Caveolin-1 (Cav-1) protein has been documented in several neoplasms with a controversial role in cell proliferation, tumour development and progression. The aim of the present study was to investigate the Cav-1 immunohistochemical expression in human meningiomas. Sixty-two cases, classified as 11 meningothelial (17%), 12 transitional (19%), 5 fibrous (8%), 3 microcystic (5%), 3 secretory (5%), 1 clear cell (2%), 1 chordoid (2%) and 26 (42%) atypical meningiomas, were selected from our pathological files. Clinico-pathological data, including Ki-67 values and survival data were also available. Ten leptomeningeal samples were utilized as normal tissue control. For each case, a polyclonal antibody against Cav-1 was applied and an intensity distribution (ID) score was determined. The Cav-1 immunoexpression was found in 95% of meningiomas with a variable ID score, while only minimal, not uniform, reactivity was noted in non-neoplastic meninges. Of note, higher Cav-1 ID score was significantly correlated with tumour site, Simpson's grade, histological type, higher histologic grade, Ki-67 labelling index > or = 4% and clinical course. Kaplan-Meier curves demonstrated a significantly worse survival in patients with higher Cav-1 ID score, Ki-67 > or = 4% and 2-3 Simpson grade. Multivariate analysis indicated that only Ki-67 was an independent prognostic factor. Increased immunoexpression of the Cav-1 seems to be associated with the biological aggressiveness of meningiomas, reflecting a worse prognosis.
Background The use of innovative methodologies, such as Surgical Data Science (SDS), based on artificial intelligence (AI) could prove to be useful for extracting knowledge from clinical data overcoming limitations inherent in medical registries analysis. The aim of the study is to verify if the application of an AI analysis to our database could develop a model able to predict cardiopulmonary complications in patients submitted to lung resection. Methods We retrospectively analyzed data of patients submitted to lobectomy, bilobectomy, segmentectomy and pneumonectomy (January 2006-December 2018. Fifty preoperative characteristics were used for predicting the occurrence of cardiopulmonary complications. The prediction model was developed by training and testing a machine learning (ML) algorithm (XGBOOST) able to deal with registries characterized by missing data. We calculated the receiver operating characteristic curve, true positive rate (TPR), positive predictive value (PPV) and accuracy of the model.
ResultsWe analyzed 1360 patients (lobectomy: 80.7%, segmentectomy: 11.9%, bilobectomy 3.7%, pneumonectomy: 3.7%) and 23.3% of them experienced cardiopulmonary complications. XGBOOST algorithm generated a model able to predict complications with an area under the curve of 0.75, a TPR of 0.76, a PPV of 0.68. The model's accuracy was 0.70. The algorithm included all the variables in the model regardless of their completeness. Conclusions Using SDS principles in thoracic surgery for the first time, we developed an ML model able to predict cardiopulmonary complications after lung resection based on 50 patient characteristics. The prediction was also possible even in the case of those patients for whom we had incomplete data. This model could improve the process of counseling and the perioperative management of lung resection candidates.
The case of a 35 years old woman affected by endometriosis located inside the spinal canal in the extradural space at the level of the third left lumbar root, and developing through the corresponding foramen into the paraspinal muscles, is presented. The clinical aspect, radiological picture and surgical treatment are described. Pathogenesis is discussed on the basis of the literature. Furthermore it is stressed that only the histopathological examination gave the correct diagnosis and permitted the definitive hormonal treatment. To our best knowledge no comparable case has been published in the literature.
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