ArticleInternational journal of surgery (London, England)2025
Clinical performance of a machine learning-based model for detecting lymph node metastasis in papillary thyroid carcinoma: A multicenter study.
Article in International journal of surgery (London, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Article
- Comprehensive molecular characterization of cfDNA as predictive and monitoring biomarkers in advanced gastric cancer receiving immunotherapy.Journal for immunotherapy of cancer · 2026Article
- A clinically practical machine learning nomogram for preoperative CLNM prediction in PTMC: tumor-capsule distance and D2-40 evidence.Frontiers in oncology · 2026Article
- Machine learning: predicting lymph node metastasis around the entrance point to the recurrent laryngeal nerve in cN0 papillary thyroid carcinoma.Frontiers in endocrinology · 2026Article
- PDE4C stabilized by ELAVL1 promotes lymph node metastasis in papillary thyroid cancer.American journal of cancer research · 2026Article
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Authors and funding
14 authors.
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Abstract
Papillary thyroid carcinoma (PTC) is a common endocrine malignancy with a generally favorable prognosis, but lymph node metastasis (LNM) complicates treatment and increases recurrence risk. Current preoperative methods like neck ultrasound often miss LNM, leading to unnecessary surgeries. This study developed a non-invasive, artificial intelligence (AI)-driven predictive model for LNM using gene expression data from 157 PTC patients and validated it with qRT-PCR across 807 participants from multiple centers. The model focused on three key genes - RPS4Y1, PKHD1L1, and CRABP1 - chosen for their predictive strength. A random forest algorithm achieved high accuracy, with an AUROC of 0.992 in training and 0.911-0.953 in external validation. RPS4Y1 emerged as a standout predictor, showing the strongest distinction between metastatic and non-metastatic cases. The study also identified immune-related pathways, such as TGF-β signaling and cancer-associated fibroblast activation, as critical in metastasis. This gene expression-based model offers a non-invasive, cost-effective solution for predicting LNM, providing valuable insights to guide surgical decisions and reduce unnecessary procedures, ultimately improving patient outcomes.
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