Recent advances in technology and artificial intelligence have transformed radiology. Artificial intelligence, which uses machine learning, deep learning algorithms, and cellular neural networks, is now applicable to image analysis, enabling the analysis of large volumes of data and the early identification of lesion features, such as tumors. Integrating artificial intelligence into clinical workflows increases the efficiency of medical report generation and enables efficient prioritization of complex or urgent cases. Predictive analytics based on artificial intelligence uses extensive patient data, including demographics, medical history, tests, and imaging, to build more accurate predictive models. Among the areas where artificial intelligence is successfully implemented in radiology is the automated detection of lung nodules. In the field of neurology, artificial intelligence helps track progressive lesions through volumetric analysis of the brain and surveillance of demyelinating diseases. In oncologic radiology, artificial intelligence is used for automated image segmentation, lesion review, and standardized report generation. Therefore, in this ever-evolving landscape, radiologists must embrace emerging technological advances in order to occupy a unique position at the intersection of different diagnostic fields.
Gliosarcoma is a rare and aggressive variant of glioblastoma, characterized by both a malignant glial component and a mesenchymal sarcomatous component. Gliosarcomas have genetic alterations with glioblastomas, including TP53, PTEN mutations, and EGFR amplification, but may also exhibit additional changes related to epithelial-mesenchymal transition pathways. Management is similar to glioblastoma, involving safe, maximal surgical resection followed by radiotherapy and chemotherapy with temozolomide, but the prognosis remains poor, with a median survival of 6-14 months. Both Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) play crucial roles in the detection, characterization, and treatment planning of gliosarcoma. While MRI is the gold standard, CT remains valuable in certain situations. Light microscopy is essential for definitive diagnosis, allowing pathologists to examine cellular morphology and tissue architecture. It is essential for distinguishing gliosarcoma from other high- grade gliomas and for guiding treatment decisions. In addition, two-photon excited fluorescence (TPEF) microscopy is an advanced optical imaging technique that enables real-time, high-resolution visualization of tumor tissue without the need for staining or contrast agents and enhances visualization of collagen structure and vascularization, key factors in gliosarcoma assessment. The study of radiological and histopathological (light microscopy) features in primary gliosarcomas of the brain is a priority to achieve an early diagnosis that can be translated into better outcomes. Here, we describe the radiological and histopathological features observed in multiple cases of gliosarcoma in current practice.
