Evidence map›Paper›PMID 34977279›Full record

ArticleEuropean journal of radiology open2022

Preliminary exploration of response the course of radiotherapy for stage III non-small cell lung cancer based on longitudinal CT radiomics features.

Ruiping Zhang, Zhengting Cai, Yan'an Luo, Zhizhen Wang, Wei Wang

Abstract read
In one paragraph

Article in European journal of radiology open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 3 pooled it
–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

4 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Review
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

5 authors.

Ruiping ZhangDepartment of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Huanhu West Road, Hexi District, Tianjin 300060, China.
Zhengting CaiSchool of Automation (Artificial Intelligence), Hangzhou Dianzi University, Xiasha Higher Education Zone, Hangzhou, Zhejiang Province 310018, China.
Yan'an LuoDepartment of Physics, Nankai University, Weijin Road, Nankai District, Tianjin 300071, China.
Zhizhen WangDepartment of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Huanhu West Road, Hexi District, Tianjin 300060, China.
Wei WangDepartment of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Huanhu West Road, Hexi District, Tianjin 300060, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeExplore the longitudinal CT-based radiomics to demonstrate the changing trend of radiotherapy response and to determine at which point after the onset of treatment radiomics exhibit the greatest change for stage III NSCLC patients. METHODS AND MATERIALS: Ten stage III NSCLC patients in line with inclusion criteria were enrolled retrospectively, each of whom received radiotherapy or concurrent chemo-radiotherapy and performed eight series of follow-up CT imaging. Longitudinal radiomics were extracted on region of interest from the eight registered images, then two steps were conducted to select significant features as indicators of tumor change: 1) stable features were selected by Kendall rank correlation; 2) texture feature types with a steadily changing trend were retained and intensity features with stable change trends were selected to represent the large number of them. Next, the trend and rate of tumor change were analyzed using the Delta method and Curve-fitting method. Finally, the statistics in the distribution of stable features in patients were calculated.

results675 stable features were selected from a total number of 1371 radiomics features, then 12 texture features types were retained and three intensity features were chosen to represent their own category. Among the final selected feature types, it was found that the two time points were weeks 1 and 3 with the higher rate of change. One patient had very few stable tumor features out of a total of 101 features, and the rate of change of features of another patient was conspicuously higher than the average level with number of 301 features.

conclusionThe longitudinal CT radiomics could demonstrate the change trend of tumor and at which point exhibit the greatest change during radiotherapy, and potentially be used for treatment decisions concerning adaptive radiotherapy.

Indexed as

CBCT, Cone-beam Computed TomographyComputed tomographyCT, Computed TomographyGLCM25/GLCM3, Gray Level Co-occurrence Matrix25/Gray Level Co-occurrence Matrix3GLRLM25, Gray Level Run Length Matrix25GTV, Gross Tumor VolumeHU, Hounsfield UnitsIBEX, Imaging Biomarker ExplorerLASSO, Least Absolute Shrinkage and Selection OperatorLongitudinal radiomics featuresNID25/NID3, Neighborhood Intensity Difference25/Neighborhood Intensity Difference3Non-small cell lung cancerNSCLC, Non-small cell lung carcinomaPCA, Principle Component AnalysisRadiation therapyROI, Region of InterestVMAT, Volumetric Modulated Arc Therapy

Identifiers

PMID34977279
PMCPMC8688890

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.