MRI radiomics-based machine-learning classification of bone chondrosarcoma

Salvatore Gitto, Renato Cuocolo, Domenico Albano, Vito Chianca, Carmelo Messina, Angelo Gambino, Lorenzo Ugga, Maria Cristina Cortese, Angelo Lazzara, Domenico Ricci, Riccardo Spairani, Edoardo Zanchetta, Alessandro Luzzati, Arturo Brunetti, Antonina Parafioriti, Luca Maria Sconfienza

Research output: Contribution to journalArticlepeer-review


Purpose: To evaluate the diagnostic performance of machine learning for discrimination between low-grade and high-grade cartilaginous bone tumors based on radiomic parameters extracted from unenhanced magnetic resonance imaging (MRI). Methods: We retrospectively enrolled 58 patients with histologically-proven low-grade/atypical cartilaginous tumor of the appendicular skeleton (n = 26) or higher-grade chondrosarcoma (n = 32, including 16 appendicular and 16 axial lesions). They were randomly divided into training (n = 42) and test (n = 16) groups for model tuning and testing, respectively. All tumors were manually segmented on T1-weighted and T2-weighted images by drawing bidimensional regions of interest, which were used for first order and texture feature extraction. A Random Forest wrapper was employed for feature selection. The resulting dataset was used to train a locally weighted ensemble classifier (AdaboostM1). Its performance was assessed via 10-fold cross-validation on the training data and then on the previously unseen test set. Thereafter, an experienced musculoskeletal radiologist blinded to histological and radiomic data qualitatively evaluated the cartilaginous tumors in the test group. Results: After feature selection, the dataset was reduced to 4 features extracted from T1-weighted images. AdaboostM1 correctly classified 85.7 % and 75 % of the lesions in the training and test groups, respectively. The corresponding areas under the receiver operating characteristic curve were 0.85 and 0.78. The radiologist correctly graded 81.3 % of the lesions. There was no significant difference in performance between the radiologist and machine learning classifier (P = 0.453). Conclusions: Our machine learning approach showed good diagnostic performance for classification of low-to-high grade cartilaginous bone tumors and could prove a valuable aid in preoperative tumor characterization.

Original languageEnglish
Article number109043
JournalEuropean Journal of Radiology
Publication statusPublished - Jul 2020


  • Artificial intelligence
  • Cartilaginous tumor
  • Machine learning
  • Radiomics
  • Texture analysis

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging


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