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- Explores AI applications to detect disease, tissue characterization, staging and correlation with molecular biomarkers
- Presents radiomic signatures to predict response to therapy, recurrence risk scores and patients classification
- Combines correlation of data sets withimaging and pathology findings
This book presents artificial intelligence applications that may help in detecting disease, defining tissue characterization (benign vs malignant), staging and correlation with molecular biomarkers. Originally positioned as a means for noninvasive molecular phenotyping and quantification in the 1970s, PET's technological improvements in the 2000s generated renewed interest in quantification, which has grown over the last five years. This progress is parallel with the development of Artificial intelligence (AI) systems for Oncology which aim at providing the best possible treatment to patients suffering from lung, breast, brain, prostate, liver and other types of cancer. The chapters provide an overview of the use of AI in PET/CT imaging for various types of cancer, and it will be an invaluable tool especially for nuclear medicine physicians and oncologists. Table of Contents- 1. Introduction: Artificial intelligence (AI) systems for Oncology.-
- 2. PET in Bone and Soft tissue tumors.-
- 3. (CNS) PET/CT: current AI applications.-
- 4. PET/CT findings in Head and Neck Cancer, current AI applications.-
- 5. PET/CT in Lung cancer, current AI applications.-
- 6. Breast cancer: PET/CT imaging, AI applications.-
- 7. PET/CT in Gynecologic cancer, current AI applications.-
- 8. PET/CT in Rectal cancer, current AI applications.-
- 9. PET/CT in Neuroendocrine tumors, current AI applications.-
- 10. PET/CT in the evaluation of Adrenal gland mass.-
- 11. PET/CT in Renal cancer.-
- 12. PET/CT in Testicular cancer.-
- 13. PET/CT in Prostate cancer.-
- 14. PET/CT in Malignant Lymphomas.
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