Evidence map›Paper›PMID 33563277›Full record

ArticleWorld journal of surgical oncology2021

Clinicopathological factors affecting the effect of neoadjuvant chemotherapy in patients with gastric cancer.

Lin Jiang, Zhiqiang Ma, Xin Ye, Weiming Kang, Jianchun Yu

Abstract read
In one paragraph

Article in World journal of surgical oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 3 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

23 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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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

5 authors.

Lin JiangDepartment of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuaifuyuan, Wangfujin, Dongcheng District, Beijing, 100730, China.
Zhiqiang MaDepartment of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuaifuyuan, Wangfujin, Dongcheng District, Beijing, 100730, China.
Xin YeDepartment of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuaifuyuan, Wangfujin, Dongcheng District, Beijing, 100730, China.
Weiming KangDepartment of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuaifuyuan, Wangfujin, Dongcheng District, Beijing, 100730, China.
Jianchun YuDepartment of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 1 Shuaifuyuan, Wangfujin, Dongcheng District, Beijing, 100730, China. yu-jch@163.com.ORCID http://orcid.org/0000-0002-8250-7475

Funding

Beijing Municipal Science and Technology Commission Program D171100006517002Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences 2019PT320009
6 · The paper itself

Abstract

backgroundNeoadjuvant chemotherapy is an important part of the comprehensive treatment of advanced gastric cancer (GC). The effect of neoadjuvant chemotherapy plays a key role in the prognosis of GC patients. Pathological response can represent the effect of neoadjuvant chemotherapy. However, evidence focused on pathological response and associated clinicopathological factors in GC patients is quite little. In this retrospective study, the clinicopathological factors affecting the effect of neoadjuvant chemotherapy in GC patients were investigated, and suggestions were proposed to improve the effect of neoadjuvant chemotherapy on GC.

methodsRetrospective analysis was performed on GC patients who received radical surgery after neoadjuvant chemotherapy from February 2016 to December 2019 at Peking Union Medical College Hospital. Relevant clinicopathological data was collected to analyze the factors influencing the effect of neoadjuvant chemotherapy. Chi-square test was used for univariate analysis. Logistic regression was used for multivariate analysis. Receiver operating characteristic curve (ROC) was used to determine the cutoff value of variables which significantly influenced the effect of neoadjuvant chemotherapy.

resultsA total of 203 GC patients were included in the study. Analyses showed that patients < 60 years old (OR = 1.840 [1.016-3.332], P = 0.044), histological type of poor differentiation or signet-ring cell carcinoma (OR = 2.606 [1.321-5.140], P = 0.006), and weight loss during neoadjuvant chemotherapy (OR = 2.110 [1.161-3.834], P = 0.014) were independent risk factors for neoadjuvant chemotherapy effect. In ROC analysis of weight change and neoadjuvant chemotherapy effect, area under the curve (AUC) was 0.593 (P = 0.024) and cutoff value of weight change was - 2.95%. Chi-square test showed that patients without weight loss during neoadjuvant chemotherapy had a higher rate of oral nutritional supplement (ONS) than patients with weight loss (P = 0.039).

conclusionsPatients <60 years old, histological type of poor differentiation or signet-ring cell carcinoma, and weight loss during neoadjuvant chemotherapy were independent risk factors for neoadjuvant chemotherapy effect in GC patients. Patients with weight loss > 2.95% during neoadjuvant may have a worse chemotherapy effect. Timely nutritional support such as ONS to maintain patients' body weight is crucial for improving the effect of neoadjuvant chemotherapy.

Indexed as

Carcinoma, Signet Ring CellStomach NeoplasmsHumansMiddle AgedNeoadjuvant TherapyPrognosisRetrospective StudiesChemotherapy effectClinicopathological factorsGastric cancerNeoadjuvant chemotherapyPathological response

Identifiers

PMID33563277
PMCPMC7874458

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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.