ArticleJournal of thoracic disease2022
Clinical predictors of treatment efficacy and a prognostic nomogram in patients with lung adenocarcinoma receiving immune checkpoint inhibitors: a retrospective study.
Article in Journal of thoracic disease, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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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.
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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.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.
- Systematic literature review of real-world evidence on overall survival in cancer patients before and after the approval of anti-PD-(L)1 therapy.Frontiers in oncology · 2025Pooled it
- Article
- An enhanced genetic mutation-based model for predicting the efficacy of immune checkpoint inhibitors in patients with melanoma.Frontiers in oncology · 2022Article
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: At present, there is no accurate biomarker for immune checkpoint inhibitors (ICIs). Since the efficacy of ICIs is associated with a variety of indicators, establishing a model to predict its efficacy is more clinically significant and in line with clinical needs. Methods: We collected and retrospectively analyzed the relationship between immunotherapy efficacy and clinicopathologic features in lung adenocarcinoma patients treated with ICIs. Progression-free survival (PFS) and overall survival (OS) were analyzed. Univariate and multivariate Cox proportional hazards regression analyses were conducted to identify prognostic factors associated with PFS. Besides, a clinical prediction model was established based on the results of the multivariate Cox regression analyses to predict PFS. Results: A total of 201 lung adenocarcinoma patients treated with ICIs were assessed. Univariate analysis showed that male gender [hazard ratio (HR) =0.521, 95% confidence interval (CI): 0.356-0.761, P=0.001], smoking (HR =0.595, 95% CI: 0.420-0.843, P=0.003), epidermal growth factor receptor (EGFR) wild type (HR =2.766, 95% CI: 1.719-4.452, P<0.001), Kirsten rat sarcoma viral oncogene homolog (KRAS) mutation (HR =0.449, 95% CI: 0.271-0.743, P=0.001), positive programmed death ligand 1 (PD-L1) expression (HR =0.527, 95% CI: 0.336-0.825, P=0.004), early tumor node metastasis (TNM) stage (HR =0.581, 95% CI: 0.344-0.983, P=0.039), no liver metastasis (HR =1.801, 95% CI: 1.046-3.102, P=0.031), ICIs combined with chemotherapy (HR =0.560, 95% CI: 0.384-0.815, P=0.002), having immune-related adverse effects (HR =0.354, 95% CI: 0.228-0.511, P<0.001) and first-line immunotherapy (HR =0.596, 95% CI: 0.420-0.845, P=0.003) were significantly associated with better PFS in patients with lung adenocarcinoma receiving immunotherapy. Multivariate analysis showed that smoking status, KRAS mutation, PD-L1 expression, line of immunotherapy and immune-related adverse effects were independent prognostic factors affecting PFS. A clinical prediction model was established to predict the PFS of lung adenocarcinoma patients treated with ICIs. The model showed good predictive ability via C-index, calibration curve and receiver operating characteristic (ROC) curve validation. Conclusions: The clinical prediction model developed in this study can be used to some extent to predict PFS after immunotherapy in lung adenocarcinoma patients. However, the model still needs to be validated in studies with large sample size.
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