Researchers in China have pioneered a method to detect critical transitions in thyroid cancer progression, enhancing early identification of high-risk patients. Published in Molecular Diagnosis & Therapy, the study employs artificial intelligence (AI) to evaluate molecular changes and classify tumor subtypes based on immune characteristics. Focusing on differentiated thyroid carcinoma (DTC) at stage II, the research identifies this stage as a pivotal transition point, marked by increased molecular instability.
To facilitate personalized treatment, the team developed the TCPSLevel molecular scoring system, which highlights early-warning signals. Patients with elevated TCPSLevel scores face greater disease aggressiveness and poorer outcomes, outperforming traditional staging methods. By analyzing over 1,100 samples, researchers identified three molecular subtypes, linked to the gene ASPH. A simplified 12-gene model, miniPC, allows for reliable subtype identification, advancing personalized treatment strategies. Integrating multiple biological data layers, this innovative approach underscores AI’s role in enhancing thyroid cancer management.
