| 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 | 57 | 2021 |
| 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 | 52 | 2021 |
| 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 | 35 | 2021 |
| A ultrasound-based radiomic approach to predict the nodal status in clinically negative breast cancer patients S Bove, MC Comes, V Lorusso, C Cristofaro, V Didonna, G Gatta, F Giotta, ... Scientific Reports 12 (1), 7914, 2022 | 28 | 2022 |
| Robustness evaluation of a deep learning model on sagittal and axial breast DCE-MRIs to predict pathological complete response to neoadjuvant chemotherapy R Massafra, MC Comes, S Bove, V Didonna, G Gatta, F Giotta, A Fanizzi, ... Journal of Personalized Medicine 12 (6), 953, 2022 | 24 | 2022 |
| A deep learning model based on whole slide images to predict disease-free survival in cutaneous melanoma patients MC Comes, L Fucci, F Mele, S Bove, C Cristofaro, I De Risi, A Fanizzi, ... Scientific Reports 12 (1), 20366, 2022 | 21 | 2022 |
| Analyzing breast cancer invasive disease event classification through explainable artificial intelligence R Massafra, A Fanizzi, N Amoroso, S Bove, MC Comes, D Pomarico, ... Frontiers in Medicine 10, 1116354, 2023 | 18 | 2023 |
| Pathological complete response to neoadjuvant chemoimmunotherapy for early triple-negative breast cancer: an updated meta-analysis A Rizzo, A Cusmai, R Massafra, S Bove, MC Comes, A Fanizzi, L Rinaldi, ... Cells 11 (12), 1857, 2022 | 13 | 2022 |
| A CT-based transfer learning approach to predict NSCLC recurrence: The added-value of peritumoral region S Bove, A Fanizzi, F Fadda, MC Comes, A Catino, A Cirillo, C Cristofaro, ... Plos one 18 (5), e0285188, 2023 | 11 | 2023 |
| A machine learning ensemble approach for 5-and 10-year breast cancer invasive disease event classification R Massafra, MC Comes, S Bove, V Didonna, S Diotaiuti, F Giotta, ... Plos one 17 (9), e0274691, 2022 | 10 | 2022 |
| A proposal of quantum-inspired machine learning for medical purposes: An application case D Pomarico, A Fanizzi, N Amoroso, R Bellotti, A Biafora, S Bove, ... Mathematics 9 (4), 410, 2021 | 10 | 2021 |
| Comparison between vision transformers and convolutional neural networks to predict non-small lung cancer recurrence A Fanizzi, F Fadda, MC Comes, S Bove, A Catino, E Di Benedetto, ... Scientific Reports 13 (1), 20605, 2023 | 9 | 2023 |
| Accurate evaluation of feature contributions for sentinel lymph node status classification in breast cancer A Lombardi, N Amoroso, L Bellantuono, S Bove, MC Comes, A Fanizzi, ... Applied Sciences 12 (14), 7227, 2022 | 8 | 2022 |
| Sentinel lymph node metastasis on clinically negative patients: Preliminary results of a machine learning model based on histopathological features A Fanizzi, V Lorusso, A Biafora, S Bove, MC Comes, C Cristofaro, ... Applied Sciences 11 (21), 10372, 2021 | 7 | 2021 |
| Explainable 3D CNN based on baseline breast DCE-MRI to give an early prediction of pathological complete response to neoadjuvant chemotherapy MC Comes, A Fanizzi, S Bove, V Didonna, S Diotiaiuti, F Fadda, ... Computers in Biology and Medicine 172, 108132, 2024 | 6 | 2024 |
| An invasive disease event-free survival analysis to investigate Ki67 role with respect to breast cancer patients’ age: a retrospective cohort study R Massafra, S Bove, D La Forgia, MC Comes, V Didonna, G Gatta, ... Cancers 14 (9), 2215, 2022 | 6 | 2022 |
| Machine learning survival models trained on clinical data to identify high risk patients with hormone responsive HER2 negative breast cancer A Fanizzi, D Pomarico, A Rizzo, S Bove, MC Comes, V Didonna, F Giotta, ... Scientific Reports 13 (1), 8575, 2023 | 5 | 2023 |
| Explainable prediction model for the human papillomavirus status in patients with oropharyngeal squamous cell carcinoma using CNN on CT images A Fanizzi, MC Comes, S Bove, E Cavalera, P de Franco, A Di Rito, ... Scientific Reports 14 (1), 14276, 2024 | 4 | 2024 |
| Transfer learning approach based on computed tomography images for predicting late xerostomia after radiotherapy in patients with oropharyngeal cancer A Fanizzi, G Scognamillo, A Nestola, S Bambace, S Bove, MC Comes, ... Frontiers in Medicine 9, 993395, 2022 | 4 | 2022 |
| Prognostic power assessment of clinical parameters to predict neoadjuvant response therapy in HER2‐positive breast cancer patients: A machine learning approach A Fanizzi, A Latorre, DA Bavaro, S Bove, MC Comes, EF Di Benedetto, ... Cancer Medicine 12 (22), 20663-20669, 2023 | 3 | 2023 |
Maria Colomba ComesUniversità degli Studi di Roma Tor VergataVerified email at uniroma2.it
