Search Results - "Subramanian, Harry"

ERK signaling couples nutrient status to antiviral defense in the insect gut by Xu, Jie, Hopkins, Kaycie, Sabin, Leah, Yasunaga, Ari, Subramanian, Harry, Lamborn, Ian, Gordesky-Gold, Beth, Cherry, Sara

“…A unique facet of arthropod-borne virus (arbovirus) infection is that the pathogens are orally acquired by an insect vector during the taking of a blood meal,…”
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The Utility of Imaging Parameters in Predicting Long-Term Clinical Improvement After Shunt Surgery in Patients with Idiopathic Normal Pressure Hydrocephalus by Subramanian, Harry E., Fadel, Sandra A., Matouk, Charles C., Zohrabian, Vahe M., Mahajan, Amit

“…It is difficult to predict which patients with idiopathic normal pressure hydrocephalus (iNPH) will improve after shunt surgery. This study investigated the…”
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Clinical implementation of artificial intelligence in neuroradiology with development of a novel workflow-efficient picture archiving and communication system-based automated brain tumor segmentation and radiomic feature extraction by Aboian, Mariam, Bousabarah, Khaled, Kazarian, Eve, Zeevi, Tal, Holler, Wolfgang, Merkaj, Sara, Cassinelli Petersen, Gabriel, Bahar, Ryan, Subramanian, Harry, Sunku, Pranay, Schrickel, Elizabeth, Bhawnani, Jitendra, Zawalich, Mathew, Mahajan, Amit, Malhotra, Ajay, Payabvash, Sam, Tocino, Irena, Lin, MingDe, Westerhoff, Malte

“…Purpose Personalized interpretation of medical images is critical for optimum patient care, but current tools available to physicians to perform quantitative…”
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The Subjective Experience of Patients Undergoing Shunt Surgery for Idiopathic Normal Pressure Hydrocephalus by Subramanian, Harry E., Mahajan, Amit, Sommaruga, Samuel, Falcone, Guido J., Kahle, Kristopher T., Matouk, Charles C.

“…The counseling of patients with idiopathic normal pressure hydrocephalus (iNPH) is difficult; there is variability in the diagnostic criteria, and a definitive…”
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Trends in Development of Novel Machine Learning Methods for the Identification of Gliomas in Datasets That Include Non-Glioma Images: A Systematic Review by Subramanian, Harry, Dey, Rahul, Brim, Waverly Rose, Tillmanns, Niklas, Cassinelli Petersen, Gabriel, Brackett, Alexandria, Mahajan, Amit, Johnson, Michele, Malhotra, Ajay, Aboian, Mariam

“…Machine learning has been applied to the diagnostic imaging of gliomas to augment classification, prognostication, segmentation, and treatment planning. A…”
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Machine Learning Models for Classifying High- and Low-Grade Gliomas: A Systematic Review and Quality of Reporting Analysis by Bahar, Ryan C, Merkaj, Sara, Cassinelli Petersen, Gabriel I, Tillmanns, Niklas, Subramanian, Harry, Brim, Waverly Rose, Zeevi, Tal, Staib, Lawrence, Kazarian, Eve, Lin, MingDe, Bousabarah, Khaled, Huttner, Anita J, Pala, Andrej, Payabvash, Seyedmehdi, Ivanidze, Jana, Cui, Jin, Malhotra, Ajay, Aboian, Mariam S

“…To systematically review, assess the reporting quality of, and discuss improvement opportunities for studies describing machine learning (ML) models for glioma…”
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Pretreatment predictors of adjuvant chemoradiation in patients receiving transoral robotic surgery for squamous cell carcinoma of the oropharynx: a case control study by Subramanian, Harry E, Park, Henry S, Barbieri, Andrea, Mahajan, Amit, Judson, Benjamin L, Mehra, Saral, Yarbrough, Wendell G, Burtness, Barbara A, Husain, Zain A

“…The purpose of this study was to identify preoperative patient characteristics associated with the incidence of positive surgical margins or lymph node…”
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Microbiota-Dependent Priming of Antiviral Intestinal Immunity in Drosophila by Sansone, Christine L., Cohen, Jonathan, Yasunaga, Ari, Xu, Jie, Osborn, Greg, Subramanian, Harry, Gold, Beth, Buchon, Nicolas, Cherry, Sara

“…Enteric pathogens must overcome intestinal defenses to establish infection. In Drosophila, the ERK signaling pathway inhibits enteric virus infection. The…”
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NIMG-17. SYSTEMATIC REVIEW OF LITERATURE EVALUATING MACHINE LEARNING ALGORITHMS TO DEVELOP OUTCOME PREDICTION MODELS IN GLIOMA USING MOLECULAR IMAGING WITH AMINO ACID PET by Shatalov, Julia, Brim, W R, Subramanian, Harry, Bazaar, John, Johnson, Michele, Aboian, Mariam

“…Abstract PURPOSE Machine learning (ML) algorithms demonstrate accurate prediction of tumor segmentation, molecular pathology, and outcomes in gliomas using MRI…”
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Machine learning approaches for automated segmentation of gliomas (P3-9.004) by Tillmanns, Niklas, Lum, Avery, Brim, Waverly, Subramanian, Harry, Payabvash, Sam, Ikuta, Ichiro, Lin, MingDe, Bousabarah, Khaled, Johnson, Michele, Malhotra, Ajay, Aboian, Mariam

“…Abstract only…”
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Bias assessment of Artificial Intelligence papers in Glioma segmentation using TRIPOD (P14-9.005) by Tillmanns, Niklas, Lum, Avery, Brim, Waverly, Subramanian, Harry, Payabvash, Sam, Ikuta, Ichiro, Lin, MingDe, Bousabarah, Khaled, Johnson, Michele, Malhotra, Ajay, Aboian, Mariam

“…Abstract only…”
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Comparing Deep Learning and Classical Machine Learning Methods For Differentiating Primary CNS Lymphomas From Gliomas – A Systematic Review (P14-9.004) by Petersen, Gabriel Cassinelli, Shatalov, Julia, Verma, Tej, Brim, Waverly, Merkaj, Sara, Bahar, Ryan, Subramanian, Harry, Cui, Jin, Johnson, Michele, Malhotra, Ajay, Omuro, Antonio, Aboian, Mariam

“…Abstract only…”
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NIMG-67. A SYSTEMATIC REVIEW ON THE DEVELOPMENT OF MACHINE LEARNING MODELS FOR DIFFERENTIATING PCNSL FROM GLIOMAS by Petersen, Gabriel Cassinelli, Shatalov, Julia, Brim, W R, Subramanian, Harry, cui, Jin, Johnson, Michele, Malhotra, Ajay, Aboian, Mariam, Brackett, Alexandria

“…Abstract PURPOSE Differentiating gliomas and Primary CNS Lymphomas (PCNSL) represents a diagnostic challenge with important therapeutic ramifications. MR…”
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Systematic Review of Machine Learning Models for Differentiation of Glioma from Brain Metastasis (P14-9.006) by Jekel, Leon, Brim, Waverly Rose, Petersen, Gabriel Cassinelli, Merkaj, Sara, Subramanian, Harry, Zeevi, Tal, Payabvash, Sam, Khaled, Bousabarah, Lin, MingDe, Cui, Jin, Brackett, Alexandria, Johnson, Michele, Omuro, Antonio, Scheffler, Bjorn, Aboian, Mariam

“…Abstract only…”
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Development of a workflow efficient PACS based automated brain tumor segmentation and radiomic feature extraction for clinical implementation (N2.003) by Aboian, Mariam, Bousabarah, Khaled, Kazarian, Eve, Zeevi, Tal, Holler, Wolfgang, Merkaj, Sara, Petersen, Gabriel Cassinelli, Bahar, Ryan, Subramanian, Harry, Sunku, Pranay, Schrickel, Elizabeth, Mahajan, Amit, Malhotra, Ajay, Payabvash, Sam, Tocino, Irena, Lin, MingDe, Westerhoff, Malte

“…Abstract only…”
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Systematic Literature Review of Machine Learning Algorithms Using Pretherapy Radiologic Imaging for Glioma Molecular Subtype Prediction by Lost, Jan, Verma, Tej, Jekel, Leon, von Reppert, Marc, Tillmanns, Niklas, Merkaj, Sara, Petersen, Gabriel Cassinelli, Bahar, Ryan, Gordem, Ayyüce, Haider, Muhammad A, Subramanian, Harry, Brim, Waverly, Ikuta, Ichiro, Omuro, Antonio, Conte, Gian Marco, Marquez-Nostra, Bernadette V, Avesta, Arman, Bousabarah, Khaled, Nabavizadeh, Ali, Kazerooni, Anahita Fathi, Aneja, Sanjay, Bakas, Spyridon, Lin, MingDe, Sabel, Michael, Aboian, Mariam

“…The molecular profile of gliomas is a prognostic indicator for survival, driving clinical decision-making for treatment. Pathology-based molecular diagnosis is…”
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NIMG-35. MACHINE LEARNING GLIOMA GRADE PREDICTION LITERATURE: A TRIPOD ANALYSIS OF REPORTING QUALITY by Merkaj, Sara, Bahar, Ryan, Brim, W R, Subramanian, Harry, Zeevi, Tal, Kazarian, Eve, Lin, Ming, Bousabarah, Khaled, Payabvash, Sam, Ivanidze, Jana, Cui, Jin, Tocino, Irena, Malhotra, Ajay, Aboian, Mariam

“…Abstract PURPOSE Reporting guidelines are crucial in model development studies to ensure the quality, transparency and objectivity of reporting. While machine…”
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NIMG-71. IDENTIFYING CLINICALLY APPLICABLE MACHINE LEARNING ALGORITHMS FOR GLIOMA SEGMENTATION USING A SYSTEMATIC LITERATURE REVIEW by Tillmanns, Niklas, Lum, Avery, Brim, W R, Subramanian, Harry, Lin, Ming, Bousabarah, Khaled, Malhotra, Ajay, cui, Jin, Brackett, Alexandria, Payabvash, Sam, Ikuta, Ichiro, Johnson, Michele, Turowski, Bernd, Aboian, Mariam

“…Abstract PURPOSE Nowadays Machine learning (ML) algorithms are often used for segmentation of gliomas, but which algorithms provide the most accurate method…”
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NIMG-38. MEASURING ADHERENCE TO TRIPOD OF ARTIFICIAL INTELLIGENCE PAPERS IN THE GLIOMA SEGMENTATION by Tillmanns, Niklas, Lum, Avery, Brim, W R, Subramanian, Harry, Lin, Ming, Bousabarah, Khaled, Malhotra, Ajay, cui, Jin, Brackett, Alexandria, Payabvash, Sam, Ikuta, Ichiro, Johnson, Michele, Turowski, Bernd, Aboian, Mariam

“…Abstract PURPOSE Generalizability, reproducibility and objectivity are critical elements that need to be considered when translating machine learning models…”
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NIMG-46. SYSTEMATIC LITERATURE REVIEW OF ARTIFICIAL INTELLIGENCE ALGORITHMS USING PRE-THERAPY MR IMAGING FOR GLIOMA MOLECULAR SUBTYPE CLASSIFICATION by Lost, Jan, Verma, Tej, Tillmanns, Niklas, Brim, W R, Subramanian, Harry, Ikuta, Ichiro, Bronen, Richard, Zucconi, William, Lin, Ming, Bousabarah, Khaled, Johnson, Michele, Cui, Jin, Malhotra, Ajay, Sabel, Michael, Aboian, Mariam

“…Abstract PURPOSE Identifying molecular subtypes in gliomas has prognostic and therapeutic value, traditionally after invasive neurosurgical tumor resection or…”
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