ArticleBMC medical imaging2021
Radiomics based on multiparametric MRI for extrathyroidal extension feature prediction in papillary thyroid cancer.
Article in BMC medical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 34 citations in OpenAlex.
- The Role of Cytology, Histology and Molecular Pathology in the Diagnostic Process of Thyroid Nodules.Cancers · 2026Review
- CT Radiomics-Based Machine Learning Model for Predicting Capsular and Neural Invasion in Thyroid Carcinoma: Diagnostic Accuracy Study.JMIR medical informatics · 2026Article
- Combined Ultrasound and MRI Assessment in Patients Undergoing Reoperation for Recurrent Papillary Thyroid Carcinoma: Oncological Outcomes and Surgical Safety.Current oncology (Toronto, Ont.) · 2026Article
- Prediction of gross extrathyroidal extension in papillary thyroid carcinoma using a deep learning radiomics model based on post-processing techniques for multi-phase contrast-enhanced MRI images.Frontiers in endocrinology · 2026Article
- Development and validation of the multidimensional machine learning model for preoperative risk stratification in papillary thyroid carcinoma: a multicenter, retrospective cohort study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Impact of artificial intelligence-based and traditional image preprocessing and resampling on MRI-based radiomics for classification of papillary thyroid carcinoma.BJR artificial intelligence · 2025Article
- Application and validation of the machine learning-based multimodal radiomics model for preoperative prediction of lateral lymph node metastasis in papillary thyroid carcinoma.Frontiers in endocrinology · 2025Article
- Construction of a predictive model for cervical lymph node metastasis in papillary thyroid carcinoma.Frontiers in oncology · 2025Article
- Application progress of artificial intelligence in managing thyroid disease.Frontiers in endocrinology · 2025Review
- Prediction of Follicular Thyroid Neoplasm and Malignancy of Follicular Thyroid Neoplasm Using Multiparametric MRI.Journal of imaging informatics in medicine · 2024Article
- A study of machine learning models for rapid intraoperative diagnosis of thyroid nodules for clinical practice in China.Cancer medicine · 2024Article
- Preoperative prediction of extrathyroidal extension: radiomics signature based on multimodal ultrasound to papillary thyroid carcinoma.BMC medical imaging · 2023Article
- Predicting Extrathyroidal Extension in Papillary Thyroid Carcinoma Using a Clinical-Radiomics Nomogram Based on B-Mode and Contrast-Enhanced Ultrasound.Diagnostics (Basel, Switzerland) · 2023Article
- Three-dimensional ultrasound-based radiomics nomogram for the prediction of extrathyroidal extension features in papillary thyroid cancer.Frontiers in oncology · 2023Article
- Review
- Radiomics in Differentiated Thyroid Cancer and Nodules: Explorations, Application, and Limitations.Cancers · 2021Review
- RADIOMICS BASED ON TWO-DIMENSIONAL AND THREE-DIMENSIONAL ULTRASOUND FOR EXTRATHYROIDAL EXTENSION FEATURE PREDICTION IN PAPILLARY THYROID CARCINOMA.Acta endocrinologica (Bucharest, Romania : 2005)Article
- Detecting Aggressive Papillary Thyroid Carcinoma Using Hyperspectral Imaging and Radiomic Features.Proceedings of SPIE--the International Society for Optical EngineeringArticle
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundTo determine the predictive capability of MRI-based radiomics for extrathyroidal extension detection in papillary thyroid cancer (PTC) pre-surgically.
methodsThe present retrospective trial assessed individuals with thyroid nodules examined by multiparametric MRI and subsequently administered thyroid surgery. Diagnosis and extrathyroidal extension (ETE) feature of PTC were based on pathological assessment. The thyroid tumors underwent manual segmentation, for radiomic feature extraction. Participants were randomized to the training and testing cohorts, at a ratio of 7:3. The mRMR (maximum correlation minimum redundancy) algorithm and the least absolute shrinkage and selection operator were utilized for radiomics feature selection. Then, a radiomics predictive model was generated via a linear combination of the features. The model's performance in distinguishing the ETE feature of PTC was assessed by analyzing the receiver operating characteristic curve.
resultsTotally 132 patients were assessed in this study, including 92 and 40 in the training and test cohorts, respectively). Next, the 16 top-performing features, including 4, 7 and 5 from diffusion weighted (DWI), T2-weighted (T2 WI), and contrast-enhanced T1-weighted (CE-T1WI) images, respectively, were finally retained to construct the radiomics signature. There were 8 RLM, 5 CM, 2 shape, and 1 SZM features. The radiomics prediction model achieved AUCs of 0.96 and 0.87 in the training and testing sets, respectively.
conclusionsOur study indicated that MRI radiomics approach had the potential to stratify patients based on ETE in PTCs preoperatively.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.