Guest Editors
Prof. Dania Cioni
Email: dania.cioni@unipi.it
Affiliation: Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy
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Research Interests: radiology, magnetic resonance imaging, ultrasound, oncologic imaging, hepatocellular carcinoma
Dr. Salvatore Claudio Fanni
Email: fannisalvatoreclaudio@gmail.com
Affiliation: Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy
Homepage:
Research Interests: radiology, oncologic imaging, radiomics, machine learning, natural language processing, artificial intelligence
Summary
Imaging plays a pivotal role in oncology research, providing crucial insights into treatment response and prognostic evaluation. More than ever, advancements in hardware and software are enabling the development of reliable imaging biomarkers that enhance clinical decision-making. These non-invasive, objective, and robust biomarkers have the potential to revolutionize oncology by offering oncologists powerful tools to stratify patients and tailor treatments based on individual disease characteristics.
The integration of artificial intelligence, radiomics, and advanced imaging modalities is transforming how treatment efficacy is monitored and how prognosis is determined. From functional imaging techniques to quantitative imaging metrics, these advancements are improving the precision and reproducibility of imaging-based assessments. The ability to track tumor response dynamically and predict outcomes more accurately can ultimately lead to better patient management and improved survival rates.
This special issue invites original research and review articles that explore the latest innovations in oncologic imaging, novel imaging biomarkers, and their clinical applications in prognosis and treatment response assessment. By fostering collaboration between radiologists, oncologists, and data scientists, we aim to highlight cutting-edge imaging methodologies that support personalized oncology and drive progress in cancer care.
Keywords
Imaging Biomarkers, Oncologic Imaging, Treatment response assessmetn
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