Evidence map›Paper›PMID 35282099›Full record

ArticleAnnals of translational medicine2022

A clinical nomogram for predicting tumor regression grade in esophageal squamous-cell carcinoma treated with immune neoadjuvant immunotherapy.

Yongkui Yu, Wei Wang, Zimin Qin, Haomiao Li, Qi Liu, Haibo Ma, Haibo Sun, Thomas L Bauer, Jose M Pimiento, Emmanuel Gabriel and 3 more

Open access · diamondAbstract read
In one paragraph

Article in Annals of translational medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
1.3field-weighted citation impact, top 22% 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

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 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

13 authors at 7 institutions in 2 countries.

Yongkui YuDepartment of Thoracic Surgery, The Affiliated Cancer Hospital of Zhengzhou University/Henan Cancer Hospital, Zhengzhou, China.
Wei WangDepartment of Thoracic Surgery, The Affiliated Cancer Hospital of Zhengzhou University/Henan Cancer Hospital, Zhengzhou, China.
Zimin QinDepartment of Thoracic Surgery, The Affiliated Cancer Hospital of Zhengzhou University/Henan Cancer Hospital, Zhengzhou, China.
Haomiao LiDepartment of Thoracic Surgery, The Affiliated Cancer Hospital of Zhengzhou University/Henan Cancer Hospital, Zhengzhou, China.
Qi LiuDepartment of Thoracic Surgery, The Affiliated Cancer Hospital of Zhengzhou University/Henan Cancer Hospital, Zhengzhou, China.
Haibo MaDepartment of Thoracic Surgery, The Affiliated Cancer Hospital of Zhengzhou University/Henan Cancer Hospital, Zhengzhou, China.
Haibo SunDepartment of Thoracic Surgery, The Affiliated Cancer Hospital of Zhengzhou University/Henan Cancer Hospital, Zhengzhou, China.
Thomas L BauerDepartment of Surgery, Jersey Shore University Medical Center, Department of General Surgery, Hackensack Meridian School of Medicine, Nutley, NJ, USA.
Jose M PimientoDepartment of Gastrointestinal Oncology, Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
Emmanuel GabrielDepartment of Surgery, Mayo Clinic, Jacksonville, FL, USA.
Thomas BirdasDepartment of Surgery, Thoracic Division, Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis, IN, USA.
Yin LiDepartment of Thoracic Surgery, Cancer Hospital, Chinese Academy of Medical Sciences, Beijing, China.
Wenqun XingDepartment of Thoracic Surgery, The Affiliated Cancer Hospital of Zhengzhou University/Henan Cancer Hospital, Zhengzhou, China.
Henan Cancer Hospital · CNZhengzhou University · CNChinese Academy of Medical Sciences & Peking Union Medical College · CNIndiana University – Purdue University Indianapolis · USJersey Shore University Medical Center · USMoffitt Cancer Center · USWinnMed · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: There are various treatment options for esophageal squamous cell cancer. including surgery, peri-operative chemotherapy, and radiation. More recently, neoadjuvant immunotherapy has also been shown improve outcomes. In this study, we addressed the question, "Can we predict which patients with esophageal squamous cell cancer will benefit from neoadjuvant immunotherapy?". Methods: All patients with thoracic esophageal squamous-cell carcinoma (T2N+M0-T3-4N0/+M0) (according to the eighth edition of the National Comprehensive Cancer Network guidelines) who underwent immune neoadjuvant immunochemotherapy with programmed cell death protein 1 (PD-1) combined with paclitaxel plus cisplatin or nedaplatin in the Affiliated Cancer Hospital of Zhengzhou University, China, between November 2019 and August 2021 were included in this study. All patients underwent surgical resection. We developed a response [tumor regression grade (TRG)] prediction model using the least absolute shrinkage and selection operator (LASSO) regression incorporating factors associated with response. The accuracy of the prediction model was then validated. Results: We included 79 patients who underwent neoadjuvant immunotherapy combined with chemotherapy, aged 48-78 years (62.05±6.67), including 21 males and 58 females. There were five cases of immune-related pneumonia, of which three cases were diagnosed as immune-related pneumonia during the perioperative period, and one case of immune-related thyroid dysfunction changes. After LASSO regression, the factors that were independently associated with TRG were clinical T stage before neoadjuvant therapy, clinical N stage before neoadjuvant therapy, albumin level difference from before to after neoadjuvant therapy, white blood cell (WBC) count before neoadjuvant therapy, and T stage before surgery. We constructed a prediction model, plotted the nomogram, and verified its accuracy. Its Brier score was 0.13, its calibration slope was 0.98, and its C-index was 0.90 (95% CI: 0.82-0.97). Conclusions: Our prediction model can predict the likelihood of TRG in patients with esophageal squamous cell cancer after immunotherapy combined with neoadjuvant chemotherapy. Using this prediction model, we plan to conduct a subsequent neoadjuvant radiotherapy in patients with of TRG 2-3 patients with neoadjuvant radiotherapy.

Indexed as

Esophageal squamous cell cancerimmunotherapypathological remission gradingprediction model

Identifiers

PMID35282099
PMCPMC8848421
OpenAlexW4210629185

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

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.