ArticlePeerJ2022
Identification of prognostic factors and nomogram model for patients with advanced lung cancer receiving immune checkpoint inhibitors.
Article in PeerJ, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.
- Monocyte-related markers as predictors of immune checkpoint inhibitor efficacy and immune-related adverse events: a systematic review and meta-analysis.Cancer metastasis reviews · 2025Pooled it
- Clinical and nutritional-inflammatory biomarkers-based nomogram predicts survival in recurrent or metastatic cervical cancer treated with immune checkpoint inhibitors.Annals of medicine · 2026Article
- Association of baseline eosinophil counts with clinical prognosis in cancer patients undergoing immune checkpoint inhibitor therapy.iScience · 2026Article
- A monocyte-centered framework for predicting immunochemotherapy efficacy in lung squamous cell carcinoma patients.EMBO molecular medicine · 2026Article
- Prognostic value of lactate dehydrogenase to albumin ratio in first-line chemoimmunotherapy for locally advanced or metastatic non-small cell lung cancer.Translational cancer research · 2025Article
- Evaluation of the efficacy of PD‑1/PD‑L1 inhibitor plus bevacizumab and chemotherapy for the treatment of patients with driver gene‑negative advanced‑stage lung adenocarcinoma: A retrospective cohort study.Oncology letters · 2025Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and aim: Some patients with lung cancer can benefit from immunotherapy, but the biomarkers that predict immunotherapy response were not well defined. Baseline characteristic of patients may be the most convenient and effective markers. Therefore, our study was designed to explore the association between baseline characteristics of patients with lung cancer and the efficacy of immunotherapy. Methods: A total of 216 lung cancer patients from Tianjin Medical University Cancer Institute & Hospital who received immunotherapy between 2017 and 2021 were included in the retrospective analysis. All baseline characteristic data were collected and then univariate log-rank analysis and multivariate COX regression analysis were performed. Kaplan-Meier analysis was used to evaluate patients' progression-free survival (PFS). A nomogram based on significant biomarkers was constructed to predict PFS rate of patients receiving immunotherapy. We evaluated the prediction accuracy of nomogram using C-indices and calibration curves. Results: Univariate analysis of all collected baseline factors showed that age, clinical stage, white blood cell (WBC), lymphocyte (LYM), monocyte (MON), eosinophils (AEC), hemoglobin (HB), lactate dehydrogenase (LDH), albumin (ALB) and treatment line were significantly associated with PFS after immunotherapy. Then these 10 risk factors were included in a multivariate regression analysis, which indicated that age (HR: 1.95, 95% CI [1.01-3.78], Conclusion: Age, ALB, MON, LDH, line can be used as reliable predictive biomarkers for PFS, response rate and cancer control in patients with lung cancer receiving immunotherapy. The nomogram based on age, ALB, MON, LDH, line was of great significance for predicting 1-year-PFS, 2-year-PFS and 3-year-PFS in patients with advanced lung cancer treated with immunotherapy.
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