Ordinal deep learning for pulmonary nodule risk assessment: an external validation study on a novel screening cohort
Year: 2026
Type: dataset
Abstract: This is a dataset for personalized low-dose CT-based lung cancer screening research. The dataset incorporates annotated screening low-dose computed tomography (LDCT) scans for a total of 211 screening participants. There are 67 lung cancer patients, 68 controls with benign pulmonary nodules, 71 controls without nodules and 5 surgically excised false positive lesions. For lung cancer cases, pathological gold standard label confirmed the presence and subtype of tumor.
Authors: Cobo Cano, Miriam, Serrano, Diego, Pasquier, Andrea, Barranco, Jennifer, de Torres, Juan Pablo, Zulueta, Javier J., Echeveste, Jose Ignacio, Ezponda, Ana, Pueyo, Jesus, Argueta Morales, Allan, Sanz-Ortega, Julian, Berto, Juan, Alcaide Ocaña, Ana Belen, Di Frisco, Madeleine, Felgueroso Rodero, Carmen, Campo, Arantza, de la Fuente Añó, Alejandra, Escobar, Ana, Valencia, Karmele, Orive Mauleón, Daniel, Ocon, Maria del Mar, Globacka, Hanna Beata, Fortuño, María-Antonia, Perna, Valerio, Rodríquez, María, Lozano, Maria D, Calvo, Alfonso, Pio, Ruben, Hung, Rayjean, Seijo, Luis, Bastarrika Alemañ, Gorka, Lloret Iglesias, Lara, Montuenga, Luis M
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7202174097