Peer-Reviewed Publication
Eur Radiol2022;32(7):4728-4737.July 1, 2022Journal Article
Validation of a deep learning segmentation algorithm to quantify the skeletal muscle index and sarcopenia in metastatic renal carcinoma.
Victoire Roblot1, Yann Giret2, Sarah Mezghani3, Edouard Auclin4, Armelle Arnoux5, Stéphane Oudard4, Loïc Duron3,6, Laure Fournier3
1Department of Radiology, Hôpital Européen Georges Pompidou, AP-HP, Université de Paris, PARCC UMRS 970, INSERM, 20 Rue Leblanc, 75015, Paris, France. victoire.roblot@hotmail.fr.
2Foodvisor, Paris, France.
3Department of Radiology, Hôpital Européen Georges Pompidou, AP-HP, Université de Paris, PARCC UMRS 970, INSERM, 20 Rue Leblanc, 75015, Paris, France.
4Department of Medical Oncology, Hôpital Européen Georges Pompidou, AP-HP, Université de Paris, INSERM CIC1418-EC Clinical Epidemiology Team, Paris, France.
5Informatics and Clinical Research Unit, Department of Biostatistics, Hôpital européen Georges Pompidou, AP-HP, Université de Paris, INSERM CIC1418-EC Clinical Epidemiology Team, Paris, France.
6Department of Radiology, Fondation Ophtalmologique Adolphe de Rothschild, Paris, France.
Abstract
OBJECTIVES: To validate a deep learning (DL) algorithm for measurement of skeletal muscular index (SMI) and prediction of overall survival in oncology populations.
METHODS: A retrospective single-center observational study included patients with metastatic renal cell carcinoma between 2007 and 2019. A set of 37 patients was used for technical validation of the algorithm, comparing manual vs DL-ba…
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