Transarterial chemoembolization outcomes in patients with HCC may be predicted before procedures by combining clinical patient data and baseline MR imaging with the use of AI and ML techniques.
IntroductionManagement of clot in transit in patients with pulmonary embolism, who are candidates for percutaneous intervention, can be challenging. This is a case report of simultaneous right atrial mechanical thrombectomy under echocardiography guidance and pulmonary artery embolectomy under fluoroscopy guidance, using the recently introduced FlowTriever system (Inari Medical Inc., Irvine, CA, USA).ReportAn 88 year old female, resuscitated from cardiopulmonary arrest near the end of a total right hip arthroplasty, presented for management of suspected massive pulmonary embolism. Her right atrial thrombus was removed under transthoracic echocardiography guidance, and her pulmonary arterial thrombus was subsequently successfully treated under fluoroscopy.DiscussionThe FlowTriever system can be safely and effectively used under real time transthoracic echocardiography guidance to retrieve clot in transit from the cardiac chambers, in addition to its standard application for the pulmonary artery under fluoroscopy guidance.
The widespread adoption of electronic health records has resulted in an abundance of imaging and clinical information. New data-processing technologies have the potential to revolutionize the practice of medicine by deriving clinically meaningful insights from large-volume data. Among those techniques is supervised machine learning, the study of computer algorithms that use self-improving models that learn from labeled data to solve problems. One clinical area of application for supervised machine learning is within oncology, where machine learning has been used for cancer diagnosis, staging, and prognostication. This review describes a framework to aid clinicians in understanding and critically evaluating studies applying supervised machine learning methods. Additionally, we describe current studies applying supervised machine learning techniques to the diagnosis, prognostication, and treatment of cancer, with a focus on gastroenterological cancers and other related pathologies.
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