Publications

  1. Panic J, Rosati S, Defeudis A, Mazzetti S, Giannetto G, Micilotta M, Vassallo L, Gatti M, Regge D, Balestra G, Giannini V.
    A Fully Automatic Deep Learning Algorithm to Segment Rectal Cancer on MR Images: A Multi-Center Study. In: IEEE Engineering in Medicine and Biology Conference, EMBC 2022 – Conference Proceedings. ; 2022
  2. Maimone G, Mazzetti S, Nicoletti G, Regge D, Giannini V. Comparison of Machine and Deep Learning models for automatic segmentation of prostate cancers on multiparametric MRI. In Proceedings of IEEE Medical Measurements and Applications, MeMeA 2022.
  3. Defeudis A, Panic J, Vassallo L,  Regge D, Giannini V. A Deep Learning model to segment liver metastases on CT images acquired at different time-points during chemotherapy. In: IEEE Medical Measurements and Applications, MeMeA 2022- Conference Proceedings. ; 2022.
  4. Panic J, Giannini V, Defeudis A, Regge D, Balestra G, and Rosati S.  Impact of network parameters on a U-Net based system for rectal cancer segmentation on MR images. In: IEEE Medical Measurements and Applications, MeMeA 2022- Conference Proceedings. ; 2022.
  5. Nicoletti G, Barra D, Defeudis A, et al. Virtual biopsy in prostate cancer: Can machine learning distinguish low and high aggressive tumors on MRI. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. ; 2021:3374-3377. doi:10.1109/EMBC46164.2021.9630988
  6. Defeudis A, Mazzetti S, Panic J, et al. MRI-based radiomics to predict response in locally advanced rectal cancer: comparison of manual and automatic segmentation on external validation in a multicentre study. Eur Radiol Exp. 2022;6(1). doi:10.1186/s41747-022-00272-2
  7. 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
  8. Giannini V, Mazzetti S, Defeudis A, et al. A Fully Automatic Artificial Intelligence System Able to Detect and Characterize Prostate Cancer Using Multiparametric MRI: Multicenter and Multi-Scanner Validation. Front Oncol. 2021;11. doi:10.3389/fonc.2021.718155
  9. Russo F, Mazzetti S, Regge D, et al. Reply to Anwar R. Padhani, Ivo G. Schoots, Jelle O. Barentsz. Fast Magnetic Resonance Imaging as a Viable Method for Directing the Prostate Cancer Diagnostic Pathway. Eur Urol Oncol 2021;4:863-5: Fast-MRI Feasibility in Biopsy-naïve Patients: Clarificatio. Eur Urol Oncol. 2021;4(6):866-867. doi:10.1016/j.euo.2021.06.005
  10. Barra D, Nicoletti G, Defeudis A, et al. Deep learning model for automatic prostate segmentation on bicentric T2w images with and without endorectal coil. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. ; 2021:3370-3373. doi:10.1109/EMBC46164.2021.9630792
  11. Coppola F, Giannini V, Gabelloni M, et al. Radiomics and magnetic resonance imaging of rectal cancer: From engineering to clinical practice. Diagnostics. 2021;11(5). doi:10.3390/diagnostics11050756
  12. Russo F, Mazzetti S, Regge D, et al. Diagnostic Accuracy of Single-plane Biparametric and Multiparametric Magnetic Resonance Imaging in Prostate Cancer: A Randomized Noninferiority Trial in Biopsy-naïve Men. Eur Urol Oncol. 2021;4(6):855-862. doi:10.1016/j.euo.2021.03.007
  13. 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
  14. Panic J, Defeudis A, Mazzetti S, et al. A Convolutional Neural Network based system for Colorectal cancer segmentation on MRI images. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. Vol 2020-July. ; 2020:1675-1678. doi:10.1109/EMBC44109.2020.9175804
  15. 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
  16. 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
  17. 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
  18. De Santi B, Salvi M, Giannini V, et al. Comparison of Histogram-based Textural Features between Cancerous and Normal Prostatic Tissue in Multiparametric Magnetic Resonance Images. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. Vol 2020-July. ; 2020:1671-1674. doi:10.1109/EMBC44109.2020.9176307
  19. Giannini V, Mazzetti S, Cappello G, et al. Computer-aided diagnosis improves the detection of clinically significant prostate cancer on multiparametric-mri: A multi-observer performance study involving inexperienced readers. Diagnostics. 2021;11(6). doi:10.3390/diagnostics11060973
  20. 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
  21. Giannini V, Defeudis A, Rosati S, et al. Deep learning to segment liver metastases on CT images: Impact on a radiomics method to predict response to chemotherapy. In: IEEE Medical Measurements and Applications, MeMeA 2020 – Conference Proceedings. ; 2020. doi:10.1109/MeMeA49120.2020.9137150
  22. Santi BD, Salvi M, Giannini V, et al. Multimodal T2w and DWI Prostate Gland Automated Registration. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. ; 2019:4427-4430. doi:10.1109/EMBC.2019.8856467
  23. Rosati S, Gianfreda CM, Balestra G, Giannini V, Mazzetti S, Regge D. Radiomics to predict response to neoadjuvant chemotherapy in rectal cancer: Influence of simultaneous feature selection and classifier optimization. In: 2018 IEEE Life Sciences Conference, LSC 2018. ; 2018:65-68. doi:10.1109/LSC.2018.8572194
  24. Russo F, Manfredi M, Panebianco V, et al. Radiological Wheeler staging system: A retrospective cohort analysis to improve the local staging of prostate cancer with multiparametric MRI. Minerva Urol e Nefrol. 2019;71(3):264-272. doi:10.23736/S0393-2249.19.03248-X
  25. D’Alonzo M, Martincich L, Fenoglio A, et al. Nipple-sparing mastectomy: external validation of a three-dimensional automated method to predict nipple occult tumour involvement on preoperative breast MRI. Eur Radiol Exp. 2019;3(1). doi:10.1186/s41747-019-0108-3
  26. 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
  27. Rosati S, Gianfreda CM, Balestra G, Martincich L, Giannini V, Regge D. Correlation based Feature Selection impact on the classification of breast cancer patients response to neoadjuvant chemotherapy. In: MeMeA 2018 – 2018 IEEE International Symposium on Medical Measurements and Applications, Proceedings. ; 2018. doi:10.1109/MeMeA.2018.8438698
  28. Giannini V, Mazzetti S, Bertotto I, et al. Predicting locally advanced rectal cancer response to neoadjuvant therapy with 18 F-FDG PET and MRI radiomics features. Eur J Nucl Med Mol Imaging. 2019;46(4):878-888. doi:10.1007/s00259-018-4250-6
  29. Giannini V, Rosati S, Castagneri C, Martincich L, Regge D, Balestra G. Radiomics for pretreatment prediction of pathological response to neoadjuvant therapy using magnetic resonance imaging: Influence of feature selection. In: Proceedings – International Symposium on Biomedical Imaging. Vol 2018-April. ; 2018:285-288. doi:10.1109/ISBI.2018.8363575
  30. Giannini V, Bianchi V, Carabalona S, et al. MRI to predict nipple-areola complex (NAC) involvement: An automatic method to compute the 3D distance between the NAC and tumor. J Surg Oncol. 2017;116(8):1069-1078. doi:10.1002/jso.24788
  31. 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
  32. Giannini V, Mazzetti S, Marmo A, Montemurro F, Regge D, Martincich L. A computer-aided diagnosis (CAD) scheme for pretreatment prediction of pathological response to neoadjuvant therapy using dynamic contrast-enhanced MRI texture features. Br J Radiol. 2017;90(1077). doi:10.1259/bjr.20170269
  33. Rosati S, Giannini V, Castagneri C, Regge D, Balestra G. Dataset homogeneity assessment for a prostate cancer CAD system. In: 2016 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2016 – Proceedings. ; 2016. doi:10.1109/MeMeA.2016.7533734
  34. Giannini V, Rosati S, Regge D, Balestra G. Specificity improvement of a CAD system for multiparametric MR prostate cancer using texture features and artificial neural networks. Health Technol (Berl). 2017;7(1):71-80. doi:10.1007/s12553-016-0150-6
  35. Giannini V, Mazzetti S, Armando E, et al. Multiparametric magnetic resonance imaging of the prostate with computer-aided detection: experienced observer performance study. Eur Radiol. 2017;27(10):4200-4208. doi:10.1007/s00330-017-4805-0
  36. Mazzetti S, Giannini V, Russo F, Regge D. Computer-aided diagnosis of prostate cancer using multi-parametric MRI: Comparison between PUN and Tofts models. Phys Med Biol. 2018;63(9). doi:10.1088/1361-6560/aab956
  37. Vignati A, Mazzetti S, Giannini V, et al. Texture features on T2-weighted magnetic resonance imaging: New potential biomarkers for prostate cancer aggressiveness. Phys Med Biol. 2015;60(7):2685-2701. doi:10.1088/0031-9155/60/7/2685
  38. Giannini V, Mazzetti S, Vignati A, et al. A fully automatic computer aided diagnosis system for peripheral zone prostate cancer detection using multi-parametric magnetic resonance imaging. Comput Med Imaging Graph. 2015;46:219-226. doi:10.1016/j.compmedimag.2015.09.001
  39. Giannini V, Rosati S, Regge D, Balestra G. Texture features and artificial neural networks: A way to improve the specificity of a CAD system for multiparametric MR prostate cancer. In: IFMBE Proceedings. Vol 57. ; 2016:296-301. doi:10.1007/978-3-319-32703-7_59
  40. Giannini V, Vignati A, Mirasole S, et al. MR-T2-weighted signal intensity: a new imaging biomarker of prostate cancer aggressiveness. Comput Methods Biomech Biomed Eng Imaging Vis. 2016;4(3-4):130-134. doi:10.1080/21681163.2014.910476
  41. Rosati S, Balestra G, Giannini V, Mazzetti S, Russo F, Regge D. ChiMerge discretization method: Impact on a computer aided diagnosis system for prostate cancer in MRI. In: 2015 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2015 – Proceedings. ; 2015:297-302. doi:10.1109/MeMeA.2015.7145216
  42. Giannini V, Vignati A, De Luca M, et al. A novel and fully automated registration method for prostate cancer detection using multiparametric magnetic resonance imaging. J Med Imaging Heal Informatics. 2015;5(6):1171-1182. doi:10.1166/jmihi.2015.1518
  43. Russo F, Regge D, Armando E, et al. Detection of prostate cancer index lesions with multiparametric magnetic resonance imaging (mp-MRI) using whole-mount histological sections as the reference standard. BJU Int. 2016;118(1):84-94. doi:10.1111/bju.13234
  44. Rossi F, Savino A, Giannini V, et al. A 3D Voxel Neighborhood Classification Approach within a Multiparametric MRI Classifier for Prostate Cancer Detection. Vol 9043.; 2015. doi:10.1007/978-3-319-16483-0_24
  45. Agliozzo S, De Luca M, Bracco C, et al. Computer-aided diagnosis for dynamic contrast-enhanced breast MRI of mass-like lesions using a multiparametric model combining a selection of morphological, kinetic, and spatiotemporal features. Med Phys. 2012;39(4):1704-1715. doi:10.1118/1.3691178
  46. Giannini V, Vignati A, De Luca M, et al. Registration, Lesion Detection, and Discrimination for Breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging.; 2013. doi:10.1117/3.1000499.Ch4
  47. Vignati A, Giannini V, Carbonaro LA, et al. A new algorithm for automatic vascular mapping of DCE-MRI of the breast: Clinical application of a potential new biomarker. Comput Methods Programs Biomed. 2014;117(3):482-488. doi:10.1016/j.cmpb.2014.09.003
  48. Savino A, Benso A, Di Carlo S, et al. A prostate cancer computer aided diagnosis software including malignancy tumor probabilistic classification. In: BIOIMAGING 2014 – 1st International Conference on Bioimaging, Proceedings; Part of 7th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2014. ; 2014:49-54. doi:10.5220/0004799100490054
  49. Giannini V, Vignati A, Mazzetti S, et al. A prostate CAD system based on multiparametric analysis of DCE T1-w, and DW automatically registered images. In: Proceedings of SPIE – The International Society for Optical Engineering. Vol 8670. ; 2013. doi:10.1117/12.2006336
  50. Giannini V, Vignati A, Mirasole S, et al. MR-T2-weighted signal intensity: A new imaging marker of prostate cancer aggressiveness. In: Computational Vision and Medical Image Processing IV – Proceedings of Eccomas Thematic Conference on Computational Vision and Medical Image Processing, VIPIMAGE 2013. ; 2014:25-30
  51. Vignati A, Giannini V, Bert A, et al. A fully automatic multiscale 3-dimensional hessian-based algorithm for vessel detection in breast DCE-MRI. Invest Radiol. 2012;47(12):705-710. doi:10.1097/RLI.0b013e31826dc3a4
  52. De Luca M, Giannini V, Vignati A, et al. A fully automatic method to register the prostate gland on T2-weighted and EPI-DWI images. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. ; 2011:8029-8032. doi:10.1109/IEMBS.2011.6091980
  53. Giannini V, Vignati A, Morra L, et al. A fully automatic algorithm for segmentation of the breasts in DCE-MR images. In: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC’10. ; 2010:3146-3149. doi:10.1109/IEMBS.2010.5627191
  54. Mazzetti S, De Luca M, Bracco C, et al. A CAD system based on multi-parametric analysis for cancer prostate detection on DCE-MRI. In: Progress in Biomedical Optics and Imaging – Proceedings of SPIE. Vol 7963. ; 2011. doi:10.1117/12.877549
  55. De Luca M, Giannini V, Vignati A, et al. A fully automatic method to register the prostate gland on T2-weighted and EPI-DWI images. Conf Proc IEEE Eng Med Biol Soc. 2011;2011:8029-8032.
  56. Vignati A, Giannini V, Bert A, et al. A fully automatic lesion detection method for DCE-MRI fat-suppressed breast images. In: Progress in Biomedical Optics and Imaging – Proceedings of SPIE. Vol 7260. ; 2009. doi:10.1117/12.811526
  57. Vignati A, Giannini V, De Luca M, et al. Performance of a fully automatic lesion detection system for breast DCE-MRI. J Magn Reson Imaging. 2011;34(6):1341-1351. doi:10.1002/jmri.22680