Evidence map›Paper›PMID 42676607›Full record

ArticleMedical review (2021)2026

The Huchang-Barrier framework: an integrative perspective on the spatial features of the tumor invasive front.

Dan Xie, Zexing Li, Jinghua Li, Kaiwen Hu

Abstract read
In one paragraph

Article in Medical review (2021), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Dan XieDongfang Hospital, Beijing University of Chinese Medicine, Beijing, China.ORCID https://orcid.org/0009-0008-6717-7245
Zexing LiDongfang Hospital, Beijing University of Chinese Medicine, Beijing, China.
Jinghua LiDongfang Hospital, Beijing University of Chinese Medicine, Beijing, China.
Kaiwen HuDongfang Hospital, Beijing University of Chinese Medicine, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The tumor invasive front is a critical interface for tumor-host interactions that impacts patient prognosis. Although the tumor-node-metastasis (TNM) staging system and single-parameter assessments such as tumor budding, T-cell density, tertiary lymphoid structures (TLS), and stromal features provide important prognostic information, they capture only partial aspects of this spatially organized tumor-host interface. Inspired by the traditional Chinese medicine concept of "Huchang", used here as a metaphorical descriptor of host-protective organization at the tumor boundary, and informed by recent advances in spatial immunology, we propose the Huchang-Barrier (HB) framework. HB conceptualizes the invasive front as a dynamic tumor-host interface whose biological state is determined by the balance between tumor invasive burden and host physical/tissue and spatial immune barriers. Unlike fixed single-marker scores or tumor-type-specific microenvironmental indices, HB integrates three conserved functional elements while allowing cancer-adapted readouts, thereby providing a modular framework for evaluating the invasion-barrier balance at the invasive front. To improve operability, we outline a two-tier assessment strategy and provide a minimal executable HB-Standard example in resected non-small cell lung cancer (NSCLC). The HB framework is proposed as a hypothesis-generating spatial pathology model that requires retrospective calibration, independent validation, and prospective testing before clinical implementation.

Indexed as

Huchang-Barriernon-small cell lung cancerspatial pathologytertiary lymphoid structurestumor invasive fronttumor microenvironment

Identifiers

PMID42676607
PMCPMC13526689

What OpenQuestion holds

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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.