A Novel Fusion of Radiomics and Semantic Features: MRI-Based Machine Learning in Distinguishing Pituitary Cystic Adenomas from Rathke's Cleft Cysts.

Objectives: To evaluate the performances of machine learning using semantic and radiomic features from magnetic resonance imaging data to distinguish cystic pituitary adenomas (CPA) from Rathke's cleft cysts (RCCs). Materials and Methods: The study involved 65 patients diagnosed with either CPA or RCCs. Multiple observers independently assessed the semantic features of the tumors on the magnetic resonance images. Radiomics features were extracted from T2-weighted, T1-weighted, and T1-contrast-enhanced images. Machine learning models, including Support Vector Machines (SVM), Logistic Regression (LR), and Light Gradient Boosting (LGB), were then trained and validated using semantic features only and a combination of semantic and radiomic features. Statistical analyses were carried out to compare the performance of these various models. Results: Machine learning models that combined semantic and radiomic features achieved higher levels of accuracy than models with semantic features only. Models with combined semantic and T2-weighted radiomics features achieved the highest test accuracies (93.8%, 92.3%, and 90.8% for LR, SVM, and LGB, respectively). The SVM model combined semantic features with T2-weighted radiomics features had statistically significantly better performance than semantic features only (p = 0.019). Conclusion: Our study demonstrates the significant potential of machine learning for differentiating CPA from RCCs.

Improving radiology workflow using ChatGPT and artificial intelligence.

Artificial Intelligence is a branch of computer science that aims to create intelligent machines capable of performing tasks that typically require human intelligence. One of the branches of artificial intelligence is natural language processing, which is dedicated to studying the interaction between computers and human language. ChatGPT is a sophisticated natural language processing tool that can understand and respond to complex questions and commands in natural language. Radiology is a vital aspect of modern medicine that involves the use of imaging technologies to diagnose and treat medical conditions artificial intelligence, including ChatGPT, can be integrated into radiology workflows to improve efficiency, accuracy, and patient care. ChatGPT can streamline various radiology workflow steps, including patient registration, scheduling, patient check-in, image acquisition, interpretation, and reporting. While ChatGPT has the potential to transform radiology workflows, there are limitations to the technology that must be addressed, such as the potential for bias in artificial intelligence algorithms and ethical concerns. As technology continues to advance, ChatGPT is likely to become an increasingly important tool in the field of radiology, and in healthcare more broadly.

PHACE Syndrome: A Rare Case.

PHACE syndrome (OMIM 606519) is a rare neurocutaneous vascular disorder, characterized by posterior fossa malformations, large cervicofacial infantile hemangiomas, arterial anomalies, aortic coarctation, cardiac abnormalities, and eye abnormalities. The long-term outcome of PHACE syndrome patients is unclear; however, it seems that they are at risk for childhood stroke. The radiologist has an important role on diagnosis of PHACE syndrome and in the assessment of potential complications. Investigation of infants with segmental craniofacial hemangiomas should include cranial magnetic resonance imaging (MRI) and magnetic resonance angiography (MRA) of the cerebral and cervical arteries. Brain MRI and MRA findings of a 5-year-old female patient with PHACE syndrome are presented.