Evidence map›Paper›PMID 38887237›Full record

ArticleFrontiers in oncology2024

Predictive model based on multiple immunofluorescence quantitative analysis for pathological complete response to neoadjuvant immunochemotherapy in lung squamous cell carcinoma.

Meng Xiao, Lili Tu, Ting Zhou, Ye He, Xiaohui Li, Qiunan Zuo

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Article in Frontiers in oncology, 2024. 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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1citing papers in PubMed
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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

1 citing paper in PubMed.

  1. Review
4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Meng Xiao *The Geriatric Respiratory Department, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Lili Tu *The Geriatric Respiratory Department, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Ting ZhouThe Geriatric Respiratory Department, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Ye HeThe Geriatric Respiratory Department, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Xiaohui LiThe Geriatric Respiratory Department, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Qiunan ZuoThe Geriatric Respiratory Department, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study aims to establish a prediction model for neoadjuvant immunochemotherapy (NICT) in lung squamous cell carcinoma to guide clinical treatment. Methods: This retrospective study included 50 patients diagnosed with lung squamous cell carcinoma who received NICT. The patients were divided into the pathological complete response (PCR) group and the non-PCR group. HE staining and multiple immunofluorescence (mIF) techniques were utilized to analyze the differences in the immune microenvironment between these groups. LASSO regression and optimal subset regression were employed to identify the most significant variables and construct a prediction model. Results: The PCR group showed higher densities of lymphocyte nuclei and karyorrhexis based on HE staining. Furthermore, based on mIF analysis, the PCR group showed higher cell densities of CD8+, PD-L1+, and CD8+PD-L1+ in the tumor region, while showing lower cell densities of CD3+Foxp3+, Foxp3+, and CD163+. Logistic univariate analysis revealed CD8+PD-L1+, PD-L1+, CD8+, CD4+LAG-3+, lymphocyte nuclei, and karyorrhexis as significant factors influencing PCR. By using diverse screening methods, the three most relevant variables (CD8+, PD-L1+, and CD8+PD-L1+ in the tumor region) were selected to establish the prediction model. The model exhibited excellent performance in both the training set (AUC=0.965) and the validation set (AUC=0.786). In the validation set, In comparison to the conventional TPS scoring criteria, the model attained superior accuracy (0.85), specificity(0.67), and sensitivity (0.92). Conclusion: NICT treatment might induce anti-tumor effects by enriching immune cells and reactivating exhausted T cells. CD8+, PD-L1+, and CD8+PD-L1+ cell abundances within the tumor region have been closely associated with therapeutic efficacy. Incorporating these three variables into a predictive model allows accurate forecasting of treatment outcomes and provides a reliable basis for selecting NICT treatment strategies.

Indexed as

lung squamous cell carcinomamultiple immunofluorescence quantitative analysisneoadjuvant immunochemotherapypathological complete responsepredictive model

Identifiers

PMID38887237
PMCPMC11180808

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