Al momento stai visualizzando Computer aided diagnosis for Breast Cancer

Computer aided diagnosis for Breast Cancer


BREAST CANCER IS the second most common malignancy after lung cancer and the most common cancer in women. Magnetic Resonance Imaging (MRI) shows promise in detecting both invasive and ductal carcinoma in situ cancers, gives information on the biological aggressiveness of tumors and may be used to evaluate response to neoadjuvant chemotherapy after therapy. However, MRI data analysis requires interpretation of hundreds of images and is therefore time-consuming and highly dependent on user experience. 

Artificial intelligence (AI) might play a crucial role in fastening reporting time by automatically highlight suspected regions and computing clinical biomarkers such as nipple-areolar complex involvement. More recently, the predictive value of quantitative biomarkers based on AI  has gained wide applications in breast medical image analysis for characterizing breast lesions and in predicting tumour response to therapy. 


The aim of this project is to develop Computer Aided Diagnosis Systems that can support radiologist in the detection and characterization of breast lesions and that can provide predictive biomarkers of response to treatment.


  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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


WO2010079519A1 – WIPO (PCT): Method and system for the automatic recognition of lesions in a set of breast magnetic resonance images