ArticleEuropean radiology2025
A radiopathomics model for predicting large-number cervical lymph node metastasis in clinical N0 papillary thyroid carcinoma.
Article in European radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Development and internal validation of a multimodal MRI-FNAC radiomics-pathomics model for predicting cervical lymph node metastasis in papillary thyroid carcinoma.Gland surgery · 2026Article
- Combined clinicopathological and ultrasonographic features for predicting extrathyroidal extension in thyroid cancer.Quantitative imaging in medicine and surgery · 2026Article
- Radiomics-Based machine learning for preoperative prediction of Large-Number central lymph node metastasis in papillary thyroid carcinoma.Endocrine · 2026Article
- A clinically translatable pathomics-based predictive model for preoperative prognostic assessment in patients with endometrial cancer.Discover oncology · 2026Article
- Ultrasound radiomics for preoperative evaluation of Ki-67 proliferation index in papillary thyroid carcinoma.Frontiers in oncology · 2026Article
- Development and Validation of a Multi-Omics Model Integrating US-Derived and WSI-Based Features to Predict Lymph Node Metastasis in Ovarian Cancer: A Multi-Center Retrospective Study.International journal of women's health · 2026Article
- Development and validation of a machine learning-based pathomics nomogram for prognostic prediction in endometrial cancer.Discover oncology · 2025Article
- Multimodal ultrasonographic and clinicopathological model for predicting high-volume lymph node metastasis in cN0 papillary thyroid carcinoma.Frontiers in endocrinology · 2025Article
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8 authors.
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Abstract
objectivesThis study aimed to develop a multimodal radiopathomics model utilising preoperative ultrasound (US) and fine-needle aspiration cytology (FNAC) to predict large-number cervical lymph node metastasis (CLNM) in patients with clinically lymph node-negative (cN0) papillary thyroid carcinoma (PTC). MATERIALS AND
methodsThis multicentre retrospective study included patients with PTC between October 2017 and June 2024 across seven institutions. Patients were categorised based on the presence or absence of large-number CLNM in training, validation, and external testing cohorts. A clinical model was developed based on the maximum diameter of thyroid nodules. Radiomics features were extracted from US images and pathomics features were extracted from FNAC images. Feature selection was performed using univariate analysis, correlation analysis, and least absolute shrinkage and selection operator regression. Six machine learning (ML) algorithms were employed to construct radiomics, pathomics, and radiopathomics models. Predictive performance was assessed using the area under the curve (AUC), and decision curve analysis (DCA).
resultsA total of 426 patients with PTC (41.65 ± 12.47 years; 124 men) were included in this study, with 213 (50%) exhibiting large-number CLNM. The multimodal radiopathomics model demonstrated excellent predictive capabilities with AUCs 0.921, 0.873, 0.903; accuracies (ACCs) 0.852, 0.800, 0.833; sensitivities (SENs) 0.876, 0.867, 0.857; specificities (SPEs) 0.829, 0.733, 0.810, for the training, validation, and testing cohorts, respectively. It significantly outperformed the clinical model (AUCs 0.730, 0.698, 0.630; ACCs 0.690, 0.656, 0.627; SENs 0.686, 0.378, 0.556; SPEs 0.695, 0.933, 0.698), the radiomics model (AUCs 0.819, 0.762, 0.783; ACCs 0.752, 0.722, 0.738; SENs 0.657, 0.844, 0.937; SPEs 0.848, 0.600, 0.540), and the pathomics model (AUCs 0.882, 0.786, 0.800; ACCs 0.829, 0.756, 0.786; SENs 0.819, 0.889, 0.857; SPEs 0.838, 0.633, 0.714).
conclusionThe multimodal radiopathomics model demonstrated significant advantages in the preoperative prediction of large-number CLNM in patients with cN0 PTC. KEY POINTS: Question Accurate preoperative prediction of large-number CLNM in PTC patients can guide treatment plans, but single-modality diagnostic performance remains limited. Findings The radiopathomics model utilising preoperative US and FNAC images effectively predicted large-number CLNM in both validation and testing cohorts, outperforming single predictive models. Clinical relevance Our study proposes a multimodal radiopathomics model based on preoperative US and FNAC images, which can effectively predict large-number CLNM in PTC preoperatively and guide clinicians in treatment planning.
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