ArticleLa Radiologia medica2021
Magnetic resonance imaging (MRI) radiomics of papillary thyroid cancer (PTC): a comparison of predictive performance of multiple classifiers modeling to identify cervical lymph node metastases before surgery.
Article in La Radiologia medica, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 1 of them a synthesis that pooled it.
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Who cites it
43 citing papers in PubMed, 1 synthesis or guideline pooled it, 56 citations in OpenAlex.
- Prediction of cervical lymph node metastasis in differentiated thyroid cancer based on radiomics models.The British journal of radiology · 2024Pooled it
- 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 Ultrasound and MRI Assessment in Patients Undergoing Reoperation for Recurrent Papillary Thyroid Carcinoma: Oncological Outcomes and Surgical Safety.Current oncology (Toronto, Ont.) · 2026Article
- MRI characteristics of cervical radioiodine-avid lymph nodes detected on post-therapeutic ¹³¹I whole-body scintigraphy in differentiated thyroid carcinoma.Frontiers in endocrinology · 2026Article
- A novel hybrid deep learning and chaotic dynamics approach for thyroid cancer classification.Scientific reports · 2025Article
- Machine learning model for differentiating malignant from benign thyroid nodules based on the thyroid function data.BMJ open · 2025Article
- Predicting lymph node metastasis in papillary thyroid carcinoma: radiomics using two types of ultrasound elastography.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Exploration of the Evaluation Value of Combined Magnetic Resonance Imaging and Ultrasound Blood Flow Parameters in Cervical Lymph Node Metastasis of Thyroid Cancer.Cancer management and research · 2025Article
- Plasma exchange as a rescue therapy for treatment-resistant thyroid storm with concurrent heart failure: a literature review based on a case report.International journal of emergency medicine · 2024Article
- Prediction of Follicular Thyroid Neoplasm and Malignancy of Follicular Thyroid Neoplasm Using Multiparametric MRI.Journal of imaging informatics in medicine · 2024Article
- Using T2-weighted magnetic resonance imaging-derived radiomics to classify cervical lymphadenopathy in children.Pediatric radiology · 2024Article
- Artificial intelligence assisted diagnosis of early tc markers and its application.Discover oncology · 2024Review
- Imaging of human papilloma virus (HPV) related oropharynx tumour: what we know to date.Infectious agents and cancer · 2023Review
- Prognostic Assessment of Gastropancreatic Neuroendocrine Neoplasm: Prospects and Limits of Radiomics.Diagnostics (Basel, Switzerland) · 2023Review
- Radiomics in colorectal cancer patients.World journal of gastroenterology · 2023Review
- Computed Tomography Urography: State of the Art and Beyond.Tomography (Ann Arbor, Mich.) · 2023Review
- Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma.Diagnostics (Basel, Switzerland) · 2023Review
- Colorectal liver metastases patients prognostic assessment: prospects and limits of radiomics and radiogenomics.Infectious agents and cancer · 2023Review
- Dose Reduction Strategies for Pregnant Women in Emergency Settings.Journal of clinical medicine · 2023Review
- Post-Surgical Imaging Assessment in Rectal Cancer: Normal Findings and Complications.Journal of clinical medicine · 2023Review
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Authors and funding
8 authors at 3 institutions in 1 country.
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Abstract
purposeTo compare predictive efficiency of multiple classifiers modeling and establish a combined magnetic resonance imaging (MRI) radiomics model for identifying lymph node (LN) metastases of papillary thyroid cancer (PTC) preoperatively. MATERIALS AND
methodsA retrospective analysis based on the preoperative MRI scans of 109 PTC patients including 77 patients with LN metastases and 32 patients without metastases was conducted, and we divided enroll cases into trained group and validation group. Radiomics signatures were selected from fat-suppressed T2-weighted MRI images, and the optimal characteristics were confirmed by spearman correlation test, hypothesis testing and random forest methods, and then, eight predictive models were constructed by eight classifiers. The receiver operating characteristic (ROC) curves analysis were performed to demonstrate the effectiveness of the models.
resultsThe area under the curve (AUC) of ROC based on MRI texture diagnosed LN status by naked eye was 0.739 (sensitivity = 0.571, specificity = 0.906). Based on the 5 optimal signatures, the best AUC of MRI radiomics model by logistics regression classifier had a considerable prediction performance with AUCs 0.805 in trained group and 0.760 in validation group, respectively, and a combination of best radiomics model with visual diagnosis of MRI texture had a high AUC as 0.969 (sensitivity = 0.938, specificity = 1.000), suggesting combined model had a preferable diagnostic efficiency in evaluating LN metastases of PTC.
conclusionOur combined radiomics model with visual diagnosis could be a potentially effective strategy to preoperatively predict LN metastases in PTC patients before clinical intervention.
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