Work

Mp06-17 Discriminating Malignant and Benign Solid Renal Masses: a Radiomic Machine Learning Model

Year: 2025

Type: article

Abstract: led to significant improvements in predictive performance as compared to previous human-derived tools.Future work will refine AI-discovered criteria and assess individual domain utility during training.

Source: The Journal of Urology

DOI: 10.1097/01.ju.0001109752.81014.18.17

Authors: Riccardo Mastroianni, Antonino Guerrisi, Umberto Anceschi, Alfredo Maria Bove, Aldo Brassetti, Simone D'Annunzio, Mariaconsiglia C. Ferriero, Rocco Simone Flammia, Salvatore Guaglianone, Costantino Leonardo, Ludovica Miseo, Leonardo Misuraca, Flavia Proietti, Gabriele Tuderti, Alessandro Valenti, Giulio Vallati, Giuseppe Simone

Language: English

Topic: Radiomics and Machine Learning in Medical Imaging

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: bronze

Funders: National Institutes of Health, National Cancer Institute

Award: Automated Assessment for Robotic Suturing Utilizing Deep Learning Algorithms

OpenAlex ID: W4409268256

FWCI:

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