CT radiomics-based machine learning classification of atypical cartilaginous tumours and appendicular chondrosarcomas.
EBioMedicine
; 68: 103407, 2021 Jun.
Article
em En
| MEDLINE
| ID: mdl-34051442
ABSTRACT
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.Palavras-chave
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Base de dados:
MEDLINE
Assunto principal:
Neoplasias Ósseas
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Interpretação de Imagem Radiográfica Assistida por Computador
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Condrossarcoma
Tipo de estudo:
Observational_studies
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Prognostic_studies
Limite:
Aged
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article