Daniele La Forgia
Daniele La Forgia
IRCCS Istituto Tumori "Giovanni Paolo II" di Bari, Italy
Verified email at
Cited by
Cited by
A prospective comparative trial of adjunct screening with tomosynthesis or ultrasound in women with mammography-negative dense breasts (ASTOUND-2)
AS Tagliafico, G Mariscotti, F Valdora, M Durando, J Nori, D La Forgia, ...
European Journal of Cancer 104, 39-46, 2018
Radiomic analysis in contrast-enhanced spectral mammography for predicting breast cancer histological outcome
D La Forgia, A Fanizzi, F Campobasso, R Bellotti, V Didonna, V Lorusso, ...
Diagnostics 10 (9), 708, 2020
An exploratory radiomics analysis on digital breast tomosynthesis in women with mammographically negative dense breasts
AS Tagliafico, F Valdora, G Mariscotti, M Durando, J Nori, D La Forgia, ...
The Breast 40, 92-96, 2018
A machine learning approach on multiscale texture analysis for breast microcalcification diagnosis
A Fanizzi, TMA Basile, L Losurdo, R Bellotti, U Bottigli, R Dentamaro, ...
BMC bioinformatics 21, 1-11, 2020
Radiomics analysis on contrast-enhanced spectral mammography images for breast cancer diagnosis: A pilot study
L Losurdo, A Fanizzi, TMA Basile, R Bellotti, U Bottigli, R Dentamaro, ...
Entropy 21 (11), 1110, 2019
Fully automated support system for diagnosis of breast cancer in contrast-enhanced spectral mammography images
A Fanizzi, L Losurdo, TMA Basile, R Bellotti, U Bottigli, P Delogu, ...
Journal of Clinical Medicine 8 (6), 891, 2019
Microcalcification detection in full-field digital mammograms: A fully automated computer-aided system
TMA Basile, A Fanizzi, L Losurdo, R Bellotti, U Bottigli, R Dentamaro, ...
Physica Medica 64, 1-9, 2019
Radiomic feature reduction approach to predict breast cancer by contrast-enhanced spectral mammography images
R Massafra, S Bove, V Lorusso, A Biafora, MC Comes, V Didonna, ...
Diagnostics 11 (4), 684, 2021
Bacterial adhesion to urethral catheters: Role of coating materials and immersion in antibiotic solution
M Cormio, L.,La Forgia, P.,La Forgia, D.,Siitonen, A.,Ruutu
European Urology 40 (3), 2001
Early prediction of neoadjuvant chemotherapy response by exploiting a transfer learning approach on breast DCE-MRIs
MC Comes, A Fanizzi, S Bove, V Didonna, S Diotaiuti, D La Forgia, ...
Scientific Reports 11 (1), 14123, 2021
Breast MRI background parenchymal enhancement as an imaging bridge to molecular cancer sub-type
G Dilorenzo, M Telegrafo, D La Forgia, AAS Ianora, M Moschetta
European Journal of Radiology 113, 148-152, 2019
Ensemble discrete wavelet transform and gray-level co-occurrence matrix for microcalcification cluster classification in digital mammography
A Fanizzi, TM Basile, L Losurdo, R Bellotti, U Bottigli, F Campobasso, ...
Applied Sciences 9 (24), 5388, 2019
MRI in pregnancy and precision medicine: a review from literature
G Gatta, G Di Grezia, V Cuccurullo, C Sardu, F Iovino, R Comune, ...
Journal of personalized medicine 12 (1), 9, 2021
A roadmap towards breast cancer therapies supported by explainable artificial intelligence
N Amoroso, D Pomarico, A Fanizzi, V Didonna, F Giotta, D La Forgia, ...
Applied Sciences 11 (11), 4881, 2021
Pre-menopausal breast fat density might predict MACE during 10 years of follow-up: the BRECARD study
C Sardu, G Gatta, G Pieretti, L Viola, C Sacra, G Di Grezia, L Musto, ...
Cardiovascular Imaging 14 (2), 426-438, 2021
Hough transform for clustered microcalcifications detection in full-field digital mammograms
A Fanizzi, TMA Basile, L Losurdo, N Amoroso, R Bellotti, U Bottigli, ...
Applications of digital image processing XL 10396, 218-229, 2017
Prediction of breast cancer histological outcome by radiomics and artificial intelligence analysis in contrast-enhanced mammography
A Petrillo, R Fusco, E Di Bernardo, T Petrosino, ML Barretta, A Porto, ...
Cancers 14 (9), 2132, 2022
Predicting of sentinel lymph node status in breast cancer patients with clinically negative nodes: A validation study
A Fanizzi, D Pomarico, A Paradiso, S Bove, S Diotaiuti, V Didonna, ...
Cancers 13 (2), 352, 2021
Early prediction of breast cancer recurrence for patients treated with neoadjuvant chemotherapy: a transfer learning approach on DCE-MRIs
MC Comes, D La Forgia, V Didonna, A Fanizzi, F Giotta, A Latorre, ...
Cancers 13 (10), 2298, 2021
A clinical decision support system for predicting invasive breast cancer recurrence: Preliminary results
R Massafra, A Latorre, A Fanizzi, R Bellotti, V Didonna, F Giotta, ...
Frontiers in Oncology 11, 576007, 2021
The system can't perform the operation now. Try again later.
Articles 1–20