Evidence map›Paper›PMID 34604085›Full record

ArticleFrontiers in oncology2021

Predicting Response to Systemic Chemotherapy for Advanced Gastric Cancer Using Pre-Treatment Dual-Energy CT Radiomics: A Pilot Study.

Yi-Yang Liu, Huan Zhang, Lan Wang, Shu-Shen Lin, Hao Lu, He-Jun Liang, Pan Liang, Jun Li, Pei-Jie Lv, Jian-Bo Gao

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 2 pooled it
1.8field-weighted citation impact, top 15% 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, 2 syntheses or guidelines pooled it, 17 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

10 authors at 4 institutions in 2 countries.

Yi-Yang LiuDepartment of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Huan ZhangDepartment of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Lan WangDepartment of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Shu-Shen LinDepartment of DI CT Collaboration, Siemens Healthineers Ltd, Shanghai, China.
Hao LuDepartment of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
He-Jun LiangDepartment of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Pan LiangDepartment of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Jun LiDepartment of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Pei-Jie LvDepartment of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Jian-Bo GaoDepartment of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
First Affiliated Hospital of Zhengzhou University · CNShanxi Provincial Cancer Hospital · CNRuijin Hospital · CNSiemens Healthcare (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo build and assess a pre-treatment dual-energy CT-based clinical-radiomics nomogram for the individualized prediction of clinical response to systemic chemotherapy in advanced gastric cancer (AGC).

methodsA total of 69 pathologically confirmed AGC patients who underwent dual-energy CT before systemic chemotherapy were enrolled from two centers in this retrospective study. Treatment response was determined with follow-up CT according to the RECIST standard. Quantitative radiomics metrics of the primary lesion were extracted from three sets of monochromatic images (40, 70, and 100 keV) at venous phase. Univariate analysis and least absolute shrinkage and selection operator (LASSO) were used to select the most relevant radiomics features. Multivariable logistic regression was performed to establish a clinical model, three monochromatic radiomics models, and a combined multi-energy model. ROC analysis and DeLong test were used to evaluate and compare the predictive performance among models. A clinical-radiomics nomogram was developed; moreover, its discrimination, calibration, and clinical usefulness were assessed.

resultAmong the included patients, 24 responded to the systemic chemotherapy. Clinical stage and the iodine concentration (IC) of the tumor were significant clinical predictors of chemotherapy response (all

conclusionThe pre-treatment DECT-based clinical-radiomics nomogram showed good performance in predicting clinical response to systemic chemotherapy in AGC, which may contribute to clinical decision-making and improving patient survival.

Indexed as

dual-energy CTgastric cancerradiomicsresponse predictionsystemic chemotherapy

Identifiers

PMID34604085
PMCPMC8480311
OpenAlexW3200648458

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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.