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Tumor Immunotherapy

Tumor Immunotherapy

Immunotherapy is a treatment that boosts or manipulates the immune system to fight diseases, including cancer. For instance, by stimulating an immune response through vaccinations against viruses that cause cancers, like hepatitis B virus and human papillomavirus, these diseases can be prevented. Nonetheless, some cancer cells can avoid the immune system due to their rapid mutation and division. The immune response to many cancers involves three phases: elimination, equilibrium, and escape.

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Radiological artificial intelligence - predicting personalized immunotherapy outcomes in lung cancer.

Laila C Roisman^1,2, Waleed Kian^3,4, Alaa Anoze³

¹The Hebrew University, Helmsley Cancer Center, Shaare Zedek Medical Center, Jerusalem, Israel. dr.roisman@gmail.com.

NPJ Precision Oncology

|November 22, 2023

Summary

Deep learning and radiomics can help personalize lung cancer treatment by integrating complex patient data. These technologies aid in predicting immunotherapy response and overcoming challenges in precision medicine.

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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.