Evidence map›Paper›PMID 41367586›Full record

ArticleTranslational lung cancer research2025

Development and validation of a multivariable prognostic model incorporating black-blood magnetic resonance imaging-based meningeal lymphatic remodeling to predict therapy response in non-small cell lung cancer brain metastases.

Wei Shao, Zongbo Li, Feng Qiu, Hengsen Zhang, Shudong Hu, Yifan Liu, Duoduo Li, Yuxi Ge, Hua Lu

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

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

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2 · The registry

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Wei Shao *Department of Neurosurgery, Affiliated Hospital of Jiangnan University, Wuxi, China.
Zongbo Li *Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi, China.
Feng QiuWuxi School of Medicine, Jiangnan University, Wuxi, China.
Hengsen ZhangDepartment of Neurosurgery, Affiliated Hospital of Jiangnan University, Wuxi, China.
Shudong HuDepartment of Radiology, Affiliated Hospital of Jiangnan University, Wuxi, China.
Yifan LiuDepartment of Neurosurgery, Affiliated Hospital of Jiangnan University, Wuxi, China.
Duoduo LiWuxi School of Medicine, Jiangnan University, Wuxi, China.
Yuxi GeDepartment of Radiology, Affiliated Hospital of Jiangnan University, Wuxi, China.
Hua LuDepartment of Neurosurgery, Affiliated Hospital of Jiangnan University, Wuxi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Brain metastases (BMs) from non-small cell lung cancer (NSCLC) remain a major clinical challenge, and existing prognostic tools such as the Graded Prognostic Assessment (GPA) do not incorporate imaging biomarkers or adequately reflect the impact of immunotherapy. Meningeal lymphatic vessels (mLVs), which regulate cerebrospinal fluid drainage and immune surveillance, have been implicated in tumor-immune interactions. We aimed to develop and internally validate a multivariable prognostic model integrating mLV remodeling measured by black-blood magnetic resonance imaging (BB-MRI) with clinical predictors to improve early prediction of treatment response. Methods: We retrospectively analyzed 130 patients with pathologically confirmed NSCLC (100 with BM, 30 without BM). Among the BM cohort, 56 patients achieved favorable treatment response [stable disease (SD) or partial response (PR)] and 44 experienced progressive disease (PD). Candidate predictors were pre-specified based on clinical relevance, and the final model incorporated total mLV diameter, immunotherapy exposure, sex, and extracranial lesion count. Internal validation was performed with 1,000 bootstrap resamples. Model performance was assessed by discrimination, calibration, and decision curve analysis (DCA). Results: The final multivariable model demonstrated good discrimination [area under the curve (AUC) =0.82; 95% confidence interval (CI): 0.75-0.90], excellent calibration, and consistent net clinical benefit across a range of threshold probabilities. The calibration and decision curves showed promising internal performance, but external validation is required before clinical application. Conclusions: BB-MRI-derived mLV remodeling may be an early and noninvasive indicator of treatment efficacy in BM. The proposed nomogram enables the individualized prediction of systemic therapy response, supporting precision immunotherapy for patients with intracranial metastases.

Indexed as

black-blood magnetic resonance imaging (BB-MRI)brain metastases (BMs)immunotherapyMeningeal lymphatic vessels (mLVs)

Identifiers

PMID41367586
PMCPMC12683432

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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.