Revolutionizing Treatment Planning: Habitat-Based Radiomics for Lateral Lymph Node Metastasis Prediction

Abstract

Background:Recent advancements in tumor microenvironment analysis have significantly impacted immunotherapy strategies, particularly in thyroid papillary carcinoma. This study focuses on the value of habitat-based radiomics for predicting lateral lymph node metastasis, a crucial factor in treatment planning and prognosis. Methods:The study selected participants with thyroid papillary carcinoma undergoing their first surgical treatment. Criteria included complete clinical data and enhanced CT imaging. Medical images were normalized and resampled for fixed-resolution pixel values. Radiomics features, classified into geometry, intensity, and texture, were extracted using the pyradiomics tool. Feature selection involved Intraclass Correlation Coefficient (ICC) and LASSO regression. Machine learning models such as Support Vector Machine (SVM), RandomForest (RF), and ExtraTrees (ET) were used to construct radiomic and habitat signatures with a specific focus on identifying lateral lymph node metastasis. Results:The habitat-based models demonstrated high efficacy in predicting lateral lymph node metastasis. The Habitat Signature showed higher accuracy (94.6% for SVM, 94.6% for RF, 91.9% for ET) and Area Under the Curve (AUC) values (0.988 for SVM, 0.961 for RF, 0.982 for ET) compared to the Radiomics Signature, specifically in identifying metastatic nodes. The Habitat model also had superior calibration performance, as evidenced by Hosmer-Lemeshow test statistics in training, validation, and test cohorts. Decision curve analysis indicated the Habitat Signature's potential for significant clinical benefit in predicting lateral lymph node involvement. Conclusion:Habitat-based radiomics analysis provides an accurate and efficient approach for predicting lateral lymph node metastasis in thyroid papillary carcinoma. This method enhances the predictive accuracy, facilitating better personalized treatment strategies in immunotherapy settings. It offers a promising tool in personalized medicine, especially for planning targeted treatment and assessing prognosis in thyroid cancer patients.