Evidence map›Paper›PMID 35451626›Full record

ArticleAbdominal radiology (New York)2022

CT radiomics signature: a potential biomarker for fibroblast activation protein expression in patients with pancreatic ductal adenocarcinoma.

Yinghao Meng, Jieyu Yu, Mengmeng Zhu, Jian Zhou, Na Li, Fang Liu, Hao Zhang, Xu Fang, Jing Li, Xiaocheng Feng and 5 more

Abstract read
PubMed Publisher
In one paragraph

Article in Abdominal radiology (New York), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.1field-weighted citation impact, top 56% 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

2 citing papers in PubMed, 1 citations in OpenAlex.

  1. Article
  2. 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

15 authors at 2 institutions in 1 country.

Yinghao Meng *Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China.
Jieyu Yu *Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China.
Mengmeng ZhuDepartment of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China.
Jian ZhouDepartment of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China.
Na LiDepartment of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China.
Fang LiuDepartment of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China.
Hao ZhangDepartment of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China.
Xu FangDepartment of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China.
Jing LiDepartment of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China.
Xiaocheng FengDepartment of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China.
Li WangDepartment of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China.
Hui JiangDepartment of Pathology, Changhai Hospital, Naval Medical University, Shanghai, China.
Jianping LuDepartment of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China.
Chengwei ShaoDepartment of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China. cwshao@sina.com.
Yun BianDepartment of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China. bianyun2012@foxmail.com.
Second Military Medical University · CNChanghai Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo develop and validate a radiomics model to predict fibroblast activation protein (FAP) expression in patients with pancreatic ductal adenocarcinoma (PDAC).

methodsThis retrospective study included consecutive 152 patients with PDAC who underwent MDCT scan and surgical resection from January 2017 to December 2017 (training set) and from January 2018 to April 2018 (validation set). In the training set, 1409 portal radiomic features were extracted from each patient's preoperative imaging. Optimal features were selected using the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm, whereupon the extreme gradient boosting (XGBoost) was developed using the radiomics features. The performance of the XGBoost classifier performance was assessed by its calibration, discrimination, and clinical usefulness.

resultsThe patients were divided into FAP-low (n = 91; 59.87%) and FAP-high (n = 61; 40.13%) groups according to the optimal FAP cutoff (45.71%). Patients in the FAP-low group showed longer survival. The XGBoost classifier comprised 13 selected radiomics features and showed good discrimination in the training set [area under the curve (AUC), 0.97] and the validation set (AUC, 0.75). It also performed well in the calibration test and decision-curve analysis, demonstrating its potential clinical value.

conclusionsThe XGBoost classifier based on CT radiomics in the portal venous phase can non-invasively predict FAP expression and may help to improve clinical decision-making in patients with PDAC.

Indexed as

Carcinoma, Pancreatic DuctalPancreatic NeoplasmsBiomarkersFibroblastsHumansRetrospective StudiesTomography, X-Ray ComputedBiomarkersCarcinomaFibroblast activation proteinMultidetector computed tomographyPancreatic neoplasmRadiomics

Identifiers

PMID35451626
OpenAlexW4224247346

What OpenQuestion holds

Textmetadata
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.