Peer-Reviewed Publication
Radiol Med2025;130(10):1669-1679.October 1, 2025Journal Article
Deep learning-driven incidental detection of vertebral fractures in cancer patients: advancing diagnostic precision and clinical management.
El Mehdi Mniai1,2, Vladimir Laletin3, Lambros Tselikas4,5, Tarek Assi6, Baptiste Bonnet4,5, Astrid Orfali Camez1, Amir Zemmouri1, Serge Muller1, Tania Moussa1, Yasmina Chaibi7, Julie Kiewsky7, Sarah Quenet7, Christophe Avare7, Nathalie Lassau1,8, Corinne Balleyguier1,8, Angela Ayobi7, Samy Ammari1,8
1Department of Radiology, Gustave Roussy, 94805, Villejuif, France.
2Mohammed VI University of Sciences and Health - UM6SS, Casablanca, Morocco.
3Avicenna.AI, 375 Avenue du Mistral, 13600, La Ciotat, France. vladimir.laletin@avicenna.ai.
4Département d'Anesthésie, Chirurgie et Interventionnel (DACI), Service de Radiologie Interventionnelle, Gustave Roussy, 94805, Villejuif, France.
5Centre d'Investigation Clinique BIOTHERIS, INSERM CIC 1428, 94805, Villejuif, France.
6Division of International Patients Care, Gustave Roussy, 94805, Villejuif, France.
7Avicenna.AI, 375 Avenue du Mistral, 13600, La Ciotat, France.
8Laboratoire d'Imagerie Biomédicale Multimodale Paris-Saclay, Inserm, CNRS, CEA, BIOMAPS, UMR 1281, Université Paris-Saclay, 94800, Villejuif, France.
Abstract
PURPOSE: Vertebral compression fractures (VCFs) are the most prevalent skeletal manifestations of osteoporosis in cancer patients. Yet, they are frequently missed or not reported in routine clinical radiology, adversely impacting patient outcomes and quality of life. This study evaluates the diagnostic performance of a deep-learning (DL)-based application and its potential to reduce the miss rate…
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