Familial cylindromatosis (Brooke-Spiegler syndrome) is a rare autosomal dominant inherited disease characterized by the development of adnexal tumors, mostly cylindromas, but also trichoepitheliomas and spiradenomas. Malignant tumors may occur, usually with the features of a cylindrocarcinoma. The authors describe the case of a 75-year-old woman with the Brooke-Spiegler syndrome who presented with multiple nodules of the scalp, face, and trunk. In 1997 she underwent surgical excision of the entire forehead and scalp with skin grafting. Histologic examination revealed multiple cylindromas, some with areas of spiradenoma and one with an extensive adenomatous component; some trichoepitheliomas were also evident. In 2002, a nodule of the trunk suddenly increased in size and became painful. The lesion was excised and histologic and immunohistochemical evaluation revealed a malignant cutaneous biphasic tumor extending into the subcutis, consisting of a major portion with the features of an adnexal carcinoma and of a minor one of atypical spindle cells. Biphasic malignant skin tumors are rare and only a limited number have been described, none in association with the Brooke-Spiegler syndrome. The authors discuss the morphogenesis of the folliculosebaceous-apocrine unit from which the tumors in this syndrome derive, and the pivotal role of mesenchymal cells in determining the process. Since the Brooke-Spiegler syndrome is characterized by a germline mutation in the CYLD oncosuppressor gene, a biphasic tumor in this setting may represent a true carcinosarcoma.
1) Background: Recently, it has been shown that the extent of resection (EOR) and molecular classification of low-grade gliomas (LGGs) are endowed with prognostic significance. However, a prognostic stratification of patients able to give specific weight to the single parameters able to predict prognosis is still missing. Here, we adopt classic statistics and an artificial intelligence algorithm to define a multiparametric prognostic stratification of grade II glioma patients. (2) Methods: 241 adults who underwent surgery for a supratentorial LGG were included. Clinical, neuroradiological, surgical, histopathological and molecular data were assessed for their ability to predict overall survival (OS), progression-free survival (PFS), and malignant progression-free survival (MPFS). Finally, a decision-tree algorithm was employed to stratify patients. (3) Results: Classic statistics confirmed EOR, pre-operative-and post-operative tumor volumes, Ki67, and the molecular classification as independent predictors of OS, PFS, and MPFS. The decision tree approach provided an algorithm capable of identifying prognostic factors and defining both the cut-off levels and the hierarchy to be used in order to delineate specific prognostic classes with high positive predictive value. Key results were the superior role of EOR on that of molecular class, the importance of second surgery, and the role of different prognostic factors within the three molecular classes. (4) Conclusions: This study proposes a stratification of LGG patients based on the different combinations of clinical, molecular, and imaging data, adopting a supervised non-parametric learning method. If validated in independent case studies, the clinical utility of this innovative stratification approach might be proved.
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