Imaging of Cancer and Radiation Therapy: Recent Advances and Challenges
A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Therapy".
Deadline for manuscript submissions: closed (15 July 2024) | Viewed by 5085
Special Issue Editors
2. Department of Radiology, Università Cattolica del S. Cuore, Fondazione Policlinico “A. Gemelli”, 00168 Rome, Italy
Interests: diffusion weighted Imaging; MRI; pelvis; genitourinary imaging
2. Department of Radiology, Università Cattolica del S. Cuore, Fondazione Policlinico “A. Gemelli”, 00168 Rome, Italy
Interests: MRI; pelvis; genitourinary imaging
2. Department of Radiology, Università Cattolica del S. Cuore, Fondazione Policlinico “A. Gemelli”, 00168 Rome, Italy
Interests: MRI; pelvis; genitourinary imaging
Special Issue Information
Dear Colleagues,
Radiation therapy is widely used in many types of the most common cancers such as breast, cervical, colorectal, and lung cancer, representing an important tool for cancer treatment.
In recent years, radiation therapy underwent rapid development due to the introduction of new advances, such as three-dimensional conformal therapy, stereotactic radiotherapy, intensity-modulated radiation therapy, image-guided and stereotactic body radiation therapy, tomotherapy, brachytherapy techniques, and proton or heavy-ion therapy, as well as low doses to sensitize to some cystostatic agents.
Recently, new imaging techniques have been introduced, such as radiomics and artificial intelligence. Radiomics involves the extraction of a large amount of quantitative information called “features” from medical images (e.g., CT, MRI, PET) through the process of segmentation of the pathological findings, which are then converted into quantifiable data and analyzed by software. The yielded computer-extracted data can be related to tumor biology and other clinical, pathologic, and genomic data. The process of feature extraction can be implemented by automatic segmentation software, which has the additional potential to obtain a more detailed analysis of MRI without increasing the time burden on the interpreting radiologist.
These modern approaches could be able to predict disease response and support decision making regarding the continuation or modification of treatment planning, as well as integration with other treatment modalities.
This Special Issue aims to explore the most modern advances of radiomics and artificial intelligence applied to the radiotherapeutic treatment of various types of cancers, and to explore their added value in the management of cancer treatment.
Prof. Dr. Riccardo Manfredi
Dr. Valerio Di Paola
Dr. Luca Russo
Guest Editors
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