Core Viewpoint - The article discusses a breakthrough in diagnosing indeterminate pulmonary nodules (IPN) through a new AI-based diagnostic model called TCRNodseek Plus, developed by a team of Chinese researchers. This model leverages T-cell receptor (TCR) profiling to provide a more accurate assessment of lung nodules, moving from morphological guesswork to immunological evidence [1][2]. Research Background - The study titled "Large-Scale T-cell Receptor Repertoire Profiling Unveils Tumor-Specific Signals for Diagnosing Indeterminate Pulmonary Nodules" is set to be published in December 2025 in the journal Cancer Research [2]. Immune Signals in Early Tumor Detection - The research is based on the human immune system's ability to detect abnormalities early in tumor formation, even before imaging features are visible. T-cells, as key players in immune response, exhibit specific TCR patterns when lung cancer cells are present, indicating an "immune storm" [6]. Technical Approach - The research team faced significant challenges in detecting weak tumor-specific signals in blood samples, akin to "finding a needle in a haystack." They constructed a leading lung nodule immune database and optimized detection technologies, achieving a correlation of over 0.97 in PCR amplification [9]. AI-Enabled Diagnostic Model - The TCRNodseek Plus model integrates multiple dimensions of data, including blood immune profiles and imaging features, using advanced machine learning algorithms. In a validation study with 1,107 patients, the model achieved an AUC of 0.84, outperforming the commonly used Mayo model [11]. Decision-Making Logic - The model employs a rigorous "dual-threshold" decision-making logic, ensuring a 95% positive predictive value for high-risk malignant nodules and a 93% negative predictive value for benign nodules. This approach clarifies diagnostic directions for over 60% of patients with indeterminate pulmonary nodules, reducing unnecessary invasive procedures [11]. Global Collaboration Platform - The research team has made the LungTCR database publicly accessible, facilitating global collaboration in lung cancer research. This platform allows clinicians and researchers to upload sequencing data and obtain diagnostic references, breaking down data barriers and promoting standardized applications in lung cancer diagnostics [13].