Evidence map›Paper›PMID 40884680›Full record

ArticleInsights into imaging2025

An MRI-based model for preoperative prediction of tertiary lymphoid structures in patients with gallbladder cancer.

Ying Xu, Zhuo Li, Weihua Zhi, Yi Yang, Jingzhong Ouyang, Yanzhao Zhou, Zeliang Ma, Sicong Wang, Lizhi Xie, Jianming Ying and 3 more

Abstract read
In one paragraph

Article in Insights into imaging, 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

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

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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

1 citing paper in PubMed.

  1. Review
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.

Ying Xu *Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Zhuo Li *Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Weihua Zhi *Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Yi YangState Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Jingzhong OuyangDepartment of Hepatobiliary Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Yanzhao ZhouDepartment of Medical Oncology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China.
Zeliang MaDepartment of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Sicong WangMagnetic Resonance Imaging Research, General Electric Healthcare, Beijing, China.
Lizhi XieMagnetic Resonance Imaging Research, General Electric Healthcare, Beijing, China.
Jianming YingDepartment of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. yingjm@cicams.ac.cn.
Jinxue ZhouDepartment of Hepatobiliary and Pancreatic Surgery, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China. zhoujx888@126.com.
Xinming ZhaoDepartment of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. zhaoxinming@cicams.ac.cn.
Feng YeDepartment of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. yefeng@cicams.ac.cn.ORCID http://orcid.org/0000-0002-4210-3345

Funding

CAMS Innovation Fund for Medical Sciences (CIFMS) 2021-I2M-C&T-B-057CAMS Innovation Fund for Medical Sciences (CIFMS) 2022-I2M-C&T-B-081
6 · The paper itself

Abstract

objectivesTo predict tertiary lymphoid structures (TLSs) in gallbladder cancer (GBC) using preoperative magnetic resonance imaging (MRI)-based radiomics.

methodsPatients with GBC from two centres served as training (n = 129) and external validation (n = 44) cohorts. Radiomics features were extracted from six imaging sequences for inclusion in a radiomics model (Rad-score). Univariate and multivariate logistic regression were used to identify independent clinico-radiological predictors of TLS status. The clinical and radiomics models were integrated into a combined model. Areas under receiver operating characteristic curves (AUC) were used to assess model performance. The combined model was divided into low- and high-risk according to the cut-off value determined by the maximum Youden index of the ROC.

resultsIntratumoural TLSs independently predicted RFS (p = 0.046). Eight features were included in the Rad-score. The clinical model included three independent predictors of TLS status (tumour height, liver invasion, and arterial-phase hypo-enhancement). In the training cohort, the combined model outperformed the separate clinical and radiomics models (AUC, 0.891 vs 0.870 and 0.775, respectively) and was externally valid. In both training and external cohorts, RFS in the low-risk group was substantially higher compared to the high-risk group. The low-risk group in the immunotherapy cohort had a significantly higher median overall survival than the high-risk group.

conclusionsThe MRI-based combined model developed in this study can preoperatively predict intratumoural TLS status. It accurately stratified the RFS of patients after surgery and the OS of patients with immunotherapy. CRITICAL RELEVANCE STATEMENT: This combined model is useful for predicting response and prognosis, not only for the recurrence-free survival of patients with GBC who have undergone surgery, but also for the overall survival of patients who have received immunotherapy KEY POINTS: Intratumoural TLSs independently predict recurrence-free survival of GBC. Our MRI-based combined model is a preoperative TLS marker. The combined model accurately stratifies postoperative/post-immunotherapy recurrence-free and overall survival of GBC.

Indexed as

Gallbladder cancerMagnetic resonance imagingRadiomicsTertiary lymphoid structure

Identifiers

PMID40884680
PMCPMC12398454

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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.