Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/221685
Title: A CT-based deep learning-driven tool for automatic liver tumor detection and delineation in patients with cancer
Author: Balaguer Montero, María
Morales, Adrià Marcos
Ligero Hernández, Marta
Zatse, Christina
Leiva Pedraza, David
Atlagich, Luz M.
Staikoglou, Nikolaos
Viaplana, Cristina
Monreal, Camilo
Mateo Valderrama, Joaquín
Hernando Cubero, Jorge
García Alvárez, Alejandro
Salvà, Francesc
Capdevila, Jaume
Elez, Elena
Dienstmann, Rodrigo
Garralda, Elena
Pérez López, Raquel
Keywords: Càncer de fetge
Aprenentatge profund
Classificació de tumors
Liver cancer
Deep learning (Machine learning)
Tumors classification
Issue Date: 20-Mar-2025
Publisher: Elsevier BV
Abstract: Liver tumors, whether primary or metastatic, significantly impact the outcomes of patients with cancer. Accurate identification and quantification are crucial for effective patient management, including precise diagnosis, prognosis, and therapy evaluation. We present SALSA (system for automatic liver tumor segmentation and detection), a fully automated tool for liver tumor detection and delineation. Developed on 1,598 computed tomography (CT) scans and 4,908 liver tumors, SALSA demonstrates superior accuracy in tumor identification and volume quantification, outperforming state-of-the-art models and inter-reader agreement among expert radiologists. SALSA achieves a patient-wise detection precision of 99.65%, and 81.72% at lesion level, in the external validation cohorts. Additionally, it exhibits good overlap, achieving a dice similarity coefficient (DSC) of 0.760, outperforming both state-of-the-art and the inter-radiologist assessment. SALSA's automatic quantification of tumor volume proves to have prognostic value across various solid tumors (p = 0.028). SALSA's robust capabilities position it as a potential medical device for automatic cancer detection, staging, and response evaluation.
Note: Reproducció del document publicat a: https://doi.org/10.1016/j.xcrm.2025.102032
It is part of: Cell Reports Medicine, 2025, vol. 6, num. 4
URI: https://hdl.handle.net/2445/221685
Related resource: https://doi.org/10.1016/j.xcrm.2025.102032
ISSN: 2666-3791
Appears in Collections:Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL))

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