ArticleEuropean radiology2023
A novel approach to quantify calcifications of thyroid nodules in US images based on deep learning: predicting the risk of cervical lymph node metastasis in papillary thyroid cancer patients.
Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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17 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- Chinese management guidelines for radioactive iodine-refractory differentiated thyroid cancer (2025 edition).European journal of nuclear medicine and molecular imaging · 2025Guideline
- From diagnostic marker-to-therapeutic modality: emerging paradigms in tumor biomineralization.Cancer biology & medicine · 2026Review
- Can GPT-5.0 Interpret Thyroid Ultrasound Images? A Comparative TI-RADS Analysis with an Expert Radiologist.Diagnostics (Basel, Switzerland) · 2026Article
- Dual-database Bibliometric Analysis Combined with Gephi-based Network Visualization of Artificial Intelligence Applications in the Identification and Diagnosis of Thyroid Space-occupying Lesions.Current medical imaging · 2026Article
- A "calcification"-enhanced deep learning approach for precise differentiation of thyroid nodules.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Preoperative multiparametric ultrasound for the prediction of central lymph node metastasis in papillary thyroid carcinoma.Quantitative imaging in medicine and surgery · 2025Article
- Interpretable deep fuzzy network-aided detection of central lymph node metastasis status in papillary thyroid carcinoma.International journal of computer assisted radiology and surgery · 2025Article
- Letter to the Editor: AI-driven thyroid malignancy prediction.European radiology · 2025Article
- Multimodal GPT model for assisting thyroid nodule diagnosis and management.NPJ digital medicine · 2025Article
- Coagulation and thyroiditis are factors associated with adverse pathological features in differentiated thyroid cancer: a retrospective cohort study.BMC surgery · 2025Article
- A cascaded clinical-ultrasound-biochemical model for precise prediction before thyroid nodule fine-needle aspiration biopsy.Frontiers in medicine · 2025Article
- Thyroid nodule and lymph node metastasis assessment from ultrasound images using deep learning.Frontiers in neuroscience · 2025Article
- Deep learning model for diagnosis of thyroid nodules with size less than 1 cm: A multicenter, retrospective study.European journal of radiology open · 2024Article
- Contrast-enhanced ultrasound image sequences based on radiomics analysis for diagnosis of metastatic cervical lymph nodes from thyroid cancer.Gland surgery · 2024Article
- Predictive Values of Clinical Features and Multimodal Ultrasound for Central Lymph Node Metastases in Papillary Thyroid Carcinoma.Diagnostics (Basel, Switzerland) · 2024Review
- A narrative review of deep learning in thyroid imaging: current progress and future prospects.Quantitative imaging in medicine and surgery · 2024Review
- Deep learning-assisted ultrasonic diagnosis of cervical lymph node metastasis of thyroid cancer: a retrospective study of 3059 patients.Frontiers in oncology · 2024Article
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Authors and funding
9 authors at 3 institutions in 2 countries.
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Abstract
objectiveBased on ultrasound (US) images, this study aimed to detect and quantify calcifications of thyroid nodules, which are regarded as one of the most important features in US diagnosis of thyroid cancer, and to further investigate the value of US calcifications in predicting the risk of lymph node metastasis (LNM) in papillary thyroid cancer (PTC).
methodsBased on the DeepLabv3+ networks, 2992 thyroid nodules in US images were used to train a model to detect thyroid nodules, of which 998 were used to train a model to detect and quantify calcifications. A total of 225 and 146 thyroid nodules obtained from two centers, respectively, were used to test the performance of these models. A logistic regression method was used to construct the predictive models for LNM in PTCs.
resultsCalcifications detected by the network model and experienced radiologists had an agreement degree of above 90%. The novel quantitative parameters of US calcification defined in this study showed a significant difference between PTC patients with and without cervical LNM (p < 0.05). The calcification parameters were beneficial to predicting the LNM risk in PTC patients. The LNM prediction model using these calcification parameters combined with patient age and other US nodular features showed a higher specificity and accuracy than the calcification parameters alone.
conclusionsOur models not only detect the calcifications automatically, but also have value in predicting cervical LNM risk of PTC patients, thereby making it possible to investigate the relationship between calcifications and highly invasive PTC in detail. CLINICAL RELEVANCE STATEMENT: Due to the high association of US microcalcifications with thyroid cancers, our model will contribute to the differential diagnosis of thyroid nodules in daily practice. KEY POINTS: • We developed an ML-based network model for automatically detecting and quantifying calcifications within thyroid nodules in US images. • Three novel parameters for quantifying US calcifications were defined and verified. • These US calcification parameters showed value in predicting the risk of cervical LNM in PTC patients.
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