CT radiomics-based machine learning classification of atypical cartilaginous tumours and appendicular chondrosarcomas
Salvatore Gitto,
Renato Cuocolo,
Alessio Annovazzi,
Vincenzo Anelli,
Marzia Acquasanta,
Antonino Cincotta,
Domenico Albano,
Vito Chianca,
Virginia Ferraresi,
Carmelo Messina,
Carmine Zoccali,
Elisabetta Armiraglio,
Antonina Parafioriti,
Rosa Sciuto,
Alessandro Luzzati,
Roberto Biagini,
Massimo Imbriaco,
Luca Maria Sconfienza
Affiliations
Salvatore Gitto
Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milan, Italy; Corresponding author.
Renato Cuocolo
Dipartimento di Medicina Clinica e Chirurgia, Università degli Studi di Napoli “Federico II”, Naples, Italy; Laboratory of Augmented Reality for Health Monitoring (ARHeMLab), Dipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione, Università degli Studi di Napoli “Federico II”, Naples, Italy
Alessio Annovazzi
Nuclear Medicine Unit, IRCCS Regina Elena National Cancer Institute, Rome, Italy
Vincenzo Anelli
Radiology and Diagnostic Imaging Unit, IRCCS Regina Elena National Cancer Institute, Rome, Italy
Marzia Acquasanta
IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
Antonino Cincotta
Scuola di Specializzazione in Radiodiagnostica, Università degli Studi di Milano, Milan, Italy
Domenico Albano
IRCCS Istituto Ortopedico Galeazzi, Milan, Italy; Sezione di Scienze Radiologiche, Dipartimento di Biomedicina, Neuroscienze e Diagnostica Avanzata, Università degli Studi di Palermo, Palermo, Italy
Vito Chianca
Ospedale Evangelico Betania, Naples, Italy; Clinica di Radiologia, Istituto Imaging della Svizzera Italiana - Ente Ospedaliero Cantonale, Lugano, Switzerland
Virginia Ferraresi
First Medical Oncology Unit, IRCCS Regina Elena National Cancer Institute, Rome, Italy
Carmelo Messina
IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
Carmine Zoccali
Oncological Orthopaedics Unit, IRCCS Regina Elena National Cancer Institute, Rome, Italy
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.
