ABSTRACT
Artificial intelligence (AI) is a rapidly evolving field with many neuro-oncology applications. In this review, we discuss how AI can assist in brain tumour imaging, focusing on machine learning (ML) and deep learning (DL) techniques. We describe how AI can help in lesion detection, differential diagnosis, anatomic segmentation, molecular marker identification, prognostication, and pseudo-progression evaluation. We also cover AI applications in non-glioma brain tumours, such as brain metastasis, posterior fossa, and pituitary tumours. We highlight the challenges and limitations of AI implementation in radiology, such as data quality, standardization, and integration. Based on the findings in the aforementioned areas, we conclude that AI can potentially improve the diagnosis and treatment of brain tumours and provide a path towards personalized medicine and better patient outcomes.
Subject(s)
Artificial Intelligence , Brain Neoplasms , Humans , Brain Neoplasms/diagnostic imaging , Image Interpretation, Computer-Assisted/methods , Neuroimaging/methods , Machine Learning , Diagnosis, DifferentialABSTRACT
A 71-year-old Indian female presented with a 3-month history of weight loss and fatigue. Further review confirmed a histological diagnosis of diffuse large B-cell lymphoma. Although bone marrow analysis did not reveal hemophagocytosis, she had some clinical and laboratory pointers to hemophagocytic lymphohistiocytosis (HLH). Her clinical state deteriorated rapidly with development of acute respiratory distress syndrome, diffuse alveolar hemorrhage, and subsequently death.
