Radiomics for bone tumour 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 analysing complex patterns within imaging data, radiomics can provide detailed insights into tumour 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 tumours, which can help in discriminating between benign and malignant lesions and predicting treatment response and outcome in malignant tumours. In bone tumour diagnosis, radiomics can improve accuracy in differentiating benign lesions from skeletal metastases and primary malignant tumours, identifying different bone-tumour histotypes, and predicting tumour grade. Regarding management, it aids in treatment planning by predicting response to neoadjuvant therapy in chemosensitive tumours, such as osteosarcoma, thus personalising treatment strategies. Additionally, radiomic features can be used to monitor disease progression and recurrence of malignant bone tumours 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 standardising imaging protocols and ensuring reproducibility of radiomic features remain.