Machine Learning Tools for Image-Based Glioma Grading and the Quality of Their Reporting: Challenges and Opportunities mdpi.com/1648502 #mdpicancers via @Cancers_MDPI @YaleRadiology 
@MerkajSara @Ryan_Bahar are an outstanding team that wrote this exciting review on application of #ML to the study of #glioma imaging. After reviewing over 12,000 articles and extracting information from over 80 articles they published their systematic review in @FrontOncology.
But, there was so much more that needed to be discussed, so @MerkajSara & @Ryan_Bahar wrote an expert review of literature with focus on future directions. @YaleMed #Neurosurgery This review is a must read when starting out in #ML & #glioma imaging #Radiomics overview
Discussion of workflows for feature extraction and classification @Ryan_Bahar @MerkajSara @YaleMed @YNeurosurgery #Neurosurgery
Critical components that address challenges in clinical implementation of algorithms. Very useful for future #ML papers that evaluate imaging of gliomas. @Ryan_Bahar @MerkajSara @YaleMed #Neurosurgery
Comprehensive overview of major reporting guidelines and bias assessment tools @Ryan_Bahar @MerkajSara @YaleMed @YNeurosurgery #Neurosurgery To editors of journals - implementation of these tools into review process will improve quality of published work!
Future directions for the #ML #Glioma field with focus on #OpenScience #DataSharing #FederatedLearning and Development of tools that allow annotation of images in clinical practice, such as #PACS based tools we are developing in our lab. @MerkajSara @Ryan_Bahar @YaleMed
@Ryan_Bahar & @MerkajSara showed to me what a true cohesive team works like. They support each other & their lab, collaborate, & share info. They are brilliant and are the hardest working #MedicalStudents I have ever met! Future of #Radiology and #Neurosurgery is very bright!
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