Habitat imaging based on enhanced CT for predicting occult central lymph node metastasis in papillary thyroid carcinoma.
Authors
Affiliations (6)
Affiliations (6)
- Department of Medical lmaging, The First Affiliated Hospital of Kunming Medical University, Kunming, China.
- Department of Radiology, Anning First People's Hospital Affiliated to Kunming University of Science and Technology, Kunming, China.
- Department of Radiology, Yunnan Cancer Hospital (The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan Campus), Kunming, China.
- Department of Ultrasonography, The First Affiliated Hospital of Kunming Medical University, Kunming, China. [email protected].
- Department of Medical lmaging, The First Affiliated Hospital of Kunming Medical University, Kunming, China. [email protected].
- Department of Medical lmaging, The First Affiliated Hospital of Kunming Medical University, Kunming, China. [email protected].
Abstract
This study aimed to develop a combined model based on clinical, intratumoral habitat, and peritumoral radiomic features to predict occult central lymph node metastasis (OCLNM) in patients with papillary thyroid carcinoma (PTC). This retrospective study analysed preoperative enhanced CT images and clinical parameters from 219 PTC patients from two medical centers. The patients were divided into a training cohort from Center 1 (n = 154) and an external validation cohort from Center 2 (n = 65). Habitat radiomics features from the tumor and 3-mm peritumoral region were extracted from both arterial phase (AP) and venous phase (VP) CT images, respectively. Five machine learning (ML) methods were employed to construct models. The area under the curve (AUC) was used to evaluate the model performance, thereby selecting the best-performing ML model. Ultimately, a combined model was developed by integrating clinical factors with the optimal AP and VP habitat models. Age, TSH, and PLT were identified as independent predictors. Both the intratumoral habitat and peritumoral radiomics models based on LR exhibited excellent performance in the training and external validation cohorts (AUC: 0.820 and 0.805 in AP; 0.884 and 0.850 in VP). The combined model demonstrated superior predictive capability, achieving AUCs of 0.932 in the training cohort and 0.878 in the external validation cohort. The combined model integrating intratumoral habitat and peritumoral radiomics from enhanced CT images with clinical features provides a potential, non-invasive preoperative method for predicting OCLNM in PTC patients and may provide supplementary information for preoperative risk stratification. Question Accurate preoperative diagnosis of OCLNM in PTC is crucial for guiding dissection but remains an urgent clinical challenge. Findings A combined model integrating CT-based intratumoral habitats, peritumoral radiomics, and clinical factors achieved superior performance (AUC: 0.878) in predicting occult metastases across two centers. Clinical relevance This non-invasive model may serve as an adjunctive tool for preoperative risk stratification, providing supplementary information to support individualized surgical planning in patients with PTC.