Novel Quantitative Imaging for Predicting Response to Therapy: Techniques and Clinical Applications

Area of Science:

• Oncology
• Radiology
• Medical Imaging Analysis

Background:

• Current Response Evaluation Criteria in Solid Tumors (RECIST) has limitations in accurately monitoring cancer treatment response.
• Radiomics, extracting quantitative imaging features, shows potential for predicting and monitoring therapy effectiveness.
• Non-small cell lung cancer (NSCLC) treatment monitoring requires improved evaluation methods.

Purpose of the Study:

• To provide an overview of radiomic approaches for predicting and monitoring treatment response in NSCLC.
• To summarize radiomic features, including shape and texture, within tumors and their microenvironment.
• To discuss the application of delta-radiomics for monitoring therapeutic changes over time.

Main Methods:

• Computerized extraction of quantitative imaging features (radiomics).
• Analysis of radiomic features such as tumor shape and texture.
• Utilizing longitudinal imaging data for delta-radiomics analysis.

Main Results:

• Radiomics demonstrates promise in predicting and monitoring treatment response in NSCLC.
• Radiomic features, including texture, within tumors and microenvironment are relevant.
• Delta-radiomics effectively monitors changes in therapy response over time.

Conclusions:

• Radiomics serves as a valuable prognostic marker for treatment effectiveness in NSCLC.
• Radiomics enables early detection of therapy response for various treatments like immunotherapy and chemoradiation.
• Radiomic approaches enhance the evaluation of treatment response beyond traditional RECIST criteria.