Tumeurs nasosinusiennes à translocation

International audienceIn recent years, several nasal cavity and sinus entities have been described with fusion genes. Salivary gland tumors with fusion genes will not be discussed in this article, but it should be kept in mind that accessory salivary glands are present in the nasal cavity and sinuses and can therefore lead to tumoral lesions. Entities with specific or more frequently described rearrangements in the nasal cavities and sinuses are DEK::AFF2 squamous cell carcinomas,;non-intestinal and non-salivary nasosinusal adenocarcinomas, some of which displaying ETV6 gene rearrangements; biphenotypic nasosinusal sarcomas, most of which displaying PAX3 gene rearrangements; and Ewing's adamantinoma-like sarcomas, which display the same rearrangements as conventional Ewing's sarcomas, mainly the EWSR1::FLI1 rearrangement. Each entity will be described morphologically, immunohistochemically, and prognostically

SCCOHT/tumeur rhabdoïde ovarienne : à propos d’un cas

International audienceWe report the case of a 22-year-old patient with acute abdominopelvic pain. The diagnosis of hypercalcemic small cell carcinoma (SCCOHT)/ovarian rhabdoid tumor has been made. Small cell carcinoma of hypercalcemic type is a rare and aggressive tumor that occurs in young women. The diagnosis of this tumor and the management must be rapid in view of its aggressiveness. Through this observation, we specify the epidemiological, diagnostic, molecular aspects and discussions about its name

Adult sinonasal soft tissue sarcoma: Analysis of 48 cases from the French Sarcoma Group database

International audienceOBJECTIVE:The aim of this study was to determine the frequency of primary sinonasal adult sarcoma, identify histological subtypes, and analyze prognostic factors.STUDY DESIGN:Retrospective review.METHOD:Forty-eight adult sinonasal sarcomas included in the French Sarcoma Group database (Conticabase) were reviewed.RESULTS:The most frequent tumor types were alveolar rhabdomyosarcoma (33.3%), embryonal rhabdomyosarcoma (14,6%), unclassified sarcoma (14.6%), and leiomyosarcoma (12.5%). All round cell tumors were rhabdomyosarcomas. The 5-year overall survival (OS), metastasis-free survival (MFS), and local recurrence-free survival (LRFS) rates were 62.3%, 73%, and 88.8%, respectively. Histotype was a prognostic factor for OS, MFS, and LRFS, with the worst prognosis associated with rhabdomyosarcomas, regardless of the subtype. The tumor grade influenced the OS and MFS. Surgery was a predictive factor for a complete response.CONCLUSIONS:These results suggest that sinonasal tract should be considered as an unfavorable site for rhabdomyosarcoma. Moreover, surgery should always be considered in treatment

Prediction of Treatment Response in Triple Negative Breast Cancer From Whole Slide Images

International audienceThe automatic analysis of stained histological sections is becoming increasingly popular. Deep Learning is today the method of choice for the computational analysis of such data, and has shown spectacular results for large datasets for a large variety of cancer types and prediction tasks. On the other hand, many scientific questions relate to small, highly specific cohorts. Such cohorts pose serious challenges for Deep Learning, typically trained on large datasets. In this article, we propose a modification of the standard nested crossvalidation procedure for hyperparameter tuning and model selection, dedicated to the analysis of small cohorts. We also propose a new architecture for the particularly challenging question of treatment prediction, and apply this workflow to the prediction of response to neoadjuvant chemotherapy for Triple Negative Breast Cancer

Prediction of Treatment Response in Triple Negative Breast Cancer From Whole Slide Images

International audienceThe automatic analysis of stained histological sections is becoming increasingly popular. Deep Learning is today the method of choice for the computational analysis of such data, and has shown spectacular results for large datasets for a large variety of cancer types and prediction tasks. On the other hand, many scientific questions relate to small, highly specific cohorts. Such cohorts pose serious challenges for Deep Learning, typically trained on large datasets. In this article, we propose a modification of the standard nested crossvalidation procedure for hyperparameter tuning and model selection, dedicated to the analysis of small cohorts. We also propose a new architecture for the particularly challenging question of treatment prediction, and apply this workflow to the prediction of response to neoadjuvant chemotherapy for Triple Negative Breast Cancer

Pan-TRK Immunohistochemistry Is Highly Correlated With NTRK3 Gene Rearrangements in Salivary Gland Tumors

International audienc

Pan-TRK Immunohistochemistry Is Highly Correlated With NTRK3 Gene Rearrangements in Salivary Gland Tumors

International audienc