Tumoral and Peritumoral Radiomics for Preoperative Prediction of Visceral Pleural Invasion in Lung Adenocarcinoma
Filippo Tommaso Gallina, Sonia Lucchese, Antonello Vidiri, Francesca Laganaro, Sergio Ruggiero, Doriana Vergara, Riccardo Tajè, Edoardo Mercadante, Paolo Visca, Simona Marzi, Calvin Sze Hang Ng · 2025
Read Tumoral and Peritumoral Radiomics for Preoperative Prediction of Visceral Pleural Invasion in Lung Adenocarcinoma by Filippo Tommaso Gallina, Sonia Lucchese, Antonello Vidiri, Francesca Laganaro, Sergio Ruggiero, Doriana Vergara, Riccardo Tajè, Edoardo Mercadante, Paolo Visca, Simona Marzi, Calvin Sze Hang Ng (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12731215.txt.
Description
Filippo Gallina and co-authors build, in a 2025 study, a machine learning tool that predicts before surgery whether a lung tumor has breached the lung's inner lining. That breach raises relapse risk and shortens survival, so knowing it early shapes the operation. Scans from 118 patients supplied texture measures from the tumor and from rings of tissue around it.
Two clinical signs, a pleural tag on the scan and the worst tumor pattern, led the predictors. A combined model mixing scan texture with clinical data scored best, with validation accuracy in the low eighties. The authors argue the tissue around the tumor carries readable traces of the breach.
Catalog record
| CREATOR | Filippo Tommaso Gallina, Sonia Lucchese, Antonello Vidiri, Francesca Laganaro, Sergio Ruggiero, Doriana Vergara, Riccardo Tajè, Edoardo Mercadante, Paolo Visca, Simona Marzi, Calvin Sze Hang Ng |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 46 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |