which may help clinicians to screen out PDAC patients with high Ki-67 expression. KEY POINTS: • Dual-energy computed tomography (DECT) can predict Ki-67 in pancreatic ductal adenocarcinoma (PDAC). • The DECT-Radiology model facilitates preoperative and non-invasive assessment of PDAC Ki-67 expression. • The nomogram may help screen out PDAC patients with high Ki-67 expression.

Indexed as

Dual-energy computed tomography (DECT)Ki-67NomogramPancreatic ductal adenocarcinoma (PDAC)Prognosis

Identifiers

PMID38353857
PMCPMC10866831
OpenAlexW4391818204

What OpenQuestion holds

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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.