Radiomics for predicting sensitivity to neoadjuvant chemotherapy in osteosarcoma: current status and advances

Area of Science:

• Oncology
• Radiology
• Medical Imaging Analysis

Background:

• Osteosarcoma is the most common primary malignant bone tumor, primarily affecting adolescents.
• Current treatments combine chemotherapy and surgery, but patient response varies significantly (30-60% response rate).
• Chemotherapy sensitivity is a key prognostic factor, highlighting the need for predictive tools.

Purpose of the Study:

• To explore the potential of radiomics in predicting neoadjuvant chemotherapy efficacy in osteosarcoma.
• To leverage quantitative imaging features for personalized treatment strategies and improved clinical outcomes.

Main Methods:

• Radiomics extracts high-throughput quantitative features (morphological, intensity, texture) from medical images (CT, MRI, PET/CT).
• Advanced computer vision algorithms automate feature extraction, overcoming limitations of manual interpretation.
• These features characterize tumor heterogeneity and microenvironment.

Main Results:

• Radiomics provides a quantitative approach to tumor phenotyping.
• Extracted features offer insights into tumor characteristics beyond traditional imaging.
• Radiomics has demonstrated significant value in predicting chemotherapy response in osteosarcoma.

Conclusions:

• Radiomics offers a powerful, objective tool for assessing osteosarcoma.
• Its ability to predict treatment response can guide personalized neoadjuvant chemotherapy.
• This quantitative imaging paradigm holds potential for improving patient outcomes in osteosarcoma management.