X-rays radiomics-based machine learning classification of atypical cartilaginous tumour and high-grade chondrosarcoma of long bones

BACKGROUND

Atypical cartilaginous tumour (ACT) and high-grade chondrosarcoma (CS) of long bones are respectively managed with active surveillance or curettage and wide resection. Our aim was to determine diagnostic performance of X-rays radiomics-based machine learning for classification of ACT and high-grade CS of long bones.

METHODS

This retrospective, IRB-approved study included 150 patients with surgically treated and histology-proven lesions at two tertiary bone sarcoma centres. At centre 1, the dataset was split into training (n = 71 ACT, n = 24 high-grade CS) and internal test (n = 19 ACT, n = 6 high-grade CS) cohorts, respectively, based on the date of surgery. At centre 2, the dataset constituted the external test cohort (n = 12 ACT, n = 18 high-grade CS). Manual segmentation was performed on frontal view X-rays, using MRI or CT for preliminary identification of lesion margins. After image pre-processing, radiomic features were extracted. Dimensionality reduction included stability, coefficient of variation, and mutual information analyses. In the training cohort, after class balancing, a machine learning classifier (Support Vector Machine) was automatically tuned using nested 10-fold cross-validation. Then, it was tested on both the test cohorts and compared to two musculoskeletal radiologists' performance using McNemar's test.

FINDINGS

Five radiomic features (3 morphology, 2 texture) passed dimensionality reduction. After tuning on the training cohort (AUC = 0.75), the classifier had 80%, 83%, 79% and 80%, 89%, 67% accuracy, sensitivity, and specificity in the internal (temporally independent) and external (geographically independent) test cohorts, respectively, with no difference compared to the radiologists (p ≥ 0.617).

INTERPRETATION

X-rays radiomics-based machine learning accurately differentiates between ACT and high-grade CS of long bones.

FUNDING

AIRC Investigator Grant.

Ultrasonographic evaluation of the median nerve: normal and variant anatomy and appearance

The median nerve is a mixed sensory and motor nerve that innervates part of the flexor muscles in the anterior compartment of the forearm and muscles in the lateral part of the hand; palmar cutaneous and digital cutaneous nerves branch from the median nerve, which provides sensory innervation to the skin on the radial side of the palm. Also, the median nerve is an object of interest because neuropathy of the median nerve at the level of the carpal tunnel is the most common entrapment neuropathy which increases dramatically in patients with diabetes. Neuromuscular ultrasound provides extensive diagnostic information and has proved itself as a useful complementary test to electrodiagnostic examinations in cases involving median nerve neuropathy. It often happens that the cause of nerve entrapment and neuropathy are variants of several anatomical structures along the course of the median nerve. It is important to be aware and report such anatomical variations of the median nerve in order to avoid damaging the nerve during surgical treatment. Despite the frequently documented abnormalities in the pathway of the brachial plexus and the median nerve, the anatomical variations are unusual to see and are rarely reported. Moreover, there are variations that do not fit under any of the classifications described in the literature.