Radiomic Machine Learning Classifiers in Spine Bone Tumors: A Multi-Software, Multi-Scanner Study. Issue 137 (April 2021)

• Record Type: Journal Article
• Authors: Chianca, Vito; Cuocolo, Renato; Gitto, Salvatore; Albano, Domenico; Merli, Ilaria; Badalyan, Julietta; Cortese, Maria Cristina; Messina, Carmelo; Luzzati, Alessandro; Parafioriti, Antonina; Galbusera, Fabio; Brunetti, Arturo; Sconfienza, Luca Maria
• Is Part Of: European journal of radiology. Issue 137(2021)
• Publication Date: 2021-04
• Subjects: 0R ZeroRules algorithm -- D-ANN Deep Artificial Neural Network -- GLCM gray-level co-occurrence matrix -- GLDM gray-level dependence matrix -- GLRLM gray-level run length matrix -- GLSZM gray-level size zone matrix -- hCAD heterogeneityCAD module in 3DSlicer -- IOLB iteratively optimized Logit Boost Decision Stump Tree -- ML machine learning -- MRI magnetic resonance imaging -- NEX number of excitations -- NGTDM neighboring gray tone difference matrix -- PBT primary bone tumors -- ROI regions of interest -- TA texture analysis -- TE echo time -- TR repetition time; Magnetic Resonance Imaging -- Artificial Intelligence -- Radiomics -- Spine -- Neoplasms; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2021.109586
• Languages: English
• ISSNs: 0720-048X

Abstract

Highlights: Radiomics-based machine learning (ML) is promising for spine lesion classification. ML was up to 94% accurate in distinguishing benign from malignant entities. The best ML models were comparable to an experienced musculoskeletal radiologist. Image pre-processing allowed for more stable performance across test datasets. Abstract: Purpose: Spinal lesion differential diagnosis remains challenging even in MRI. Radiomics and machine learning (ML) have proven useful even in absence of a standardized data mining pipeline. We aimed to assess ML diagnostic performance in spinal lesion differential diagnosis, employing radiomic data extracted by different software. Methods: Patients undergoing MRI for a vertebral lesion were retrospectively analyzed (n = 146, 67 males, 79 females; mean age 63 ± 16 years, range 8-89 years) and constituted the train (n = 100) and internal test cohorts (n = 46). Part of the latter had additional prior exams which constituted a multi-scanner, external test cohort (n = 35). Lesions were labeled as benign or malignant (2-label classification), and benign, primary malignant or metastases (3-label classification) for classification analyses. Features extracted via 3D Slicer heterogeneityCAD module (hCAD) and PyRadiomics were independently used to compare different combinations of feature selection methods and ML classifiers (n = 19). Results: In total, 90 and 1548 features were extracted by hCAD and PyRadiomics, respectively. The best feature selection method-ML algorithm combination was selected by 10 iterations of 10-fold cross-validation in the training data. For the 2-label classification ML obtained 94% accuracy in the internal test cohort, using hCAD data, and 86% in the external one. For the 3-label classification, PyRadiomics data allowed for 80% and 69% accuracy in the internal and external test sets, respectively. Conclusions: MRI radiomics combined with ML may be useful in spinal lesion assessment. More robust pre-processing led to better consistency despite scanner and protocol heterogeneity.