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CT radiomics-based machine learning classification of atypical cartilaginous tumours and appendicular chondrosarcomas

BACKGROUND: Clinical management ranges from surveillance or curettage to wide resection for atypical to higher-grade cartilaginous tumours, respectively. Our aim was to investigate the performance of computed tomography (CT) radiomics-based machine learning for classification of atypical cartilagino...

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Detalles Bibliográficos
Autores principales: Gitto, Salvatore, Cuocolo, Renato, Annovazzi, Alessio, Anelli, Vincenzo, Acquasanta, Marzia, Cincotta, Antonino, Albano, Domenico, Chianca, Vito, Ferraresi, Virginia, Messina, Carmelo, Zoccali, Carmine, Armiraglio, Elisabetta, Parafioriti, Antonina, Sciuto, Rosa, Luzzati, Alessandro, Biagini, Roberto, Imbriaco, Massimo, Sconfienza, Luca Maria
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8170113/
https://www.ncbi.nlm.nih.gov/pubmed/34051442
http://dx.doi.org/10.1016/j.ebiom.2021.103407
Descripción
Sumario:BACKGROUND: Clinical management ranges from surveillance or curettage to wide resection for atypical to higher-grade cartilaginous tumours, respectively. Our aim was to investigate the performance of computed tomography (CT) radiomics-based machine learning for classification of atypical cartilaginous tumours and higher-grade chondrosarcomas of long bones. METHODS: One-hundred-twenty patients with histology-proven lesions were retrospectively included. The training cohort consisted of 84 CT scans from centre 1 (n=55 G1 or atypical cartilaginous tumours; n=29 G2-G4 chondrosarcomas). The external test cohort consisted of the CT component of 36 positron emission tomography-CT scans from centre 2 (n=16 G1 or atypical cartilaginous tumours; n=20 G2-G4 chondrosarcomas). Bidimensional segmentation was performed on preoperative CT. Radiomic features were extracted. After dimensionality reduction and class balancing in centre 1, the performance of a machine-learning classifier (LogitBoost) was assessed on the training cohort using 10-fold cross-validation and on the external test cohort. In centre 2, its performance was compared with preoperative biopsy and an experienced radiologist using McNemar's test. FINDINGS: The classifier had 81% (AUC=0.89) and 75% (AUC=0.78) accuracy in identifying the lesions in the training and external test cohorts, respectively. Specifically, its accuracy in classifying atypical cartilaginous tumours and higher-grade chondrosarcomas was 84% and 78% in the training cohort, and 81% and 70% in the external test cohort, respectively. Preoperative biopsy had 64% (AUC=0.66) accuracy (p=0.29). The radiologist had 81% accuracy (p=0.75). INTERPRETATION: Machine learning showed good accuracy in classifying atypical and higher-grade cartilaginous tumours of long bones based on preoperative CT radiomic features. FUNDING: ESSR Young Researchers Grant.