Transfer learning approach in pre-treatment CT images to predict therapeutic response in advanced malignant pleural mesothelioma
The proposed model has shown high performance even by varying the reference slices. Novel tools could help to improve the prognostic assessment of patients with MPM and to better identify subgroups of patients with different therapeutic responsiveness.
Authors
- Maria Colomba Comes (ORCID: https://orcid.org/0000-0003-2903-4389)
- Domenico Galetta (ORCID: https://orcid.org/0000-0002-4878-5906)
- Annarita Fanizzi (ORCID: https://orcid.org/0000-0002-2729-9896)
- Pia Perrotti
- Pamela Pizzutilo
- Michele Montrone (ORCID: https://orcid.org/0000-0002-8568-7723)
- Raffaella Massafra (ORCID: https://orcid.org/0000-0001-8588-261X)
- Samantha Bove (ORCID: https://orcid.org/0000-0001-7772-7513)
- Annamaria Catino (ORCID: https://orcid.org/0000-0001-7786-7970)
- Rahel Signorile (ORCID: https://orcid.org/0000-0002-0430-7865)
- Angela Sicolo
Institutions
- Istituto Tumori Bari (IT)
- Istituti di Ricovero e Cura a Carattere Scientifico (IT)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2024-09-16
- DOI
- https://doi.org/10.3389/fonc.2024.1432188
- Citations
- 2
- Primary Topic
- Occupational and environmental lung diseases
- Type
- article
- Field-Weighted Citation Impact
- 0.84
Funders
- Ministry of Health
- Ministero della Salute