Radiomics to Predict Response to Neoadjuvant Chemotherapy in Rectal Cancer: Influence of Simultaneous Feature Selection and Classifier Optimization

Samanta Rosati, Claudia Maria Gianfreda1, Gabriella Balestra2, Valentina Giannini3, Simone Mazzetti3, Daniele Regge3

  • 1Politecnico di Torino
  • 3University of Turin



Poster Session


10:00 - 17:00 | Mon 29 Oct | Foyer | A1P-E

Cognitive Computing & Deep Learning in Life Sciences

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According to the guidelines, patients with locally advanced colorectal cancer undergo neoadjuvant chemotherapy. However, response to therapy is reached only up to 30% of cases. Therefore, it would be important to predict response to therapy before treatment. In this study, we demonstrated that the simultaneous optimization of feature subset and classifier parameters on different imaging datasets (T2w, DWI and PET) could improve classification performance. On a dataset of 51 patients (21 responders, 30 non responders), we obtained an accuracy of 90%, 84% and 76% using three optimized SVM classifiers fed with selected features from PET, T2w and ADC images, respectively.

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