Objective To develop a comprehensive preoperative risk stratification model using habitat imaging combined with multimodal analysis for identifying low-risk papillary thyroid carcinoma (PTC) patients suitable for active surveillance. Materials and methods This multicenter study analyzed 1215 patients with pathologically confirmed PTC from four Chinese institutions. Habitat imaging analysis was performed on preoperative CT and ultrasound images using K-means clustering and supervoxel segmentation. Radiomic features were extracted from ultrasound habitats using PyRadiomics, while multi-scale index (MSI) features were extracted from CT habitats. Clinical characteristics and immunological markers were identified through multivariate logistic regression. Six machine learning algorithms were evaluated with three fusion strategies to integrate imaging features with clinical data. Results Four ultrasound habitats and five CT habitats were identified. Ultrasound Habitat 2 achieved an AUC of 0.92 in training and 0.80–0.92 in validation. CT habitat analysis using MSI features achieved an AUC of 0.93 in training and 0.88–0.92 in validation. The optimal ensemble fusion model integrating CT-derived MSI features, ultrasound habitat characteristics, clinical parameters (chronic lymphocytic thyroiditis and tumor size) and immunological markers (platelet-to-lymphocyte ratio) achieved an AUC of 0.98 in training, 0.95 in internal validation, and 0.95–0.99 across external validation cohorts, with accuracy exceeding 0.88 in all validation sets. Conclusion Habitat imaging combined with multimodal analysis provides superior preoperative risk stratification for PTC, enabling personalized treatment planning and identification of low-risk patients suitable for active surveillance while potentially reducing unnecessary surgical interventions. Critical relevance statement Habitat imaging combined with multimodal analysis provides superior preoperative risk stratification for papillary thyroid carcinoma, enabling personalized treatment decisions and reducing unnecessary surgical interventions. Key Points <UnorderedList Mark="Bullet"> <ItemContent> Current papillary thyroid carcinoma (PTC) risk stratification relies on postoperative pathology, limiting preoperative treatment planning. </ItemContent> <ItemContent> Multimodal habitat imaging achieved exceptional performance across validation cohorts. </ItemContent> <ItemContent> This framework enables personalized treatment planning and identifies low-risk patients for active surveillance. </ItemContent> </UnorderedList> Graphical Abstract

Habitat imaging combined with multimodal analysis for preoperative risk stratification of papillary thyroid carcinoma

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发表日期：

2025年12月2日

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Jia-Wei Feng,
You-Long Zhu,
Lu Zhang,
Yu-Xin Yang,
An-Cheng Qin,
Shui-Qing Liu,
Yong Jiang

摘要

Objective

To develop a comprehensive preoperative risk stratification model using habitat imaging combined with multimodal analysis for identifying low-risk papillary thyroid carcinoma (PTC) patients suitable for active surveillance.

Materials and methods

This multicenter study analyzed 1215 patients with pathologically confirmed PTC from four Chinese institutions. Habitat imaging analysis was performed on preoperative CT and ultrasound images using K-means clustering and supervoxel segmentation. Radiomic features were extracted from ultrasound habitats using PyRadiomics, while multi-scale index (MSI) features were extracted from CT habitats. Clinical characteristics and immunological markers were identified through multivariate logistic regression. Six machine learning algorithms were evaluated with three fusion strategies to integrate imaging features with clinical data.

Results

Four ultrasound habitats and five CT habitats were identified. Ultrasound Habitat 2 achieved an AUC of 0.92 in training and 0.80–0.92 in validation. CT habitat analysis using MSI features achieved an AUC of 0.93 in training and 0.88–0.92 in validation. The optimal ensemble fusion model integrating CT-derived MSI features, ultrasound habitat characteristics, clinical parameters (chronic lymphocytic thyroiditis and tumor size) and immunological markers (platelet-to-lymphocyte ratio) achieved an AUC of 0.98 in training, 0.95 in internal validation, and 0.95–0.99 across external validation cohorts, with accuracy exceeding 0.88 in all validation sets.

Conclusion

Habitat imaging combined with multimodal analysis provides superior preoperative risk stratification for PTC, enabling personalized treatment planning and identification of low-risk patients suitable for active surveillance while potentially reducing unnecessary surgical interventions.

Critical relevance statement

Habitat imaging combined with multimodal analysis provides superior preoperative risk stratification for papillary thyroid carcinoma, enabling personalized treatment decisions and reducing unnecessary surgical interventions.

Key Points

Current papillary thyroid carcinoma (PTC) risk stratification relies on postoperative pathology, limiting preoperative treatment planning.

Multimodal habitat imaging achieved exceptional performance across validation cohorts.

This framework enables personalized treatment planning and identifies low-risk patients for active surveillance.

Graphical Abstract