0000000000524693
AUTHOR
Alessandro Luzzati
Accuracy of CT and MRI to assess resection margins in primary malignant bone tumours having histology as the reference standard.
AIM To evaluate the accuracy of magnetic resonance imaging (MRI) and computed tomography (CT) in assessing the resection margins of primary malignant bone tumours. MATERIALS AND METHODS Resected primary malignant bone tumour specimens removed from 46 patients (27 male; mean age: 48±22 years) were imaged using MRI (fat-saturated proton density-weighted and three-dimensional fat-suppressed T1-weighted gradient-recalled-echo) and CT immediately after surgery. A radiologist and an orthopaedist evaluated bone and soft-tissue margins of the specimens on both examinations. Histological evaluation was performed by a senior orthopaedic oncology pathologist. Margins were classified as R0 (safe margin…
Solid bone tumors of the spine: Diagnostic performance of apparent diffusion coefficient measured using diffusion-weighted MRI using histology as a reference standard.
Purpose To assess the diagnostic performance of mean apparent diffusion coefficient (mADC) in differentiating benign from malignant bone spine tumors, using histology as a reference standard. Conventional magnetic resonance imaging (MRI) sequences have good reliability in evaluating spinal bone tumors, although some features of benign and malignant cancers may overlap, making the differential diagnosis challenging. Materials and Methods In all, 116 patients (62 males, 54 females; mean age 59.5 ± 14.1) with biopsy-proven spinal bone tumors were studied. Field strength/sequences: 1.5T MR system; T1-weighted turbo spin-echo (repetition time / echo time [TR/TE], 500/13 msec; number of excitatio…
Radiomic Machine Learning Classifiers in Spine Bone Tumors: A Multi-Software, Multi-Scanner Study
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 la…
MRI radiomics-based machine-learning classification of bone chondrosarcoma.
Abstract 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 bidimensiona…