Topic:

Radiomics is defined as a “process designed to extract a large number of quantitative features from digital images”. Its final aim is to extract hidden information from images that can be useful to develop Computer-Aided Diagnosis (CAD) systems and decision support. Since images are processed as matrices of data, radiomics can be potentially applied to all kinds of images, such as CT, MRI, PET.

In collaboration with the Candiolo Cancer Institute and the Department of Surgical Science of Università di Torino, we apply radiomics and artificial intelligence techniques to develop CAD Systems with a two-fold aim: tumor detection/segmentation and prediction of the metastases response to chemotherapy.

People:

  • Gabriella Balestra (Confirmed Assistant Professor)
  • Samanta Rosati (Assistant Professor with time contract (RTD/a))
  • Jovana Panic (Ph.D. Student)
  • Collaborators:

  • Daniele Regge, Candiolo Cancer Institute, FPO -IRCCS, Candiolo (TO), Italy and University of Turin, Department of Surgical Science, Torino, Italy.
  • Valentina Giannini, University of Turin, Department of Surgical Science, Torino, Italy

  • Recent publications:

  • Radiomics to predict response to neoadjuvant chemotherapy in rectal cancer: Influence of simultaneous feature selection and classifier optimization
      Rosati, S., Gianfreda, C.M., Balestra, G., Giannini, V., Mazzetti, S., Regge, D.
      IEEE Life Sciences Conference, LSC 2018
      10.1109/LSC.2018.8572194
  • Radiomics predicts response of individual HER2-amplified colorectal cancer liver metastases in patients treated with HER2-targeted therapy
      V. Giannini, S. Rosati, A. Defeudis, G. Balestra, L. Vassallo, G. Cappello, S. Mazzetti, C. De Mattia, F. Rizzetto, A. Torresin, A. Sartore-Bianchi, S. Siena, A. Vanzulli, F. Leone, V. Zagonel, S. Marson
      International journal of cancer
      10.1002/ijc.33271
  • A Convolutional Neural Network based system for Colorectal cancer segmentation on MRI images
      J. Panic; A. Defeudis; S. Mazzetti; S. Rosati; G. Giannetto; L. Vassallo; D. Regge; G. Balestra; V. Giannini
      2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
      10.1109/EMBC44109.2020.9175804