Evidence map›Paper›PMID 39430316›Full record

ArticleTranslational lung cancer research2024

Establishment of a survival predictive model for patients with two synchronous multiple primary lung cancers: a multicenter cohort analysis.

Ying Ji, Qing Zhao, Yi Liu, Bin Qiu, Guangyu Bai, Siyuan Ai, Wei Feng, Ligong Yuan, Xin Wang, Lulu Rong and 8 more

Abstract read
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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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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

18 authors.

Ying Ji *Capital Medical University, Beijing, China.
Qing Zhao *Capital Medical University, Beijing, China.
Yi Liu *Department of Thoracic Surgery, Beijing Institute of Respiratory Medicine and Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.
Bin QiuDepartment 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.
Guangyu BaiDepartment 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.
Siyuan AiDepartment of Thoracic Surgery, Beijing Liangxiang Hospital, Beijing, China.
Wei FengDepartments of Cardiothoracic Surgery, The Third Xiangya Hospital, Central South University, Changsha, China.
Ligong YuanDepartment of Thoracic Surgery, the First Affiliated Hospital of University of Science and Technology of China, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
Xin WangDepartment 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.
Lulu RongDepartment of Pathology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.
Hua FuDepartment of Pathology, The Third Xiangya Hospital, Central South University, Changsha, China.
Huihui XieDepartment of Radiology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.
Linlin 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.
Ye TaoDepartment of Radiology, The Third Xiangya Hospital, Central South University, Changsha, China.
Longyu JinDepartments of Cardiothoracic Surgery, The Third Xiangya Hospital, Central South University, Changsha, China.
Jing ZhouCenter for Applied Statistics, Renmin University of China, Beijing, China.
Bin HuCapital Medical University, Beijing, China.
Shugeng GaoDepartment 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.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The prognostic predictors of the synchronous multiple primary lung cancer (SMPLC) still remain unclear, and there is a lack of studies on the prognosis of SMPLC patients excluding those with multifocal ground-glass/lepidic (GG/L) nodules. The aim of this study is to develop an effective model for predicting survival of SMPLC patients. Methods: In this multicenter cohort study, a total of 831 SMPLC patients presenting for lung cancer resection from January 2004 to January 2018 at five institutions were included for developing and validating a nomogram model. Specifically, 499 patients from the Cancer Hospital, Chinese Academy of Medical Sciences, and Beijing Chao-Yang Hospital, Capital Medical University were served as the training cohort. A total of 332 patients from The Third Xiangya Hospital of Central South University, the First Affiliated Hospital of University of Science and Technology of China, and Beijing Liangxiang Hospital were served as the external validation cohort. The nomogram model was compared with the Tumor Node Metastasis (TNM) system for the overall survival. The C-index, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were used to evaluate the model performance. A user-friendly website for SMPLC survival probability calculation was also provided for a better understanding of prognosis of patients with resected SMPLC. Results: A total of seven independent risk factors were selected by conducting a multivariate analysis on the training set. Further, a nomogram model was developed with these factors. Both the internal and external validations exhibited good discrimination (C-index: internal, 0.827; external, 0.784). The NRI and IDI of this model were 0.33 and 0.21, respectively. The survival rates for 1-year, 3-year, and 5-year were consistent with the actual observed values. A set of cutoff values were determined by grouping the patients into three different groups. For each group, we should expect a significant distinction between survival curves. Conclusions: The novel nomogram model enables accurate survival risk stratification of patients with resected SMPLC and may assist in decision-making that is conducive to patients with SMPLC at high risk.

Indexed as

Lung cancerprognosissurvival predictive modelsynchronous multiple primary lung cancer (SMPLC)

Identifiers

PMID39430316
PMCPMC11484729

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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.