Radiomics for bone tumor diagnosis and management

Radiomics is an advanced imaging technique that extracts quantitative features from medical images. It can be useful to support diagnosis and management, particularly in bone oncology. By analyzing complex patterns within imaging data, radiomics can provide detailed insights into tumor characteristics that are not apparent through visual inspection alone. This approach leverages machine learning algorithms to identify and quantify features like texture, shape, and intensity of tumors, which can help in discriminating between benign and malignant lesions and predicting treatment response and outcome in malignant tumors. In bone tumor diagnosis, radiomics can improve accuracy in differentiating benign lesions from skeletal metastases and primary malignant tumors, identifying different bone tumor histotypes and predicting tumor grade. Regarding management, it aids in treatment planning by predicting response to neoadjuvant therapy in chemosensitive tumors, such as osteosarcoma, thus personalizing treatment strategies. Additionally, radiomic features can be used to monitor disease progression and recurrence of malignant bone tumors more effectively than traditional imaging methods. Recent studies have demonstrated that integrating radiomics with clinical data enhances predictive models for patient outcomes. However, challenges such as standardizing imaging protocols and ensuring reproducibility of radiomic features remain.