Evidence map›Paper›PMID 37349127›Full record

ArticleJournal for immunotherapy of cancer2023

Associations of subcutaneous fat area and Systemic Immune-inflammation Index with survival in patients with advanced gastric cancer receiving dual PD-1 and HER2 blockade.

Meng He, Zi-Fan Chen, Li Zhang, Xiangyu Gao, Xiaoyi Chong, Hao-Shen Li, Lin Shen, Jiafu Ji, Xiaotian Zhang, Bin Dong and 2 more

Abstract read
In one paragraph

Article in Journal for immunotherapy of cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
43citing 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

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

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  12. When adipose meets inflammation: a novel adiposity-inflammation signature predicts prognosis and chemotherapy benefit in locally advanced gastric cancer.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2026
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  20. Predicting response to patients with gastric cancer via a dynamic-aware model with longitudinal liquid biopsy data.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2025
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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.

Meng He *Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital, Beijing, China.
Zi-Fan Chen *Center for Data Science, Peking University, Beijing, China.
Li ZhangCenter for Data Science, Peking University, Beijing, China.
Xiangyu GaoDepartment of Gastrointestinal Surgery, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital, Beijing, China.
Xiaoyi ChongDepartment of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing, China.
Hao-Shen LiCenter for Data Science, Peking University, Beijing, China.
Lin ShenDepartment of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing, China.ORCID 0000-0003-1134-2922
Jiafu JiDepartment of Gastrointestinal Surgery, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital, Beijing, China.ORCID 0000-0001-6878-5543
Xiaotian ZhangDepartment of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital & Institute, Beijing, China tangl@bjcancer.org ziyu_li@hsc.pku.edu.cn dongbin@math.pku.edu.cn zhangxiaotianmed@163.com.
Bin DongBeijing International Center for Mathematical Research, Peking University; Center for Machine Learning Research, Peking University; National Biomedical Imaging Center, Peking University, Beijing, China tangl@bjcancer.org ziyu_li@hsc.pku.edu.cn dongbin@math.pku.edu.cn zhangxiaotianmed@163.com.
Zi-Yu LiDepartment of Gastrointestinal Surgery, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital, Beijing, China tangl@bjcancer.org ziyu_li@hsc.pku.edu.cn dongbin@math.pku.edu.cn zhangxiaotianmed@163.com.
Tang LeiDepartment of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital, Beijing, China tangl@bjcancer.org ziyu_li@hsc.pku.edu.cn dongbin@math.pku.edu.cn zhangxiaotianmed@163.com.ORCID 0000-0003-4157-2040

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSystemic Immune-inflammation Index (SII) and body composition parameters are easily assessed, and can predict overall survival (OS) in various cancers, allowing early intervention. This study aimed to assess the correlation between CT-derived body composition parameters and SII and OS in patients with advanced gastric cancer receiving dual programmed death-1 (PD-1) and human epidermal growth factor receptor 2 (HER2) blockade. MATERIALS AND

methodsThis retrospective study enrolled patients with advanced gastric cancer treated with dual PD-1 and HER2 blockade from March 2019 to June 2022. We developed a deep learning model based on nnU-Net to automatically segment skeletal muscle, subcutaneous fat and visceral fat at the third lumbar level, and calculated the corresponding Skeletal Muscle Index, skeletal muscle density, subcutaneous fat area (SFA) and visceral fat area. SII was computed using the formula that total peripheral platelet count×neutrophil/lymphocyte ratio. Univariate and multivariate Cox regression analysis were used to determine the associations between SII, body composition parameters and OS.

resultsThe automatic segmentation deep learning model was developed to efficiently segment body composition in 158 patients (0.23 s/image). Multivariate Cox analysis revealed that high SII (HR=2.49 (95% CI 1.54 to 4.01), p<0.001) and high SFA (HR=0.42 (95% CI 0.24 to 0.73), p=0.002) were independently associated with OS, whereas sarcopenia was not an independent prognostic factor for OS (HR=1.41 (95% CI 0.86 to 2.31), p=0.173). In further analysis, patients with high SII and low SFA had worse long-term prognosis compared with those with low SII and high SFA (HR=8.19 (95% CI 3.91 to 17.16), p<0.001).

conclusionPretreatment SFA and SII were significantly associated with OS in patients with advanced gastric cancer. A comprehensive analysis of SII and SFA may improve the prognostic stratification of patients with gastric cancer receiving dual PD-1 and HER2 blockade.

Indexed as

Programmed Cell Death 1 ReceptorStomach NeoplasmsHumansInflammationRetrospective StudiesSubcutaneous FatProgrammed Cell Death 1 Receptorgastrointestinal neoplasmsimmunotherapyinflammationtumor biomarkers

Identifiers

PMID37349127
PMCPMC10314655

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.