Roberto Altieri^1,2,*, Francesco Certo¹, Marco Garozzo¹, Giacomo Cammarata¹, Massimiliano Maione¹, Giuseppa Fiumanò³, Giuseppe Broggi⁴, Giada Maria Vecchio⁴, Rosario Caltabiano⁴, Gaetano Magro⁴, Giuseppe Barbagallo¹

Oncologie, Vol.24, No.4, pp. 693-706, 2022, DOI:10.32604/oncologie.2022.025393

Abstract Background: Intracranial lymphoma remains a challenging differential diagnosis in daily neurosurgical practice. We analyzed our early experience with a surgical series of frameless neuronavigated biopsies in Primary CNS Lymphomas (PCNSLs), highlighting the importance of using an intraoperative combined imaging protocol (5-ALA fluorescence, i-CT and ¹¹C-MET-PET) to overcome potential targeting errors secondary to tumor volume reduction after corticosteroid therapy. Materials and Methods: All patients treated for PCNLSs at our center in a 24-month period (1/1/2019 to 31/12/2020) were analyzed. Our cohort included 6 patients (4 males), with a median age of 67 years (59–82). A total of 45 samples were evaluated… More >

Schahrazad Soltane^*, Sameer Alsharif , Salwa M.Serag Eldin

Computer Systems Science and Engineering, Vol.40, No.2, pp. 629-644, 2022, DOI:10.32604/csse.2022.019333

Abstract Current cancer diagnosis procedure requires expert knowledge and is time-consuming, which raises the need to build an accurate diagnosis support system for lymphoma identification and classification. Many studies have shown promising results using Machine Learning and, recently, Deep Learning to detect malignancy in cancer cells. However, the diversity and complexity of the morphological structure of lymphoma make it a challenging classification problem. In literature, many attempts were made to classify up to four simple types of lymphoma. This paper presents an approach using a reliable model capable of diagnosing seven different categories of rare and aggressive lymphoma. These Lymphoma types… More >