Detection of Early-stage Primary Central Nervous System Lymphoma Manifesting Atypical Radiological Phenotype Using Multi-Task Neural Network

Motivation: In clinical practice, distinguishing atypical radiological features of primary central nervous system lymphoma (PCNSL) from certain glioma or demyelinating disease patients is challenging and often lead to delayed or incorrect treatment. Goal(s): To develop deep learning model to identify PCNSL with atypical radiological features. Approach: Developing a multi-task, multi-modal deep learning model capable of end-to-end identification of early-stage atypical PCNSL. Results: The multi-task, multi-modal deep learning model accurately discriminates early-stage atypical PCNSL from other radiologically similar diseases, significantly enhancing the diagnostic accuracy of radiologists in clinical practice. Impact: The practical clinical application of the model demonstrates its diagnostic value in identifying challenging cases suspected of early-stage atypical PCNSL. This research shifts academic attention towards distinguishing specific subtypes prone to misdiagnosis, rather than solely focusing on disease-level differentiations.