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The application of machine learning in the field of medicine is expanding on an almost daily basis. Data from the healthcare industry typically have high dimensionality but a limited sample size. The learning process can be sped up, system performance can be improved, complexity can be minimised, and the risk of overfitting may be decreased by selecting a smaller subset of relevant features from the high-dimensional data set. The primary objective of this investigation was to diagnose patients with major depressive disorder (MDD) using radiomics features extracted from MR images. In addition, the thesis tries to accomplish the objective by locating a collection of biomarkers that can assist in developing individualised treatment plans. Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC) was the source of the data. Before beginning the classification process, the data set’s dimensionality was decreased by applying a technique known as RENT, which stands for Repeated Elastic Net Technique for Feature Selection. Logistic Regression, Support Vector Machines and Random Forest are three common classifiers utilised in computing the performance of all features and the RENT chosen features predictions. A technique known as principal component analysis (PCA) was used for the analysis of the data. Throughout the splits and the dataset, RENT chose eleven characteristics in all. According to RENT, the rostral middle frontal cortex may be a significant biomarker that can predict people who have MDD. M-MF

Mansoor M Aman,1 Ammar Mahmoud,2 Timothy Deer,3 Dawood Sayed,4 Jonathan M Hagedorn,5 Shane E Brogan,6 Vinita Singh,7 Amitabh Gulati,8 Natalie Strand,9 Jacqueline Weisbein,10 Johnathan H Goree,11 Fangfang Xing,12 Ali Valimahomed,13 Daniel J Pak,14 Antonios El Helou,15 Priyanka Ghosh,16 Krishna Shah,17 Vishal Patel,1 Alexander Escobar,18 Keith Schmidt,19 Jay Shah,20 Vishal Varshney,21 William Rosenberg,22 Sanjeet Narang23 1Department of Anesthesiology, Division of Pain Medicine, Advocate Aurora Health, Oshkosh, WI, USA; 2Department of Anesthesiology, Division of Pain Medicine, Northern Light Health Eastern Maine Medical Center, Bangor, ME, USA; 3The Spine and Nerve Center of the Virginias, Charleston, WV, USA; 4Department of Anesthesiology, Pain and Perioperative Medicine, University of Kansas Medical Center, Kansas City, KS, USA; 5Department of Anesthesiology and Perioperative Medicine, Division of Pain Medicine, Mayo Clinic, Rochester, MN, USA; 6Department of Anesthesiology, Division of Pain Medicine, University of Utah, Salt Lake City, UT, USA; 7Department of Anesthesiology, Division of Pain Medicine, Emory University, Atlanta, GA, USA; 8Department of Anesthesiology and Critical Care, Memorial Sloan Kettering Cancer Center, New York, NY, USA; 9Department of Anesthesiology, Division of Pain Medicine, Mayo Clinic, Phoenix, AZ, USA; 10Department of Anesthesiology, Chronic Pain Division, University of Arkansas for Medical Sciences, Little Rock, AR, USA; 11Interventional Pain Medicine, Napa Valley Orthopedic Medical Group, Napa, CA, USA; 12Swedish Pain Services, Swedish Health Services, Seattle, WA, USA; 13Gramercy Pain Center, Holmdel, NJ, & Advanced Orthopedics Sports Medicine Institute, Freehold, NJ, USA; 14Department of Anesthesiology, Division of Pain Medicine, New York-Presbyterian Hospital/Weill Cornell Medical College, New York, NY, USA; 15Department of Neurosciences, Division of Neurosurgery, The Moncton Hospital, Moncton, NB. Assistant Professor, Department of Surgery, Dalhousie University, Halifax, NS, Canada; 16Remedy Medical Group, San Francisco, CA, USA; 17Assistant Professor of Anesthesiology, Baylor St. Luke’s Medical Center, Baylor College of Medicine, Houston, TX, USA; 18Department of Anesthesiology and Pain Medicine, University of Toledo Medical Center, Toledo, OH, USA; 19AMITA Neurosciences Institute, Comprehensive Pain Management Program, St. Alexius Medical Center, Hoffman Estates, IL, USA; 20SamWell Institute for Pain Management, Colonia, NJ, USA; 21Department of Anesthesia, Providence Healthcare, Vancouver, BC, Canada & Department of Anesthesiology, Pharmacology, Therapeutics, University of British Columbia, Vancouver, BC, Canada; 22Center for the Relief of Pain, Midwest Neurosurgery Associates, Kansas City, Missouri, USA; 23Department of Anesthesiology, Pain and Perioperative Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USACorrespondence: Mansoor M AmanInterventional Pain Medicine, Department of Anesthesiology, Advocate Aurora Health, Oshkosh, WI, 54904, USATel +1 920-456-7715Email aman.mansoorm@gmail.comAbstract: Moderate to severe pain occurs in many cancer patients during their clinical course and may stem from the primary pathology, metastasis, or as treatment side effects. Uncontrolled pain using conservative medical therapy can often lead to patient distress, loss of productivity, shorter life expectancy, longer hospital stays, and increase in healthcare utilization. Various publications shed light on strategies for conservative medical management for cancer pain and a few international publications have reviewed limited interventional data. Our multi-institutional working group was assembled to review and highlight the body of evidence that exists for opioid utilization for cancer pain, adjunct medication such as ketamine and methadone and interventional therapies. We discuss neurolysis via injections, neuromodulation including targeted drug delivery and spinal cord stimulation, vertebral tumor ablation and augmentation, radiotherapy and surgical techniques. In the United States, there is a significant variance in the interventional treatment of cancer pain based on fellowship training. As a first of its kind, this best practices and interventional guideline will offer evidenced-based recommendations for reducing pain and suffering associated with malignancy.Keywords: cancer pain, neurolysis, ketamine, pain pump, intrathecal drug delivery, neuromodulation, radiofrequency, spinal cord stimulation, vertebral augmentation