Evidence map›Paper›PMID 33718021›Full record

ArticleTranslational lung cancer research2021

Novel risk scoring system for immune checkpoint inhibitors treatment in non-small cell lung cancer.

Chuling Li, Meiqi Shi, Xinqing Lin, Yongchang Zhang, Shaorong Yu, Chengzhi Zhou, Nong Yang, Jianya Zhang, Fang Zhang, Tangfeng Lv and 2 more

Open access · diamondAbstract read
In one paragraph

Article in Translational lung cancer research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.7field-weighted citation impact, top 27% of its field
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

6 citing papers in PubMed, 8 citations in OpenAlex.

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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

12 authors at 8 institutions in 1 country.

Chuling LiDepartment of Respiratory Medicine, Jinling Hospital, Nanjing Medical University, Nanjing, China.
Meiqi ShiDepartment of Medical Oncology, The Affiliated Cancer Hospital of Nanjing Medical University, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing, China.
Xinqing LinState Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Yongchang ZhangDepartment of Medical Oncology, Lung Cancer and Gastrointestinal Unit, Hunan Cancer Hospital/The affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.
Shaorong YuDepartment of Medical Oncology, The Affiliated Cancer Hospital of Nanjing Medical University, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing, China.
Chengzhi ZhouState Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Nong YangDepartment of Medical Oncology, Lung Cancer and Gastrointestinal Unit, Hunan Cancer Hospital/The affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.
Jianya ZhangDepartment of Respiratory Medicine, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China.
Fang ZhangDepartment of Respiratory Medicine, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China.
Tangfeng LvDepartment of Respiratory Medicine, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China.
Hongbing LiuDepartment of Respiratory Medicine, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China.
Yong SongDepartment of Respiratory Medicine, Jinling Hospital, Nanjing Medical University, Nanjing, China.
Nanjing Medical University · CNNanjing University · CNCentral South University · CNFirst Affiliated Hospital of Guangzhou Medical University · CNGuangzhou Medical University · CNHunan Cancer Hospital · CNJiangsu Cancer Hospital · CNNanjing General Hospital of Nanjing Military Command · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundImmune checkpoint inhibitor (ICI)-based immunotherapy has improved the clinical outcome of non-small cell lung cancer (NSCLC). However, current indicators, such as programmed cell death-ligand 1 (PD-L1) expression in tumors or tumor mutational burden (TMB), are not considered ideal biomarkers for prognosis. Thus, there is an urgent requirement for a comprehensive risk scoring system.

methodsIn this study, we enrolled 464 NSCLC patients who received ICIs between March 2017 and January 2020 at four clinical centers. Univariate and multivariate (the logistic and the Cox regression) analyses were conducted to screen clinically relevant variables. Significant parameters (P<0.05) including absolute lymphocyte count (ALC, L), Eastern Cooperative Oncology Group Performance Status (ECOG PS, E) and lung/pleural metastasis (M) were selected for LEM score. Weighted values based on odds ratio and hazard ratio of multiple analyses were assigned to each parameter. LEM score was the sum of weighted values of each variable (Good, 0-1; Intermediate, 2-3; Poor, 4-6). Kaplan-Meier curves were used to evaluate the association between LEM score and progression-free survival (PFS).

resultsIn total, 258 patients were pooled and stratified into three risk categories based on the LEM score. Objective response rate (ORR) was significantly higher in the good-risk group compared with the poor-risk group [55.9%

conclusionsIn conclusion, the LEM score provided a potential prognostic biomarker for NSCLC patients treated with ICIs.

Indexed as

immune checkpoint inhibitors (ICIs)LEMNon-small cell lung cancer (NSCLC)prognostic biomarkerrisk scoring system

Identifiers

PMID33718021
PMCPMC7947409
OpenAlexW3134769619

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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.