Abstract
Immunotherapy is one of the most interesting and promising cancer treatments. Encouraging results have confirmed the effectiveness of immunotherapy drugs for treating tumors in terms of long-term survival and a significant reduction in toxicity compared to more traditional chemotherapy approaches. However, the percentage of patients eligible for immunotherapy is rather small, and this is likely related to the limited knowledge of physiological mechanisms by which certain subjects respond to the treatment while others have no benefit. To address this issue, the authors propose an innovative approach based on the use of a non-linear cellular architecture with a deep downstream classifier for selecting and properly augmenting 2D features from chest-abdomen CT images toward improving outcome prediction. The proposed pipeline has been designed to make it usable over an innovative embedded Point of Care system. The authors report a case study of the proposed solution applied to a specific type of aggressive tumor, namely Metastatic Urothelial Carcinoma (mUC). The performance evaluation (overall accuracy close to 93%) confirms the proposed approach effectiveness.
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Rundo, F. et al. (2021). Advanced Non-linear Generative Model with a Deep Classifier for Immunotherapy Outcome Prediction: A Bladder Cancer Case Study. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12661. Springer, Cham. https://doi.org/10.1007/978-3-030-68763-2_17
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DOI: https://doi.org/10.1007/978-3-030-68763-2_17
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