ArticleComputational and structural biotechnology journal2024
A clinical-information-free method for early diagnosis of lung cancer from the patients with pulmonary nodules based on backpropagation neural network model.
Article in Computational and structural biotechnology journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Feasibility of BMI-based sub-milliSievert low-dose CT in individualized detection of lung nodules.European radiology · 2026Article
- Computational prediction of TCR cross-reactivity: principles, challenges and translational opportunities.Journal of translational medicine · 2026Review
- LCPBert: ProtBERT-based early-stage lung cancer prediction from T cell receptor beta sequences.iScience · 2026Article
- Liquid biopsy biomarkers for accurate detection of malignant pulmonary nodules: a meta-analytic approach.Discover oncology · 2026Article
- Large-Scale T-cell Receptor Repertoire Profiling Unveils Tumor-Specific Signals for Diagnosing Indeterminate Pulmonary Nodules.Cancer research · 2025Article
- [Recent Advances in Peripheral Immunoscore in Lung Cancer].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2025Review
- A machine learning framework for predicting cognitive impairment in aging populations using urinary metal and demographic data.Frontiers in genetics · 2025Article
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10 authors.
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Abstract
Lung cancer is the main cause of cancer-related deaths worldwide. Due to lack of obvious clinical symptoms in the early stage of the lung cancer, it is hard to distinguish between malignancy and pulmonary nodules. Understanding the immune responses in the early stage of malignant lung cancer patients may provide new insights for diagnosis. Here, using high-through-put sequencing, we obtained the TCRβ repertoires in the peripheral blood of 100 patients with Stage I lung cancer and 99 patients with benign pulmonary nodules. Our analysis revealed that the usage frequencies of TRBV, TRBJ genes, and V-J pairs and TCR diversities indicated by D50s, Shannon indexes, Simpson indexes, and the frequencies of the largest TCR clone in the malignant samples were significantly different from those in the benign samples. Furthermore, reduced TCR diversities were correlated with the size of pulmonary nodules. Moreover, we built a backpropagation neural network model with no clinical information to identify lung cancer cases from patients with pulmonary nodules using 15 characteristic TCR clones. Based on the model, we have created a web server named "Lung Cancer Prediction" (LCP), which can be accessed at http://i.uestc.edu.cn/LCP/index.html.
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