Valerio Pugliese
Longitudinal Radio-Anatomical Modeling and Discovery of Prognostic Factors via Artificial Intelligence: an Ablation Study.
Rel. Filippo Molinari, Stefano Trebeschi, Laura Jovani Estacio Cerquin. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Biomedica, 2023
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Abstract
Response evaluation is a crucial aspect in the field of oncology as it allows clinicians to assess the effectiveness of anti-cancer treatments, make adjustments to management plans, and determine the overall prognosis of patients. The widely adopted quantitative tool for this purpose is the Response Evaluation Criteria In Solid Tumors (RECIST), which classifies therapy response based on one-dimensional diameter measurements of target lesions, categorizing them as partial response, stable disease, or progressive disease. However, RECIST has certain limitations, including inter- and intra- observer variability, as well as reliance on one-dimensional measurements only. These limitations can impact the accuracy of assessments and subsequently affect patient prognoses.
Therefore, there is a need for a new method to overcome these drawbacks
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