TAMBORRA, PASQUALE

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TAMBORRA, PASQUALE

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A Combined Approach of Multiscale Texture Analysis and Interest Point/Corner Detectors for Microcalcifications Diagnosis

2018-01-01 Losurdo, Liliana; Fanizzi, Annarita; Basile, Teresa M. A.; Bellotti, Roberto; Bottigli, Ubaldo; Dentamaro, Rosalba; Didonna, Vittorio; Fausto, Alfonso; Massafra, Raffaella; Monaco, Alfonso; Moschetta, Marco; Popescu, Ondina; Tamborra, Pasquale; Tangaro, Sabina; La Forgia, Daniele

A machine learning ensemble approach for 5- And 10-year breast cancer invasive disease event classification

2022-01-01 Massafra, R.; Comes, M. C.; Bove, S.; Didonna, V.; Diotaiuti, S.; Giotta, F.; Latorre, A.; La Forgia, D.; Nardone, A.; Pomarico, D.; Ressa, C. M.; Rizzo, A.; Tamborra, P.; Zito, A.; Lorusso, V.; Fanizzi, A.

A ultrasound-based radiomic approach to predict the nodal status in clinically negative breast cancer patients

2022-01-01 Bove, S.; Comes, M. C.; Lorusso, V.; Cristofaro, C.; Didonna, V.; Gatta, G.; Giotta, F.; La Forgia, D.; Latorre, A.; Pastena, M. I.; Petruzzellis, N.; Pomarico, D.; Rinaldi, L.; Tamborra, P.; Zito, A.; Fanizzi, A.; Massafra, R.

Advancement study of CancerMath model as prognostic tools for predicting Sentinel lymph node metastasis in clinically negative T1 breast cancer patients

2021-01-01 Massafra, Raffaella; Pomarico, Domenico; Fanizzi, Annarita; Campobasso, Francesco; Didonna, Vittorio; Latorre, Agnese; Nardone, Annalisa; Pastena, Irene-Maria; Tamborra, Pasquale; Lorusso, Vito; La#Forgia, Daniele

Analyzing breast cancer invasive disease event classification through explainable artificial intelligence

2023-01-01 Massafra, R.; Fanizzi, A.; Amoroso, N.; Bove, S.; Comes, M. C.; Pomarico, D.; Didonna, V.; Diotaiuti, S.; Galati, L.; Giotta, F.; La Forgia, D.; Latorre, A.; Lombardi, A.; Nardone, A.; Pastena, M. I.; Ressa, C. M.; Rinaldi, L.; Tamborra, P.; Zito, A.; Paradiso, A. V.; Bellotti, R.; Lorusso, V.

Decision support systems for the prediction of lymph node involvement in early breast cancer

2021-01-01 Massafra, R.; Pomarico, D.; La Forgia, D.; Bove, S.; Didonna, V.; Latorre, A.; Russo, A. O.; Tamborra, P.; Lorusso, V.; Fanizzi, A.

Machine learning survival models trained on clinical data to identify high risk patients with hormone responsive HER2 negative breast cancer

2023-01-01 Fanizzi, A.; Pomarico, D.; Rizzo, A.; Bove, S.; Comes, M. C.; Didonna, V.; Giotta, F.; La Forgia, D.; Latorre, A.; Pastena, M. I.; Petruzzellis, N.; Rinaldi, L.; Tamborra, P.; Zito, A.; Lorusso, V.; Massafra, R.

Predicting of sentinel lymph node status in breast cancer patients with clinically negative nodes: A validation study

2021-01-01 Fanizzi, A.; Pomarico, D.; Paradiso, A.; Bove, S.; Diotiaiuti, S.; Didonna, V.; Giotta, F.; La Forgia, D.; Latorre, A.; Pastena, M. I.; Tamborra, P.; Zito, A.; Lorusso, V.; Massafra, R.

Radiomic analysis in contrast-enhanced spectral mammography for predicting breast cancer histological outcome

2020-01-01 la Forgia, D.; Fanizzi, A.; Campobasso, F.; Bellotti, R.; Didonna, V.; Lorusso, V.; Moschetta, M.; Massafra, R.; Tamborra, P.; Tangaro, S.; Telegrafo, M.; Pastena, M. I.; Zito, A.

Robustness Evaluation of a Deep Learning Model on Sagittal and Axial Breast DCE‐MRIs to Predict Pathological Complete Response to Neoadjuvant Chemotherapy

2022-01-01 Massafra, R.; Comes, M. C.; Bove, S.; Didonna, V.; Gatta, G.; Giotta, F.; Fanizzi, A.; La Forgia, D.; Latorre, A.; Pastena, M. I.; Pomarico, D.; Rinaldi, L.; Tamborra, P.; Zito, A.; Lorusso, V.; Paradiso, A. V.

Sentinel lymph node metastasis on clinically negative patients: Preliminary results of a machine learning model based on histopathological features

2021-01-01 Fanizzi, A.; Lorusso, V.; Biafora, A.; Bove, S.; Comes, M. C.; Cristofaro, C.; Digennaro, M.; Didonna, V.; La Forgia, D.; Nardone, A.; Pomarico, D.; Tamborra, P.; Zito, A.; Paradiso, A. V.; Massafra, R.

Titolo	Data di pubblicazione	Autore(i)
A Combined Approach of Multiscale Texture Analysis and Interest Point/Corner Detectors for Microcalcifications Diagnosis	1-gen-2018	Losurdo, Liliana; Fanizzi, Annarita; Basile, Teresa M. A.; Bellotti, Roberto; Bottigli, Ubaldo; Dentamaro, Rosalba; Didonna, Vittorio; Fausto, Alfonso; Massafra, Raffaella; Monaco, Alfonso; Moschetta, Marco; Popescu, Ondina; Tamborra, Pasquale; Tangaro, Sabina; La Forgia, Daniele
A machine learning ensemble approach for 5- And 10-year breast cancer invasive disease event classification	1-gen-2022	Massafra, R.; Comes, M. C.; Bove, S.; Didonna, V.; Diotaiuti, S.; Giotta, F.; Latorre, A.; La Forgia, D.; Nardone, A.; Pomarico, D.; Ressa, C. M.; Rizzo, A.; Tamborra, P.; Zito, A.; Lorusso, V.; Fanizzi, A.
A ultrasound-based radiomic approach to predict the nodal status in clinically negative breast cancer patients	1-gen-2022	Bove, S.; Comes, M. C.; Lorusso, V.; Cristofaro, C.; Didonna, V.; Gatta, G.; Giotta, F.; La Forgia, D.; Latorre, A.; Pastena, M. I.; Petruzzellis, N.; Pomarico, D.; Rinaldi, L.; Tamborra, P.; Zito, A.; Fanizzi, A.; Massafra, R.
Advancement study of CancerMath model as prognostic tools for predicting Sentinel lymph node metastasis in clinically negative T1 breast cancer patients	1-gen-2021	Massafra, Raffaella; Pomarico, Domenico; Fanizzi, Annarita; Campobasso, Francesco; Didonna, Vittorio; Latorre, Agnese; Nardone, Annalisa; Pastena, Irene-Maria; Tamborra, Pasquale; Lorusso, Vito; La#Forgia, Daniele
Analyzing breast cancer invasive disease event classification through explainable artificial intelligence	1-gen-2023	Massafra, R.; Fanizzi, A.; Amoroso, N.; Bove, S.; Comes, M. C.; Pomarico, D.; Didonna, V.; Diotaiuti, S.; Galati, L.; Giotta, F.; La Forgia, D.; Latorre, A.; Lombardi, A.; Nardone, A.; Pastena, M. I.; Ressa, C. M.; Rinaldi, L.; Tamborra, P.; Zito, A.; Paradiso, A. V.; Bellotti, R.; Lorusso, V.
Decision support systems for the prediction of lymph node involvement in early breast cancer	1-gen-2021	Massafra, R.; Pomarico, D.; La Forgia, D.; Bove, S.; Didonna, V.; Latorre, A.; Russo, A. O.; Tamborra, P.; Lorusso, V.; Fanizzi, A.
Machine learning survival models trained on clinical data to identify high risk patients with hormone responsive HER2 negative breast cancer	1-gen-2023	Fanizzi, A.; Pomarico, D.; Rizzo, A.; Bove, S.; Comes, M. C.; Didonna, V.; Giotta, F.; La Forgia, D.; Latorre, A.; Pastena, M. I.; Petruzzellis, N.; Rinaldi, L.; Tamborra, P.; Zito, A.; Lorusso, V.; Massafra, R.
Predicting of sentinel lymph node status in breast cancer patients with clinically negative nodes: A validation study	1-gen-2021	Fanizzi, A.; Pomarico, D.; Paradiso, A.; Bove, S.; Diotiaiuti, S.; Didonna, V.; Giotta, F.; La Forgia, D.; Latorre, A.; Pastena, M. I.; Tamborra, P.; Zito, A.; Lorusso, V.; Massafra, R.
Radiomic analysis in contrast-enhanced spectral mammography for predicting breast cancer histological outcome	1-gen-2020	la Forgia, D.; Fanizzi, A.; Campobasso, F.; Bellotti, R.; Didonna, V.; Lorusso, V.; Moschetta, M.; Massafra, R.; Tamborra, P.; Tangaro, S.; Telegrafo, M.; Pastena, M. I.; Zito, A.
Robustness Evaluation of a Deep Learning Model on Sagittal and Axial Breast DCE‐MRIs to Predict Pathological Complete Response to Neoadjuvant Chemotherapy	1-gen-2022	Massafra, R.; Comes, M. C.; Bove, S.; Didonna, V.; Gatta, G.; Giotta, F.; Fanizzi, A.; La Forgia, D.; Latorre, A.; Pastena, M. I.; Pomarico, D.; Rinaldi, L.; Tamborra, P.; Zito, A.; Lorusso, V.; Paradiso, A. V.
Sentinel lymph node metastasis on clinically negative patients: Preliminary results of a machine learning model based on histopathological features	1-gen-2021	Fanizzi, A.; Lorusso, V.; Biafora, A.; Bove, S.; Comes, M. C.; Cristofaro, C.; Digennaro, M.; Didonna, V.; La Forgia, D.; Nardone, A.; Pomarico, D.; Tamborra, P.; Zito, A.; Paradiso, A. V.; Massafra, R.