Radiomic-Based MRI for Classification of Solitary Brain Metastases Subtypes From Primary Lymphoma of the Central Nervous System.

BACKGROUND: Differential diagnosis of brain metastases subtype and primary central nervous system lymphoma (PCNSL) is necessary for treatment decisions. The application of machine learning facilitates the classification of brain tumors, but prior investigations into primary lymphoma and brain metastases subtype classification have been limited. PURPOSE: To develop a machine-learning model to classify PCNSL, brain metastases with primary lung and non-lung origin. STUDY TYPE: Retrospective. POPULATION: A total of 211 subjects with pathologically confirmed PCNSL or brain metastases (training cohort 168 and testing cohort 43). FIELD STRENGTH/SEQUENCE: A 3.0 T axial contrast-enhanced T1-weighted spin-echo inversion recovery sequence (T1WI-CE), axial T2-weighted fluid-attenuation inversion recovery sequence (T2FLAIR) ASSESSMENT: Several machine-learning models (support vector machine, random forest, and K-nearest neighbors) were built with least absolute shrinkage and selection operator (LASSO) using features from T1WI-CE, T2FLAIR, and clinical. The model with the highest performance in the training cohort was selected to differentiate lesions in the testing cohort. Then, three radiologists conducted a two-round classification (with and without model reference) using images and clinical information from testing cohorts. STATISTICAL TESTS: Five-fold cross-validation was used for model evaluation and calibration. Model performance was assessed based on sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC). RESULTS: Twenty-five image features were selected by LASSO analysis. Random forest classifier was selected for its highest performance on the training set with an AUC of 0.73. After calibration, this model achieved an accuracy of 0.70 on the testing set. Accuracies of all three radiologists improved under model reference (0.49 vs. 0.70, 0.60 vs. 0.77, 0.58 vs. 0.72, respectively). DATA CONCLUSION: The random forest model based on conventional MRI and clinical data can diagnose PCNSL and brain metastases subtypes (lung and non-lung origin). Model classification can help foster the diagnostic accuracy of specialists and streamline prognostication workflow. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

Involvement of BDNF/TrkB and ERK/CREB axes in nitroglycerin-induced rat migraine and effects of estrogen on these signals in the migraine.

Migraine is a highly prevalent headache disorder, especially in women. Brain-derived neurotrophic factor (BDNF) and its receptor tropomyosin receptor kinases (TrkB), as well as extracellular signal-regulated kinase (ERK) and its downstream target c-AMP-responsive element binding protein (CREB) are strongly associated with the transmission of nociceptive information. However, the involvement of these substances in migraine has rarely been examined. In the present study, intraperitoneal injection of nitroglycerin (NTC) successfully induced rat migraine attack, as evidenced by behavioral testing. The location and abundance of these substances in the migraine model were determined by immunohistochemistry, real-time polymerase chain reaction (RT-PCR), western blot and enzyme-linked immunosorbant assays (ELISA). Results showed that BDNF, TrkB, phosphor(p)-ERK and p-CREB were up-regulated in the brain neurons of both male and female rats with NTG-induced migraine compared to non-migraine control, whereas their expression levels were decreased in headache-free intervals of the migraine compared to migraine attacks. Estrogen is an important contributor to migraine. Female ovariectomized rats showed significant reduction in the expression of BDNF, TrkB, p-CREB and p-ERK in both attacks and intervals of NTG-induced migraine, relative to rats that have their ovaries. But, intraperitoneal administration of exogenous estrogen recovered their expression in ovariectomized rats. Collectively, this study unveiled a positive correlation of BDNF/TrkB and ERK/CREB axes in NTG-induced migraine and promoting effects of estrogen on their signals in the migraine. These findings contribute to further understanding the pathogenesis of migraine in the molecular basis.