Sternoclavicular joint arthropathy mimicking radiculopathy in a patient with concurrent C4-5 disc herniation

Background Patients with sternoclavicular joint arthropathy, which can result from septic arthritis, often present with localized sternoclavicular pain as well as shoulder pain. Such pain may be similar to the presenting symptoms of cervical intervertebral disc herniation. Clinical presentation A 47-year-old female presented with 1 month of significant pain in the neck as well as right anterior chest and deltoid. The patient was found to have reduced strength in the right deltoid muscle on physical examination. MRI revealed a C4-C5 herniated nucleus pulposus. The patient underwent successful C4-C5 anterior cervical discectomy, but subsequently developed painful swelling in the region of the right sternoclavicular joint with limited motor strength in the right shoulder and arm. A needle biopsy of the mass yielded negative results, but her erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) numbers did respond to antibiotics, consistent with infection of the sternoclavicular joint. A follow-up CT scan (6.5 months postoperatively) revealed apparent resolution right sternoclavicular joint arthropathy, thought the patient continued to experience pain. 15 months postoperatively, the patient was prescribed methotrexate due to persistent pain and mild weakness arising from a possible rheumatologic inflammation. 19 months postoperatively, the patient had full strength of the right shoulder and arm and visible decrease in swelling at the sternoclavicular joint. More than three years postoperatively, the patient was diagnosed with multiple myeloma, which was appropriately treated. At follow-up four years postoperatively, the patient had an MRI showing new C6-C7 herniated nucleus pulposus, but no longer had any right shoulder or chest pain or associated weakness. Conclusion This case demonstrates that sternoclavicular joint arthropathy results in symptoms that can mimic the presenting symptoms of shoulder or cervical spine pathology, such as shoulder and neck pain, necessitating careful diagnosis and management

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset