ArticleFrontiers in medicine2024
Predicting lung cancer's metastats' locations using bioclinical model.
Article in Frontiers in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Interpretable machine learning based decision-making system for lung adenocarcinoma metastasis: a population-based study with exploration of multi-classification models.BMC medical informatics and decision making · 2026Article
- Animal models of lung cancer: Phenotypic comparison of different animal models of lung cancer and their application in the study of mechanisms.Animal models and experimental medicine · 2025Review
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Authors and funding
2 authors.
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Abstract
Background: Lung cancer is a global leading cause of cancer-related deaths, and metastasis profoundly influences treatment outcomes. The limitations of conventional imaging in detecting small metastases highlight the crucial need for advanced diagnostic approaches. Methods: This study developed a bioclinical model using three-dimensional CT scans to predict the spatial spread of lung cancer metastasis. Utilizing a three-layer biological model, we identified regions with a high probability of metastasis colonization and validated the model on real-world data from 10 patients. Findings: The validated bioclinical model demonstrated a promising 74% accuracy in predicting metastasis locations, showcasing the potential of integrating biophysical and machine learning models. These findings underscore the significance of a more comprehensive approach to lung cancer diagnosis and treatment. Interpretation: This study's integration of biophysical and machine learning models contributes to advancing lung cancer diagnosis and treatment, providing nuanced insights for informed decision-making.
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