Evidence map›Paper›PMID 38779006›Full record

ArticleHeliyon2024

Spatial distance between tumor and lymphocyte can predict the survival of patients with resectable lung adenocarcinoma.

Xipeng Pan, Siyang Feng, Yumeng Wang, Jiale Chen, Huan Lin, Zimin Wang, Feihu Hou, Cheng Lu, Xin Chen, Zhenbing Liu and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

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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.

Xipeng PanSchool of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China.
Siyang FengSchool of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China.
Yumeng WangSchool of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China.
Jiale ChenSchool of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China.
Huan LinDepartment of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
Zimin WangSchool of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China.
Feihu HouSchool of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China.
Cheng LuSchool of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China.
Xin ChenSchool of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China.
Zhenbing LiuSchool of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China.
Zhenhui LiDepartment of Radiology, The Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Centre, Kunming, 650118, China.
Yanfen CuiDepartment of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
Zaiyi LiuSchool of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and objective: Spatial interaction between tumor-infiltrating lymphocytes (TILs) and tumor cells is valuable in predicting the effectiveness of immune response and prognosis amongst patients with lung adenocarcinoma (LUAD). Recent evidence suggests that the spatial distance between tumor cells and lymphocytes also influences the immune responses, but the distance analysis based on Hematoxylin and Eosin (H&E) -stained whole-slide images (WSIs) remains insufficient. To address this issue, we aim to explore the relationship between distance and prognosis prediction of patients with LUAD in this study. Methods: We recruited patients with resectable LUAD from three independent cohorts in this multi-center study. We proposed a simple but effective deep learning-driven workflow to automatically segment different cell types in the tumor region using the HoVer-Net model, and quantified the spatial distance (DIST) between tumor cells and lymphocytes based on H&E-stained WSIs. The association of DIST with disease-free survival (DFS) was explored in the discovery set (D1, n = 276) and the two validation sets (V1, n = 139; V2, n = 115). Results: In multivariable analysis, the low DIST group was associated with significantly better DFS in the discovery set (D1, HR, 0.61; 95 % CI, 0.40-0.94; p = 0.027) and the two validation sets (V1, HR, 0.54; 95 % CI, 0.32-0.91; p = 0.022; V2, HR, 0.44; 95 % CI, 0.24-0.81; p = 0.009). By integrating the DIST with clinicopathological factors, the integrated model (full model) had better discrimination for DFS in the discovery set (C-index, D1, 0.745 vs. 0.723) and the two validation sets (V1, 0.621 vs. 0.596; V2, 0.671 vs. 0.650). Furthermore, the computerized DIST was associated with immune phenotypes such as immune-desert and inflamed phenotypes. Conclusions: The integration of DIST with clinicopathological factors could improve the stratification performance of patients with resectable LUAD, was beneficial for the prognosis prediction of LUAD patients, and was also expected to assist physicians in individualized treatment.

Indexed as

Artificial intelligenceLung adenocarcinomaPrognosis analysisSpatial distance

Identifiers

PMID38779006
PMCPMC11109847

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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.