Evidence map›Paper›PMID 36387160›Full record

ArticleFrontiers in oncology2022

Radiomics models based on CT at different phases predicting lymph node metastasis of esophageal squamous cell carcinoma (GASTO-1089).

Guobo Peng, Yizhou Zhan, Yanxuan Wu, Chengbing Zeng, Siyan Wang, Longjia Guo, Weitong Liu, Limei Luo, Ruoheng Wang, Kang Huang and 3 more

Abstract read
In one paragraph

Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 2 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

8 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Review
  6. Radiomics signature for dynamic changes of tumor-infiltrating CD8+ T cells and macrophages in cervical cancer during chemoradiotherapy.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024
    Article
  7. Article
  8. 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

13 authors.

Guobo PengDepartment of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China.
Yizhou ZhanDepartment of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China.
Yanxuan WuDepartment of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China.
Chengbing ZengDepartment of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China.
Siyan WangDepartment of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China.
Longjia GuoDepartment of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China.
Weitong LiuDepartment of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China.
Limei LuoDepartment of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China.
Ruoheng WangDepartment of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China.
Kang HuangDepartment of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China.
Baotian HuangDepartment of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China.
Jianzhou ChenDepartment of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China.
Chuangzhen ChenDepartment of Radiation Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To investigate the value of radiomics models based on CT at different phases (non-contrast-enhanced and contrast-enhanced images) in predicting lymph node (LN) metastasis in esophageal squamous cell carcinoma (ESCC). Methods and materials: Two hundred and seventy-four eligible patients with ESCC were divided into a training set (n =193) and a validation set (n =81). The least absolute shrinkage and selection operator algorithm (LASSO) was used to select radiomics features. The predictive models were constructed with radiomics features and clinical factors through multivariate logistic regression analysis. The predictive performance and clinical application value of the models were evaluated by area under receiver operating characteristic curve (AUC) and decision curve analysis (DCA). The Delong Test was used to evaluate the differences in AUC among models. Results: Sixteen and eighteen features were respectively selected from non-contrast-enhanced CT (NECT) and contrast-enhanced CT (CECT) images. The model established using only clinical factors (Model 1) has an AUC value of 0.655 (95%CI 0.552-0.759) with a sensitivity of 0.585, a specificity of 0.725 and an accuracy of 0.654. The models contained clinical factors with radiomics features of NECT or/and CECT (Model 2,3,4) have significantly improved prediction performance. The values of AUC of Model 2,3,4 were 0.766, 0.811 and 0.809, respectively. It also achieved a great AUC of 0.800 in the model built with only radiomics features derived from NECT and CECT (Model 5). DCA suggested the potential clinical benefit of model prediction of LN metastasis of ESCC. A comparison of the receiver operating characteristic (ROC) curves using the Delong test indicated that Models 2, 3, 4, and 5 were superior to Model 1(P< 0.05), and no difference was found among Model 2, 3, 4 and Model 5(P > 0.05). Conclusion: Radiomics models based on CT at different phases could accurately predict the lymph node metastasis in patients with ESCC, and their predictive efficiency was better than the clinical model based on tumor size criteria. NECT-based radiomics model could be a reasonable option for ESCC patients due to its lower price and availability for renal failure or allergic patients.

Indexed as

computed tomographyesophageal canceresophageal squamous cell carcinomaslymph node metastasisprediction modelradiomics

Identifiers

PMID36387160
PMCPMC9643555

What OpenQuestion holds

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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.