ArticleGland surgery2024
Intranodular and perinodular ultrasound radiomics distinguishes benign and malignant thyroid nodules: a multicenter study.
Article in Gland surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Multimodal Ultrasound-Based Decision Support for Bethesda IV Thyroid Nodules: Integration of Clinical, Radiomics, Deep Learning, and Topological Features.Journal of imaging informatics in medicine · 2026Article
- Can Whole-Thyroid-Based CT Radiomics Model Achieve the Performance of Lesion-Based Model in Predicting the Thyroid Nodules Malignancy? - A Comparative Study.Journal of imaging informatics in medicine · 2026Article
- Super-resolution ultrasound radiomics for pre-FNA prediction of nondiagnostic (Bethesda I) thyroid nodules.Frontiers in endocrinology · 2026Article
- Development and validation of an intra-tumoral and peri-tumoral radiomics model based on dynamic contrast-enhanced ultrasound for predicting lymph node metastasis in type 2 diabetic patients with thyroid cancer.Frontiers in endocrinology · 2026Article
- Multimodal model enhances qualitative diagnosis of hypervascular thyroid nodules: integrating radiomics and deep learning features based on B-mode and PDI images.Gland surgery · 2025Article
- Ultrasound radiomics-based machine learning models for risk stratification of follicular thyroid tumors.Frontiers in oncology · 2025Article
- Multimodal ultrasound radiomics containing microflow images for the prediction of central lymph node metastasis in papillary thyroid carcinoma.Frontiers in oncology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Ultrasound based radiomics prediction model can improve the differentiation ability of benign and malignant thyroid nodules to avoid overtreatment. This study evaluates the role of predictive models based on intranodular and perinodular ultrasound radiomics in distinguishing between benign and malignant thyroid nodules. Methods: A total of 1,076 thyroid nodules were enrolled from three hospitals between 2016 and 2022, forming the training, validation and test cohorts. The clinical signature (Clinic_Sig) was developed based on clinical information and conventional morphological features of ultrasound. Expanding 1 pixel, 3 pixels, 5 pixels, 7 pixels, and 9 pixels outward from the thyroid nodule, six radiomics models were constructed using intranodular (intra) and combined radiomics (intranodular and perinodular: +p1,+p3,+p5,+p7,+p9) features. The model with the best area under the curve (AUC) was defined as radiomics signature (Rad_Sig). The combined model was constructed from Clinic_Sig and Rad_Sig. AUC and calibration curves were used to evaluate the predictive performance of the model. Decision curve analysis (DCA) was used to evaluate the clinical net benefit of the model. Results: The intra+p1 radiomics model exhibited the highest efficacy (AUC =0.863) in the test cohort, which was combined with Clinic_Sig to construct the combined model. Compared with Clinic_Sig and Rad_Sig, the combined model showed the higher predictive performance, with AUCs of 0.942 (training), 0.894 (validation), and 0.933 (test). The calibration curve showed that the predicted probabilities of the combined model were in good agreement with the actual probabilities, and DCA indicated that it provided more net benefit than the treat-none or treat-all scheme. Conclusions: The combined model based on clinical signatures, intranodular and perinodular ultrasound radiomics has the potential to effectively predict benign or malignant thyroid nodules.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.