In the first part, dedicated to radiomic techniques in image analysis, the student will reach a good level of autonomy in doing data analysis also with open source software. For clinical part, a critical ability of analysis and interpretation of the data will be developed.
Course Prerequisites
Knowledge of specifics, limits and advantages of radiological investigation techniques and the basis for the selection of radiological tools and investigation methodologies to be adopted based on the specific clinical question.
Teaching Methods
Frontal lessons, laboratory activity (image segmentation and feature extraction) in the informatics laboratory of the campus
Assessment Methods
Oral exam also through the study of recent literature papers. Evaluation takes place via judgment (passed/ not passed) with the exception of IUSS students for whom a grade is required.
Texts
Radiomic techniques : slides and review papers given during the lectures. Possible book : Radiomics and Its Clinical Application Artificial Intelligence and Medical Big Data, 1st Edition - June 3, 2021, Authors: Jie Tian, Di Dong, Zhenyu Liu, Jingwei Wei Paperback ISBN: 9780128181010 eBook ISBN: 9780128181027
Contents
Radiomic techniques of data analysis: - introduction to radiomics and radiomic features - definition of the main radiomic features - radiomic models - radiomic workflow: "good" data acquisition, segmentation, software for analysis, statistical models or machine learning models - robustness, reliability and harmonization Review of main clinical application in the radiological and neuroradiological environment with a focus on translation into clinical practice. For body imaging review of application of radiomics divided into specific radiological areas: head and neck, chest radiology, abdominal application; technical challenges and minor application in msk and vascular imaging. A dedicated lecture will be given on clinical translation with critical review of the literature, sharing hand-on experience and discussion of future direction (radiomics integration into randomized clinical trial).
Course Language
Italian
More information
For "Radiomic techniques" : Alessandro Lascialfari, alessandro.lascialfari@unipv.it ; reception on request. For "Radiomics, Radiology and Neuroradiology applications". Anna Pichiecchio, anna.pichiecchio@unipv.it ; Lorenzo Preda, lorenzo.preda@unipv.it. Reception on request.
