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Plos Computational Biology|March 30, 2021
Apoptosis mapping in space and time of 3D tumor ecosystems reveals transmissibility of cytotoxic cancer deathIrina Veith, Arianna Mencattini, Valentin Picant, et al.
Scientific Reports|September 25, 2020
Accelerating the experimental responses on cell behaviors: a long-term prediction of cell trajectories using Social Generative Adversarial NetworkMaria Colomba Comes, J Filippi, A Mencattini, et al.
Scientific Reports|May 7, 2025
A Radiomic-based model to predict the depth of myometrial invasion in endometrial cancer on ultrasound imagesFrancesca Arezzo, Annarita Fanizzi, Rosanna Mancari, et al.
Expert Review of Clinical Pharmacology|November 13, 2025
Immunotherapy in biliary tract cancer: effective but not enough?Alessandro Rizzo, Enes Erul, Deniz Can Guven, et al.
Cancer Medicine|June 26, 2024
An explainable machine learning model to solid adnexal masses diagnosis based on clinical data and qualitative ultrasound indicatorsAnnarita Fanizzi, Francesca Arezzo, Gennaro Cormio, et al.
International Journal of Environmental Research and Public Health|January 8, 2023
Lean Perspectives in an Organizational Change in a Scientific Direction of an Italian Research Institute: Experience of the Cancer Institute of BariDaniele La Forgia, Gaetano Paparella, Rahel Signorile, et al.
Scientific Reports|July 9, 2021
Early prediction of neoadjuvant chemotherapy response by exploiting a transfer learning approach on breast DCE-MRIsMaria Colomba Comes, Annarita Fanizzi, Samantha Bove, et al.
Frontiers in Artificial Intelligence|December 23, 2024
Explainable machine learning for predicting recurrence-free survival in endometrial carcinosarcoma patientsSamantha Bove, Francesca Arezzo, Gennaro Cormio, et al.
Scientific Reports|May 26, 2023
Machine learning survival models trained on clinical data to identify high risk patients with hormone responsive HER2 negative breast cancerAnnarita Fanizzi, Domenico Pomarico, Alessandro Rizzo, et al.
Cancer Medicine|October 31, 2023
Prognostic power assessment of clinical parameters to predict neoadjuvant response therapy in HER2-positive breast cancer patients: A machine learning approachAnnarita Fanizzi, Agnese Latorre, Domenica Antonia Bavaro, et al.
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