Radiological artificial intelligence - predicting personalized immunotherapy outcomes in lung cancer

Area of Science:

• Oncology
• Artificial Intelligence
• Medical Imaging

Background:

• Personalized medicine revolutionizes lung cancer treatment by tailoring therapies to individual patients.
• Physicians face challenges integrating tumor molecular profile, microenvironment, resistance, and metastases for optimal treatment.
• Limited biomarkers and combination therapy choices complicate precision medicine in lung cancer.

Purpose of the Study:

• To review innovative technologies like deep learning and radiomics for lung cancer precision therapy.
• To explain the benefits of these technologies in predicting immunotherapy response.
• To discuss future challenges in integrating these tools into clinical practice.

Main Methods:

• Review of current literature on deep learning and radiomics in oncology.
• Analysis of how these technologies address clinical decision-making complexities.
• Exploration of their role in predicting immunotherapy outcomes.

Main Results:

• Deep learning and radiomics offer potential to support clinical decision-making in lung cancer.
• These technologies can integrate diverse clinical data for precision therapy delivery.
• Potential benefits include improved prediction of immunotherapy response.

Conclusions:

• Innovative technologies like deep learning and radiomics show promise for advancing lung cancer precision medicine.
• Integration of these tools can help overcome current treatment challenges.
• Further research is needed to address future implementation hurdles.