Endocrine practice : official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists · 2025
Deep Learning for the Diagnosis and Treatment of Thyroid Cancer: A Review.
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Gao Rili, Mai Shangqing, Wang Song, Hu Wuqiang, Chang Zhangqi, Wu Guozhi, Guan Haixia
Аннотация
OBJECTIVE: In recent years, the application of deep learning (DL) technology in the thyroid field has expanded rapidly, driving substantial innovation in thyroid disease research. This review aims to provide clinicians with the latest research advances in the application of DL to the diagnosis and treatment of thyroid cancer.
METHODS: A systematic review was conducted of studies published in the past five years in the PubMed database on the application of deep learning in the diagnosis, treatment, and prognosis of thyroid cancer.
RESULTS: DL has made substantial advances in the diagnosis and treatment of thyroid cancer, particularly through the application of advanced models such as convolutional neural networks, long short-term memory networks, and generative adversarial networks. These models have delivered breakthrough performance in key areas, including ultrasound image analysis of thyroid nodules, automated classification of pathological images, and assessment of extrathyroidal extension. DL also shows considerable promise for individualized treatment planning and prognosis prediction. Nonetheless, its widespread clinical adoption is hindered by substantial technical, clinical, and ethical challenges. Addressing these barriers is crucial to achieving meaningful improvements in thyroid cancer care and realizing the full potential of DL in precision medicine.
CONCLUSION: DL techniques are advancing the precision diagnosis and treatment of thyroid cancer and hold the potential to enhance diagnostic accuracy and improve therapeutic outcomes for patients.
METHODS: A systematic review was conducted of studies published in the past five years in the PubMed database on the application of deep learning in the diagnosis, treatment, and prognosis of thyroid cancer.
RESULTS: DL has made substantial advances in the diagnosis and treatment of thyroid cancer, particularly through the application of advanced models such as convolutional neural networks, long short-term memory networks, and generative adversarial networks. These models have delivered breakthrough performance in key areas, including ultrasound image analysis of thyroid nodules, automated classification of pathological images, and assessment of extrathyroidal extension. DL also shows considerable promise for individualized treatment planning and prognosis prediction. Nonetheless, its widespread clinical adoption is hindered by substantial technical, clinical, and ethical challenges. Addressing these barriers is crucial to achieving meaningful improvements in thyroid cancer care and realizing the full potential of DL in precision medicine.
CONCLUSION: DL techniques are advancing the precision diagnosis and treatment of thyroid cancer and hold the potential to enhance diagnostic accuracy and improve therapeutic outcomes for patients.