AI drives the assessment of lung cancer microenvironment composition
We show that employing a machine learning approach in cell population quantification reduces inter- and intra-observer variability, improving reproducibility and facilitating its use in further validation studies.
Authors
- Paola Nisticò (ORCID: https://orcid.org/0000-0003-4409-2261)
- Simona Di Martino (ORCID: https://orcid.org/0000-0003-4533-3776)
- Marialuisa Lavitrano (ORCID: https://orcid.org/0000-0003-4852-1318)
- Sara Baldinelli
- Martina Betti
- Theodora Daralioti
- Paolo Visca (ORCID: https://orcid.org/0000-0001-8484-6991)
- Elisabetta Merenda (ORCID: https://orcid.org/0000-0002-7231-2295)
- Gennaro Ciliberto (ORCID: https://orcid.org/0000-0003-2851-8605)
- Matteo Pallocca (ORCID: https://orcid.org/0000-0002-7756-3579)
- Enzo Gallo (ORCID: https://orcid.org/0000-0002-4938-4071)
- Andrea Ascione (ORCID: https://orcid.org/0000-0002-4253-3193)
- Edoardo Pescarmona (ORCID: https://orcid.org/0000-0002-0443-9331)
- Davide Guardiani
- Brindusa Ana Maria Arteni
Institutions
- Policlinico Umberto I (IT)
- Istituti Fisioterapici Ospitalieri (IT)
- Institute for Experimental Endocrinology and Oncology (IT)
- Istituti di Ricovero e Cura a Carattere Scientifico (IT)
- National Research Council (IT)
- University of Milano-Bicocca (IT)
- Sapienza University of Rome (IT)
Publication Details
- Journal
- Journal of Pathology Informatics
- Published
- 2024-09-30
- DOI
- https://doi.org/10.1016/j.jpi.2024.100400
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.40
Funders
- European Commission
- Ministero della Salute