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.
CT radiomics-based machine learning classification of atypical cartilaginous tumours and appendicular chondrosarcomas / S. Gitto, R. Cuocolo, A. Annovazzi, V. Anelli, M. Acquasanta, A. Cincotta, D. Albano, V. Chianca, V. Ferraresi, C. Messina, C. Zoccali, E. Armiraglio, A. Parafioriti, R. Sciuto, A. Luzzati, R. Biagini, M. Imbriaco, L.M. Sconfienza. - In: EBIOMEDICINE. - ISSN 2352-3964. - 68(2021 Jun).
|Titolo:||CT radiomics-based machine learning classification of atypical cartilaginous tumours and appendicular chondrosarcomas|
GITTO, SALVATORE (Primo) (Corresponding)
SCONFIENZA, LUCA MARIA (Ultimo)
|Parole Chiave:||Artificial intelligence; Chondrosarcoma; Machine learning; Multidetector computed tomography;|
|Settore Scientifico Disciplinare:||Settore MED/36 - Diagnostica per Immagini e Radioterapia|
|Data di pubblicazione:||giu-2021|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1016/j.ebiom.2021.103407|
|Appare nelle tipologie:||01 - Articolo su periodico|