Evidence map›Paper›PMID 39830763›Full record

ArticleTranslational lung cancer research2024

Enhancing the prediction of

Jing-Wen Ma, Cai-Xing Yuan, Shan Muhammad, Yan-Mei Wang, Lin-Lin Qi, Jiu-Ming Jiang, Xu Jiang, Lei Miao, Meng-Wen Liu, Xin Liang and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Translational lung cancer research, 2024. 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

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

1 citing paper in PubMed.

  1. 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

13 authors.

Jing-Wen MaDepartment of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Cai-Xing YuanDepartment of Radiology, Affiliated Hospital of Putian College, Putian, China.
Shan MuhammadDepartment of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Yan-Mei WangGE Healthcare China, Shanghai, China.
Lin-Lin QiDepartment of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Jiu-Ming JiangDepartment of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Xu JiangDepartment of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Lei MiaoDepartment of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Meng-Wen LiuDepartment of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Xin LiangMedical Statistics Office, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Tian QiuDepartments of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Li Zhang *Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Meng Li *Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Lung adenocarcinoma (LUAD) is a sub-type of non-small cell lung cancer (NSCLC) that is often associated with genetic alterations, including the Kirsten rat sarcoma viral oncogene homolog ( Methods: A total of 172 patients with LUAD were retrospectively enrolled and divided into a developing cohort (n=120) and a validation cohort (n=52). Clinical, DESCT and radiomics features were extracted and analyzed. Four predictive models were constructed: clinical, DESCT, radiomics, and combined clinical-DESCT-radiomics (C-S-R) model. The performance of these models was evaluated by the receiver operating characteristic curves. A nomogram incorporating clinical, DESCT, radiomics features with R-score was developed in the validation cohort. Results: In this study, 8.7% (15/172) of the patients showed Conclusions: This study suggests that integrating clinical, DESCT, and radiomics features can enhance the prediction of

Indexed as

duel-energy spectral computed tomography (DESCT)Kirsten rat sarcoma viral oncogene homolog mutation (KRAS mutation)lung adenocarcinoma (LUAD)Radiomics

Identifiers

PMID39830763
PMCPMC11736580

What OpenQuestion holds

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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.