Al momento stai visualizzando Radiomics for Precision Medicine

Radiomics for Precision Medicine


Diagnostics medical imaging such as Magnetic Resonance Imaging (MRI), and Computed Tomography (CT),  have a crucial role in detecting and characterizing tumors. However, recent evidences have demonstrated that it is possible to improve the diagnostic accuracy of currently available diagnostic tests by extracting information not evaluable through the human eye from radiological images, through a process called radiomics.

Radiomics allows the extraction and selection of features that are consequently fed into AI models in order to characterize lesion’s pathological characteristics and molecular status. The output is, therefore, correlated either with the patient’s phenotype, or with gene expression patterns and mutations, creating a bridge between radiology, pathology, genomics, and artificial intelligence (AI).


The aim of this project is to develop and validate radiomics signature to timely predict response to therapy of tumors based on radiomics and artificial intelligence. 

The AI-based solution we develop could be a game changer in the management of oncological patients allowing to tailor both the type of surgery and chemotherapy according to the features of each patient tumor.  This AI-based marker will also be very cost-effective, since it relays on material and information already available for any patient, in any hospital across the planet (radiological images). 


  1. Alleanza contro il Cancro (ACC) – WG RADIOMICS
  2. Alleanza contro il Cancro (ACC) – WG LUNG RATIONALE


  1. Giannini V, Pusceddu L, Defeudis A, et al. Delta-Radiomics Predicts Response to First-Line Oxaliplatin-Based Chemotherapy in Colorectal Cancer Patients with Liver Metastases. Cancers (Basel). 2022;14(1). doi:10.3390/cancers14010241
  2. Rizzetto F, Calderoni F, De Mattia C, et al. Impact of inter-reader contouring variability on textural radiomics of colorectal liver metastases. Eur Radiol Exp. 2020;4(1). doi:10.1186/s41747-020-00189-8
  3. Defeudis A, Cefaloni L, Giannetto G, et al. Comparison of radiomics approaches to predict resistance to 1st line chemotherapy in liver metastatic colorectal cancer. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. ; 2021:3305-3308. doi:10.1109/EMBC46164.2021.9630316
  4. Defeudis A, de Mattia C, Rizzetto F, et al. Standardization of CT radiomics features for multi-center analysis: Impact of software settings and parameters. Phys Med Biol. 2020;65(19). doi:10.1088/1361-6560/ab9f61
  5. Giannini V, Defeudis A, Rosati S, et al. An innovative radiomics approach to predict response to chemotherapy of liver metastases based on CT images. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. Vol 2020-July. ; 2020:1339-1342. doi:10.1109/EMBC44109.2020.9176627
  6. Giannini V, Rosati S, Defeudis A, et al. Radiomics predicts response of individual HER2-amplified colorectal cancer liver metastases in patients treated with HER2-targeted therapy. Int J Cancer. 2020;147(11):3215-3223. doi:10.1002/ijc.33271
  7. Vassallo L, Traverso A, Agnello M, et al. A cloud-based computer-aided detection system improves identification of lung nodules on computed tomography scans of patients with extra-thoracic malignancies. Eur Radiol. 2019;29(1):144-152. doi:10.1007/s00330-018-5528-6
  8. Regge D, Mazzetti S, Giannini V, Bracco C, Stasi M. Big data in oncologic imaging. Radiol Medica. 2017;122(6):458-463. doi:10.1007/s11547-016-0687-5