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BACKGROUND: Alveolar echinococcosis is a rare condition, but living or working in a rural environment is a substantial risk factor. The liver is the organ primarily affected, with additional extrahepatic manifestations in approximately 25% of cases. Primary extrahepatic disease is rare, and isolated cerebral involvement is extremely unusual. OBSERVATIONS: The authors described an illustrative case of isolated cerebral alveolar echinococcosis in an immunocompetent farmer. Magnetic resonance imaging of the brain showed a predominantly cystic lesion with perifocal edema and a "bunch of grapes" appearance in the left frontal lobe. Histology revealed sharply demarcated fragments of a fibrous cyst wall accompanied by marked inflammation and necrosis. Higher magnification showed remnants of protoscolices with hooklets and calcified corpuscles. Immunohistochemistry and polymerase chain reaction (PCR) analysis confirmed the diagnosis of cerebral alveolar echinococcosis. Interestingly, serology and thoracic and abdominal computed tomography results were negative, indicative of an isolated primary extrahepatic manifestation. LESSONS: Isolated, primary central nervous system echinococcosis is extremely rare, with only isolated case reports. As in the authors' case, it can occur in immunocompetent patients, especially persons with a rural vocational history. Negative serology results do not exclude cerebral echinococcosis, which requires histological confirmation. Immunohistochemical staining and PCR analysis are especially useful in cases without classic morphological findings.
RESUMEN
OBJECTIVE: Machine learning (ML) is an innovative method to analyze large and complex data sets. The aim of this study was to evaluate the use of ML to identify predictors of early postsurgical and long-term outcomes in patients treated for Cushing disease (CD). METHODS: All consecutive patients in our center who underwent surgery for CD through the endoscopic endonasal approach were retrospectively reviewed. Study endpoints were gross-tumor removal (GTR), postsurgical remission, and long-term control of disease. Several demographic, radiological, and histological factors were assessed as potential predictors. For ML-based modeling, data were randomly divided into 2 sets with an 80% to 20% ratio for bootstrapped training and testing, respectively. Several algorithms were tested and tuned for the area under the curve (AUC). RESULTS: The study included 151 patients. GTR was achieved in 137 patients (91%), and postsurgical hypersecretion remission was achieved in 133 patients (88%). At last follow-up, 116 patients (77%) were still in remission after surgery and in 21 patients (14%), CD was controlled with complementary treatment (overall, of 131 cases, 87% were under control at follow-up). At internal validation, the endpoints were predicted with AUCs of 0.81-1.00, accuracy of 81%-100%, and Brier scores of 0.035-0.151. Tumor size and invasiveness and histological confirmation of adrenocorticotropic hormone (ACTH)-secreting cells were the main predictors for the 3 endpoints of interest. CONCLUSIONS: ML algorithms were used to train and internally validate robust models for all the endpoints, giving accurate outcome predictions in CD cases. This analytical method seems promising for potentially improving future patient care and counseling; however, careful clinical interpretation of the results remains necessary before any clinical adoption of ML. Moreover, further studies and increased sample sizes are definitely required before the widespread adoption of ML to the study of CD.