Validation of a deep learning segmentation algorithm to quantify the skeletal muscle index and sarcopenia in metastatic renal carcinoma.

Autor: Roblot V; Department 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., Giret Y; Foodvisor, Paris, France., Mezghani S; Department of Radiology, Hôpital Européen Georges Pompidou, AP-HP, Université de Paris, PARCC UMRS 970, INSERM, 20 Rue Leblanc, 75015, Paris, France., Auclin E; Department of Medical Oncology, Hôpital Européen Georges Pompidou, AP-HP, Université de Paris, INSERM CIC1418-EC Clinical Epidemiology Team, Paris, France., Arnoux A; Informatics and Clinical Research Unit, Department of Biostatistics, Hôpital européen Georges Pompidou, AP-HP, Université de Paris, INSERM CIC1418-EC Clinical Epidemiology Team, Paris, France., Oudard S; Department of Medical Oncology, Hôpital Européen Georges Pompidou, AP-HP, Université de Paris, INSERM CIC1418-EC Clinical Epidemiology Team, Paris, France., Duron L; Department of Radiology, Hôpital Européen Georges Pompidou, AP-HP, Université de Paris, PARCC UMRS 970, INSERM, 20 Rue Leblanc, 75015, Paris, France.; Department of Radiology, Fondation Ophtalmologique Adolphe de Rothschild, Paris, France., Fournier L; Department of Radiology, Hôpital Européen Georges Pompidou, AP-HP, Université de Paris, PARCC UMRS 970, INSERM, 20 Rue Leblanc, 75015, Paris, France.
Jazyk: angličtina
Zdroj: European radiology [Eur Radiol] 2022 Jul; Vol. 32 (7), pp. 4728-4737. Date of Electronic Publication: 2022 Mar 18.
DOI: 10.1007/s00330-022-08579-9
Abstrakt: 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-based evaluations. Segmentations were compared using mean Dice similarity coefficient (DSC), SMI using concordance correlation coefficient (CCC) and Bland-Altman plots. Overall survivals (OS) were compared using log-rank (Kaplan-Meier) and Mann-Whitney tests. Generalizability of the prognostic value was tested in an independent validation population (N = 87).
Results: Differences between two manual segmentations (DSC = 0.91, CCC = 0.98 for areas) or manual vs. automated segmentation (DSC = 0.90, CCC = 0.98 for areas, CCC = 0.97 for SMI) had the same order of magnitude. Bland-Altman plots showed a mean difference of -3.33 cm 2 [95%CI: -15.98, 9.1] between two manual segmentations, and -3.28 cm 2 [95% CI: -14.77, 8.21] for manual vs. automated segmentations. With each method, 20/37 (56%) patients were classified as sarcopenic. Sarcopenic vs. non-sarcopenic groups had statistically different survival curves with median OS of 6.0 vs. 12.5 (p = 0.008) and 6.0 vs. 13.9 (p = 0.014) months respectively for manual and DL methods. In the independent validation population, sarcopenic patients according to DL had a lower OS (10.7 vs. 17.3 months, p = 0.033).
Conclusion: A DL algorithm allowed accurate estimation of SMI compared to manual reference standard. The DL-calculated SMI demonstrated a prognostic value in terms of OS.
Key Points: • A deep learning algorithm allows accurate estimation of skeletal muscle index compared to a manual reference standard with a concordance correlation coefficient of 0.97. • Sarcopenic patients according to SMI thresholds after segmentation by the deep learning algorithm had statistically significantly lower overall survival compared to non-sarcopenic patients.
(© 2022. The Author(s), under exclusive licence to European Society of Radiology.)
Databáze: MEDLINE
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