Evidence map›Paper›PMID 37010663›Full record

Trial reportAnnals of surgical oncology2023

A Nomogram Based on Nutrition-Related Indicators and Computed Tomography Imaging Features for Predicting Preoperative Lymph Node Metastasis in Curatively Resected Esophagogastric Junction Adenocarcinoma.

Can-Tong Liu, Yu-Hui Peng, Chao-Qun Hong, Xin-Yi Huang, Ling-Yu Chu, Yi-Wei Lin, Hai-Peng Guo, Fang-Cai Wu, Yi-Wei Xu

Abstract readRandomized Controlled Trial
PubMed Publisher
In one paragraph

Trial report in Annals of surgical oncology, 2023. 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
  2. Article
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

9 authors.

Can-Tong Liu *Department of Clinical Laboratory Medicine, Cancer Hospital of Shantou University Medical College, Shantou, Guangdong Province, China.
Yu-Hui Peng *Department of Clinical Laboratory Medicine, Cancer Hospital of Shantou University Medical College, Shantou, Guangdong Province, China.
Chao-Qun Hong *Department of Oncological Laboratory Research, Cancer Hospital of Shantou University Medical College, Shantou, Guangdong Province, China.
Xin-Yi HuangDepartment of Gastrointestinal Endoscopy, The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong Province, China.
Ling-Yu ChuDepartment of Clinical Laboratory Medicine, Cancer Hospital of Shantou University Medical College, Shantou, Guangdong Province, China.
Yi-Wei LinDepartment of Clinical Laboratory Medicine, Cancer Hospital of Shantou University Medical College, Shantou, Guangdong Province, China.
Hai-Peng GuoEsophageal Cancer Prevention and Control Research Center, The Cancer Hospital of Shantou University Medical College, Shantou, Guangdong Province, China. ghaipeng@sina.cn.
Fang-Cai WuEsophageal Cancer Prevention and Control Research Center, The Cancer Hospital of Shantou University Medical College, Shantou, Guangdong Province, China. 280550109@qq.com.
Yi-Wei XuDepartment of Clinical Laboratory Medicine, Cancer Hospital of Shantou University Medical College, Shantou, Guangdong Province, China. yiwei512@126.com.

Funding

2020 Li Ka Shing Foundation Cross-Disciplinary Research Project Fund 2020LKSFG01BGuangdong Basic and Applied Basic Research Foundation Enterprise Joint Foundation 22202104030000834Guangdong Basic and Applied Basic Research Foundation Enterprise Joint Foundation 22202104030000892Guangdong Basic and Applied Basic Research Foundation Enterprise Joint Foundation 22202104030000903Innovative Team Grant of Guangdong Department of Education 2021KCXTD005National Natural Science Foundation of China 81972801Science and Technology Planning Project of Shantou City 221115196492930Science and Technology Special Fund of Guangdong Province of China STKJ202209069Youth Research Fund Project of Cancer Hospital of Shantou University Medical College 2023A005
6 · The paper itself

Abstract

backgroundsPreoperative noninvasive tools to predict pretreatment lymph node metastasis (PLNM) status accurately for esophagogastric junction adenocarcinoma (EJA) are few. Thus, the authors aimed to construct a nomogram for predicting PLNM in curatively resected EJA.

methodsThis study enrolled 638 EJA patients who received curative surgery resection and divided them randomly (7:3) into training and validation groups. For nomogram construction, 26 candidate parameters involving 21 preoperative clinical laboratory blood nutrition-related indicators, computed tomography (CT)-reported tumor size, CT-reported PLNM, gender, age, and body mass index were screened.

resultsIn the training group, Lasso regression included nine nutrition-related blood indicators in the PLNM-prediction nomogram. The PLNM prediction nomogram yielded an area under the receiver operating characteristic (ROC) curve of 0.741 (95 % confidence interval [CI], 0.697-0.781), which was better than that of the CT-reported PLNM (0.635; 95% CI 0.588-0.680; p < 0.0001). Application of the nomogram in the validation cohort still gave good discrimination (0.725 [95% CI 0.658-0.785] vs 0.634 [95% CI 0.563-0.700]; p = 0.0042). Good calibration and a net benefit were observed in both groups.

conclusionsThis study presented a nomogram incorporating preoperative nutrition-related blood indicators and CT imaging features that might be used as a convenient tool to facilitate the preoperative individualized prediction of PLNM for patients with curatively resected EJA.

Indexed as

AdenocarcinomaNomogramsEsophageal NeoplasmsEsophagogastric JunctionHumansLymphatic MetastasisTomography, X-Ray Computed

Identifiers

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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 Socratic graph.