Evidence map›Paper›PMID 34221979›Full record

ArticleFrontiers in oncology2021

Total Lesion Glycolysis Estimated by a Radiomics Model From CT Image Alone.

Hongwei Si, Xinzhong Hao, Lianyu Zhang, Xiaokai Xu, Jianzhong Cao, Ping Wu, Li Li, Zhifang Wu, Shengyang Zhang, Sijin Li

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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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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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

1 citing paper in PubMed.

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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.

Hongwei SiDepartment of Nuclear Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Xinzhong HaoNuclear Medicine, The First Affiliated Hospital of Shanxi Medical University, Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Shanxi Medical University, Taiyuan, China.
Lianyu ZhangDepartment of Diagnostic Imaging, National Cancer Center/ Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Xiaokai XuDepartment of Nuclear Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Jianzhong CaoDepartment of Radiation Oncology, The Cancer Hospital of Shanxi Province, Taiyuan, China.
Ping WuNuclear Medicine, The First Affiliated Hospital of Shanxi Medical University, Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Shanxi Medical University, Taiyuan, China.
Li LiDepartment of Nuclear Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Zhifang WuNuclear Medicine, The First Affiliated Hospital of Shanxi Medical University, Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Shanxi Medical University, Taiyuan, China.
Shengyang ZhangDepartment of Nuclear Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Sijin LiNuclear Medicine, The First Affiliated Hospital of Shanxi Medical University, Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Shanxi Medical University, Taiyuan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeIn this study, total lesion glycolysis (TLG) on positron emission tomography images was estimated by a trained and validated CT radiomics model, and its prognostic ability was explored among lung cancer (LC) and esophageal cancer patients (EC).

methodsUsing the identical features between the combined and thin-section CT, the estimation model of SUVsum (summed standard uptake value) was trained from the lymph nodes (LNs) of LC patients (n = 1239). Besides LNs of LC patients from other centers, the validation cohorts also included LNs and primary tumors of LC/EC from the same center. After calculating TLG (accumulated SUVsum of each individual) based on the model, the prognostic ability of the estimated and measured values was compared and analyzed.

resultsIn the training cohort, the model of 3 features was trained by the deep learning and linear regression method. It performed well in all validation cohorts (n = 5), and a linear regression could correct the bias from different scanners. Additionally, the absolute biases of the model were not significantly affected by the evaluated factors whether they included LN metastasis or not. Between the estimated natural logarithm of TLG (elnTLG) and the measured values (mlnTLG), significant difference existed among both LC (n = 137, bias = 0.510 ± 0.519, r = 0.956, P<0.001) and EC patients (n = 56, bias = 0.251± 0.463, r = 0.934, P<0.001). However, for both cancers, the overall shapes of the curves of hazard ratio (HR) against elnTLG or mlnTLG were quite alike.

conclusionTotal lesion glycolysis can be estimated by three CT features with particular coefficients for different scanners, and it similar to the measured values in predicting the outcome of cancer patients.

Indexed as

computed tomographypositron emission tomographyradiomicsstandard uptake valuetotal lesion glycolysis

Identifiers

PMID34221979
PMCPMC8247448

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