Extraosseous dural-based primary Ewing’s sarcoma of the central nervous system is a rare tumour posing a diagnostic challenge. On cross-sectional radiological imaging, the lesion has an extra-axial location with heterogeneous appearance. These lesions are usually multicystic with internal haemorrhage causing fluid-haematocrit levels. It mimics conditions like an aneurysmal bone cyst, microcystic meningioma, telangiectatic osteosarcoma or cystic metastasis. Exclusion of primary Ewing’s sarcoma or malignancy elsewhere in the body is required to rule out a secondary. Surgery along with adjuvant chemotherapy and focal radiotherapy is the preferred mode of treatment. Due to the presence of non-specific small round blue cells on H&E stain, these tumours are also confused with lymphoma, osteosarcoma, rhabdomyosarcoma, Merkel cell carcinoma, ependymoblastoma and neuroendocrine carcinoma. Immunohistochemistry provides a definitive diagnosis. A high degree of suspicion in the preoperative scans is crucial for prognostication and early management of this aggressive tumour leading to improved patient survival.

Background LDL-C is a strong risk factor for cardiovascular disorders. The formulas used to calculate LDL-C showed varying performance in different populations. Machine learning models can study complex interactions between the variables and can be used to predict outcomes more accurately. The current study evaluated the predictive performance of three machine learning models—random forests, XGBoost, and support vector Rregression (SVR) to predict LDL-C from total cholesterol, triglyceride, and HDL-C in comparison to linear regression model and some existing formulas for LDL-C calculation, in eastern Indian population. Methods The lipid profiles performed in the clinical biochemistry laboratory of AIIMS Bhubaneswar during 2019–2021, a total of 13,391 samples were included in the study. Laboratory results were collected from the laboratory database. 70% of data were classified as train set and used to develop the three machine learning models and linear regression formula. These models were tested in the rest 30% of the data (test set) for validation. Performance of models was evaluated in comparison to best six existing LDL-C calculating formulas. Results LDL-C predicted by XGBoost and random forests models showed a strong correlation with directly estimated LDL-C (r = 0.98). Two machine learning models performed superior to the six existing and commonly used LDL-C calculating formulas like Friedewald in the study population. When compared in different triglycerides strata also, these two models outperformed the other methods used. Conclusion Machine learning models like XGBoost and random forests can be used to predict LDL-C with more accuracy comparing to conventional linear regression LDL-C formulas.

Background The complications associated with abdominal testis include torsion, rupture, and malignant transformation. Case presentation A 40-year-old man presented with complaints of left-sided abdominal mass and abdominal pain. On contrast-enhanced computed tomography (CECT), there was a well-defined heterogeneously enhancing mass lesion in the lower abdomen with calcification showing fistulous communication to the adjacent ileal loops. The lesion was seen supplied by the left gonadal artery raising suspicion for testicular origin. On performing a scrotal ultrasound, the left testis was found absent. A radiological diagnosis of abdominal testicular neoplasm was made. Conclusions Histopathological examination proved the lesion to be a germ cell tumor, consistent with seminoma.