Work

The Clinical Value of Distinguishing Benign and Malignant Thyroid Micronodules With Intratumoral and Peritumoral CT Radiomic Models

Year: 2026

Type: article

Abstract: OBJECTIVE: To study the utility of intratumoral and peritumoral radiomic models in distinguishing between benign and malignant thyroid micronodules. METHODS: CT-enhanced images of 607 patients with thyroid micronodules were obtained, and an additional 100 patients with micronodules were collected as an independent test group on the basis of the sequence of their examination times. The peritumoral region of interest (ROI) was delineated by expanding outward by 1 and 2 mm. Radiomics features were extracted from the 3-dimensional volume of interest (VOI), which included intratumoral, peritumoral 1 mm, peritumoral 2 mm, intratumoral+peritumoral 1 mm and 2 mm in the arterial phase, venous phase, and combined arteriovenous phase. All of the data were randomly divided into training and validation groups at an 8:2 ratio. The minimum redundancy maximum relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO) method was utilized for regression dimension reduction. Radscore, clinical, and combined models were constructed. A nomogram was used to assess the performance of the fusion model, and an external test group was utilized to test the model's performance. RESULTS: The intratumoral+peritumoral 1 mm radiomic model based on arterial-venous phase fusion had the best prediction performance (AUC training group 0.838; 95% CI: 0.802-0.873; validation group 0.827; 95% CI: 0.755-0.899; external test group 0.770; 95% CI: 0675-0.864). The combined model constructed with the radscore and clinical model was the optimal model (AUC training group 0.859; 95% CI: 0.826-0.892; validation group 0.876; 95% CI: 0817-0.935; external test group 0.810; 95% CI: 0.723-0.897). CONCLUSION: Both the intratumoral and peritumoral radiomic models had good diagnostic efficacy in differentiating benign and malignant thyroid micronodules.

Source: Journal of Computer Assisted Tomography

DOI: 10.1097/rct.0000000000001909

Authors: Dan Kong, Fen Wang, Wenli Shan, Yingying Zhuang, Yan Zeng, Lili Guo

Institutions: Huaian First People’s Hospital, Nanjing Medical University, United Imaging Intelligence (China)

Language: English

Topic: Radiomics and Machine Learning in Medical Imaging

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

Funder: Beijing Medical Award Foundation