Imaging diagnosis and differential diagnosis of extraskeletal osteosarcoma.

OBJECTIVE: The aim of this study was to investigate the clinical, imaging and pathological features of extraskeletal osteosarcoma (EOS) and to improve the understanding of this disease and other similar lesions. METHODS: The data for 11 patients with pathologically confirmed extraosseous osteosarcoma, including tumour site and size and imaging and clinical manifestations, were analysed retrospectively. RESULTS: Six patients were male (60%), and 5 were female (40%); patient age ranged from 23 to 76 years (average age 47.1 years). Among the 11 patients, 7 had clear calcifications or ossification with different morphologies, and 2 patients showed a massive mature bone tumour. MRI showed a mixed-signal mass with slightly longer T1 and T2 signals in the tumour parenchyma. Enhanced CT and MRI scans showed enhancement in the parenchyma. Ten patients had different degrees of necrosis and cystic degeneration in the mass, 2 of whom were complicated with haemorrhage, and MRI showed "fluid‒fluid level" signs. Of the 11 patients, five patients survived after surgery, and no obvious recurrence or metastasis was found on imaging examination. One patient died of lung metastasis after surgery, and 2 patients with open biopsy died of disease progression. One patient died of respiratory failure 2 months after operation. 2 patients had positive surgical margins, and 1 had lung metastasis 6 months after operation and died 19 months after operation. Another patient had recurrence 2 months after surgery. CONCLUSION: The diagnosis of EOS requires a combination of clinical, imaging and histological examinations. Cystic degeneration and necrosis; mineralization is common, especially thick and lumpy mineralization. Extended resection is still the first choice for localized lesions. For patients with positive surgical margins or metastases, adjuvant chemoradiotherapy is needed.

Machine learning for differentiation of lipid-poor adrenal adenoma and subclinical pheochromocytoma based on multiphase CT imaging radiomics.

BACKGROUND: There is a paucity of research investigating the application of machine learning techniques for distinguishing between lipid-poor adrenal adenoma (LPA) and subclinical pheochromocytoma (sPHEO) based on radiomic features extracted from non-contrast and dynamic contrast-enhanced computed tomography (CT) scans of the abdomen. METHODS: We conducted a retrospective analysis of multiphase spiral CT scans, including non-contrast, arterial, venous, and delayed phases, as well as thin- and thick-thickness images from 134 patients with surgically and pathologically confirmed. A total of 52 patients with LPA and 44 patients with sPHEO were randomly assigned to training/testing sets in a 7:3 ratio. Additionally, a validation set was comprised of 22 LPA cases and 16 sPHEO cases from two other hospitals. We used 3D Slicer and PyRadiomics to segment tumors and extract radiomic features, respectively. We then applied T-test and least absolute shrinkage and selection operator (LASSO) to select features. Six binary classifiers, including K-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP), were employed to differentiate LPA from sPHEO. Receiver operating characteristic (ROC) curves and area under the curve (AUC) values were compared using DeLong's method. RESULTS: All six classifiers showed good diagnostic performance for each phase and slice thickness, as well as for the entire CT data, with AUC values ranging from 0.706 to 1. Non-contrast CT densities of LPA were significantly lower than those of sPHEO (P < 0.001). However, using the optimal threshold for non-contrast CT density, sensitivity was only 0.743, specificity 0.744, and AUC 0.828. Delayed phase CT density yielded a sensitivity of 0.971, specificity of 0.641, and AUC of 0.814. In radiomics, AUC values for the testing set using non-contrast CT images were: KNN 0.919, LR 0.979, DT 0.835, RF 0.967, SVM 0.979, and MLP 0.981. In the validation set, AUC values were: KNN 0.891, LR 0.974, DT 0.891, RF 0.964, SVM 0.949, and MLP 0.979. CONCLUSIONS: The machine learning model based on CT radiomics can accurately differentiate LPA from sPHEO, even using non-contrast CT data alone, making contrast-enhanced CT unnecessary for diagnosing LPA and sPHEO.

Spectrum of SLC20A2, PDGFRB, PDGFB, and XPR1 mutations in a large cohort of patients with primary familial brain calcification.

Primary familial brain calcification (PFBC) is a rare neurodegenerative disorder with four causative genes (SLC20A2, PDGFRB, PDGFB, and XPR1) that have been identified. Here, we aim to describe the mutational spectrum of four causative genes in a series of 226 unrelated Chinese PFBC patients. Mutations in four causative genes were detected in 16.8% (38/226) of PFBC patients. SLC20A2 mutations accounted for 14.2% (32/226) of all patients. Mutations in the other three genes were relatively rare, accounting for 0.9% (2/226) of all patients, respectively. Clinically, 44.8% of genetically confirmed patients (probands and relatives) were considered symptomatic. The most frequent symptoms were chronic headache, followed by movement disorders and vertigo. Moreover, the total calcification score was significantly higher in the symptomatic group compared to the asymptomatic group. Functionally, we observed impaired phosphate transport induced by seven novel missense mutations in SLC20A2 and two novel mutations in XPR1. The mutation p.D164Y in XPR1 might result in low protein expression through an enhanced proteasome pathway. In conclusion, our study further confirms that mutations in SLC20A2 are the major cause of PFBC and provides additional evidence for the crucial roles of phosphate transport impairment in the pathogenies of PFBC.