Martina Sollini

Main scientific fields of interest include advanced image analysis, infection and inflammation, and new tracers. Advanced image analysis (radiomics and artificial intelligence) aims to identify novel image-derived biomarkers to characterize lesions, quantify tumor heterogeneity, and predict outcomes.

Research in infection and inflammation aims to validate accurate tools for early diagnosis and follow-up of patients, with particular regard to musculoskeletal and cardiovascular infections. Molecular imaging of infection has been enormously augmented during the past decades, gaining notability in clinical practice for diagnostic and prognostic purposes as well as for treatment decision making.

In the era of personalized medicine, new tracers capable of “capturing” specific molecular processes would impact on patients’ management and treatment tailoring, eventually improve final outcome.

Scientific acitivity

PI and co-investigator of many observational, profit and “no-profit” phase I/II clinical trials focused on oncological imaging and radioimmunotherapy.

Author/co-author of 61 papers on international peer reviewed journals (total IF = 245) and of 20 book’s chapters; h-index (Scopus, 01/09/2020) = 19. Coordinator of the “PREDICt – Profiling radioREsistant Differentiated thyroid Cancer: genes, immunity, cancer stem cells and epithelial-mesenchymal transition” project funded within the TRANSCAN-2 Joint Transnational Call 2017.

1. Sollini M, Antunovic L, Chiti A, Kirienko M. Towards Clinical Application of Image Mining: A Systematic Review on Artificial Intelligence and Radiomics. Eur J Nucl Med Mol Imaging. 2019 Jun 18. doi: 10.1007/s00259-019-04372-x
2. Sollini M, Trenti N, Malagoli E, Catalano M, Di Mento L, Kirienko A, Berlusconi M, Chiti A, Antunovic L. [¹⁸F]FDG PET/CT in non-union: improving the diagnostic performances by using both PET and CT criteria. Eur J Nucl Med Mol Imaging. 2019;46(8):1605-1615. doi: 10.1007/s00259-019-04336-1
3. Antunovic L, De Sanctis R, Cozzi L, Kirienko M, Sagona A, Torrisi R, Tinterri C, Santoro A, Chiti A, Zelic R, Sollini M. PET/CT radiomics in breast cancer: promising tool for prediction of pathological response to neoadjuvant chemotherapy. Eur J Nucl Med Mol Imaging. 2019; 46(7):1468-1477. doi: 10.1007/s00259-019-04313-8
4. Kirienko M, Cozzi L, Rossi A, Voulaz E, Antunovic L, Fogliata A, Chiti A, Sollini M. Ability of FDG PET and CT radiomics features to differentiate between primary and metastatic lung lesions. Eur J Nucl Med Mol Imaging. 2018 Sep;45(10):1649-1660. doi: 10.1007/s00259-018-3987-2
5. Kirienko M, Cozzi L, Rossi A, Voulaz E, Antunovic L, Fogliata A, Chiti A, Sollini M. Ability of FDG PET and CT radiomics features to differentiate between primary and metastatic lung lesions. Eur J Nucl Med Mol Imaging. 2018 Sep;45(10):1649-1660. doi: 10.1007/s00259-018-3987-2