Evidence map›Paper›PMID 36779997›Full record

ArticleEJNMMI research2023

Prognostic value of

Bingzhen Wang, Jinghua Liu, Xiaolei Zhang, Zhongxiao Wang, Zhendong Cao, Lijun Lu, Wenbing Lv, Aihui Wang, Shuyan Li, Xiaotian Wu and 1 more

Abstract read
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In one paragraph

Article in EJNMMI research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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

5 citing papers in PubMed.

  1. Review
  2. Review
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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

11 authors.

Bingzhen WangDepartment of Biomedical Engineering, Chengde Medical University, Chengde, Hebei, China.
Jinghua LiuDepartment of Nursing, Chengde Central Hospital, Chengde, Hebei, China.
Xiaolei ZhangDepartment of Biomedical Engineering, Chengde Medical University, Chengde, Hebei, China.
Zhongxiao WangDepartment of Biomedical Engineering, Chengde Medical University, Chengde, Hebei, China.
Zhendong CaoDepartment of Radiology, The Affiliated Hospital of Chengde Medical University, Chengde, Hebei, China.
Lijun LuSchool of Biomedical Engineering and Guangdong Provincal Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, China.
Wenbing LvDepartment of Electronic Engineering, Information School, Yunnan University, Kunming, Yunnan, China.
Aihui WangDepartment of Nuclear Medicine, The Affiliated Hospital of Chengde Medical University, Chengde, Hebei, China.
Shuyan LiDepartment of Biomedical Engineering, Chengde Medical University, Chengde, Hebei, China.
Xiaotian WuDepartment of Biomedical Engineering, Chengde Medical University, Chengde, Hebei, China.
Xianling DongDepartment of Biomedical Engineering, Chengde Medical University, Chengde, Hebei, China. dongxl@cdmc.edu.cn.ORCID http://orcid.org/0000-0001-8572-2632

Funding

Hebei Province Introduced Returned Overseas Chinese Scholars Funding Project C20220107Natural Science Foundation of Hebei Province C2022406010
6 · The paper itself

Abstract

objectivesBy comparing the prognostic performance of MATERIALS AND

methodsA total of 220 cases from four centres based on the Cancer Imaging Archive public dataset were used in this study, 2260 radiomics features and 1116 dosiomics features and 8 DVH features were extracted for each case, and classified into seven different models of PET, CT, Dose, PET+CT, PET+Dose, CT+Dose and PET+CT+Dose. Features were selected by univariate Cox and Spearman correlation coefficients, and the selected features were brought into the least absolute shrinkage and selection operator (LASSO)-Cox model. A nomogram was constructed to visually analyse the prognostic impact of the incorporated dose features. C-index and Kaplan-Meier curves (log-rank analysis) were used to evaluate and compare these models.

resultsThe cases from the four centres were divided into three different training and validation sets according to the hospitals. The PET+CT+Dose model had C-indexes of 0.873 (95% CI 0.812-0.934), 0.759 (95% CI 0.663-0.855) and 0.835 (95% CI 0.745-0.925) in the validation set respectively, outperforming the rest models overall. The PET+CT+Dose model did well in classifying patients into high- and low-risk groups under all three different sets of experiments (p < 0.05).

conclusionMultidimensional model of radiomics features combining dosiomics features and DVH features showed high prognostic performance for predicting OS in patients with HNC.

Indexed as

DosiomicsDVHHead and neck cancerPET/CTPrognosisRadiomics

Identifiers

PMID36779997
PMCPMC9925656

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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.