Evidence map›Paper›PMID 40836336›Full record

ArticleBMC cancer2025

Comparative analysis and optimization of tumor regression grade assessment systems in neoadjuvant therapy for esophageal squamous cell carcinoma.

Qiao Yang, Xiaoqing Zhou, Jiayu Li, Jieyu Wang, Jingcheng Luo, Dan Luo, Lin Shi, Chengmin Zhou, Yi Wang, Xuefeng Leng and 3 more

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

13 authors.

Qiao Yang *Department of Pathology, Sichuan Cancer Center, School of Medicine, Sichuan Cancer Hospital & Institute, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
Xiaoqing Zhou *Department of Pathology, Sichuan Cancer Center, School of Medicine, Sichuan Cancer Hospital & Institute, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
Jiayu Li *Department of Pathology, Sichuan Cancer Center, School of Medicine, Sichuan Cancer Hospital & Institute, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
Jieyu WangDepartment of Pathology, Sichuan Cancer Center, School of Medicine, Sichuan Cancer Hospital & Institute, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
Jingcheng LuoDepartment of Pathology, Sichuan Cancer Center, School of Medicine, Sichuan Cancer Hospital & Institute, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
Dan LuoDepartment of Pathology, Sichuan Cancer Center, School of Medicine, Sichuan Cancer Hospital & Institute, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
Lin ShiDepartment of Pathology, Sichuan Cancer Center, School of Medicine, Sichuan Cancer Hospital & Institute, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
Chengmin ZhouDepartment of Pathology, Sichuan Cancer Center, School of Medicine, Sichuan Cancer Hospital & Institute, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
Yi WangDepartment of Pathology, Sichuan Cancer Center, School of Medicine, Sichuan Cancer Hospital & Institute, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
Xuefeng LengDepartment of Pathology, Sichuan Cancer Center, School of Medicine, Sichuan Cancer Hospital & Institute, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
Qifeng WangDepartment of Pathology, Sichuan Cancer Center, School of Medicine, Sichuan Cancer Hospital & Institute, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
Yang LiuDepartment of Pathology, Sichuan Cancer Center, School of Medicine, Sichuan Cancer Hospital & Institute, University of Electronic Science and Technology of China, Chengdu, Sichuan, China. liuyang_sccancer@sina.com.
Yehan ZhouDepartment of Pathology, Sichuan Cancer Center, School of Medicine, Sichuan Cancer Hospital & Institute, University of Electronic Science and Technology of China, Chengdu, Sichuan, China. zhouyehan@scszlyy.org.cn.

Funding

This work was supported by the Sichuan Provincial Foundation of Science and Technology Grant Nos. 2023NSFSC0626
6 · The paper itself

Abstract

objectiveThis study aims to compare and analyze the effectiveness of different tumor regression grade (TRG) assessment systems in evaluating neoadjuvant therapy for esophageal cancer, with the goal of identifying an optimal assessment method to guide clinical practice.

methodsA total of 467 patients with esophageal squamous cell carcinoma who underwent neoadjuvant therapy followed by surgical resection were included in this study. We comprehensively evaluated the effectiveness of five TRG assessment systems-Mandard, CAP, Becker, JSED, and Ryan-in predicting the prognosis of the primary tumor (PT) and lymph nodes (LN). The inter-observer consistency among these systems was also assessed to identify the most effective TRG evaluation method.

resultsThe performance of the TRG assessment systems in predicting LN prognosis was generally superior to that for PT. Specifically, while the Ryan criteria demonstrated the highest inter-observer consistency coefficient (Mean Kappa = 0.848), its predictive efficacy was the lowest (Mean AUC = 0.502). In contrast, the Becker criteria exhibited the highest predictive efficacy (Mean AUC = 0.609) alongside a good consistency coefficient (Mean Kappa = 0.788). Notably, the modified Modified TRG system not only achieved a higher AUC value (0.624) but also showed excellent inter-observer consistency (Kappa = 0.904).

conclusionThe modified Modified TRG system, with a focus on LN evaluation, demonstrates superior prognostic predictive ability and risk stratification effectiveness. These findings may assist clinicians in more accurately assessing patient prognosis and adjusting treatment strategies accordingly, ultimately optimizing patient treatment pathways.

Indexed as

Esophageal NeoplasmsEsophageal Squamous Cell CarcinomaNeoadjuvant TherapyAdultAgedEsophagectomyFemaleHumansLymphatic MetastasisLymph NodesMaleMiddle AgedNeoplasm GradingPrognosisRetrospective StudiesEsophageal cancerLymph nodesPrognosisTumor regression grade

Identifiers

PMID40836336
PMCPMC12369091

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LicenceCC BY
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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.