Issam El Naqa
University of Michigan
Machine learningCancerSupport vector machineInternal medicineRadiologySurgeryOncologyArtificial intelligenceMedical physicsRadiation treatment planningLung cancerLungRadiomicsNuclear medicineMedical imagingComputer scienceRadiation therapyMedicineDosimetryImage processing
Publications 272
#1Jordan D. Fuhrman (U of C: University of Chicago)H-Index: 1
#2Naveena Gorre (U of C: University of Chicago)
Last. Maryellen L. Giger (U of C: University of Chicago)H-Index: 94
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The development of medical imaging AI systems for evaluating COVID-19 patients has demonstrated potential for improving clinical decision-making and assessing patient outcomes during the recent COVID-19 pandemic. These have been applied to many medical imaging tasks including disease diagnosis and patient prognosis, as well as augmented other clinical measurements to better inform treatment decisions. Because these systems are used in life-or-death decisions, clinical implementation relies on us...
#1Elisabeth Pfaehler (UMCG: University Medical Center Groningen)H-Index: 8
#2Ivan Zhovannik (Radboud University Nijmegen)H-Index: 5
Last. Leonard Wee (Maastricht University Medical Centre)H-Index: 15
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Abstract null null Purpose null Although quantitative image biomarkers (radiomics) show promising value for cancer diagnosis, prognosis, and treatment assessment, these biomarkers still lack reproducibility. In this systematic review, we aimed to assess the progress in radiomics reproducibility and repeatability in the recent years. null null null Methods and materials null Four hundred fifty-one abstracts were retrieved according to the original PubMed search pattern with the publication dates ...
#1Michele AvanzoH-Index: 15
#2Vito GagliardiH-Index: 1
Last. G. SartorH-Index: 7
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PURPOSE The aim of this study is to improve the performance of machine learning (ML) models in predicting response of non-small cell lung cancer (NSCLC) to stereotactic body radiation therapy (SBRT) by integrating image features from pre-treatment computed tomography (CT) with features from the biologically effective dose (BED) distribution. MATERIALS AND METHODS Image features, consisting of crafted radiomic features or machine-learned features extracted using a convolutional neural network, we...
#1Julia M. Pakela (UM: University of Michigan)H-Index: 2
#2Martha M. Matuszak (UM: University of Michigan)H-Index: 23
Last. Issam El NaqaH-Index: 60
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Modern radiotherapy stands to benefit from the ability to efficiently adapt plans during treatment in response to setup and geometric variations such as those caused by internal organ deformation or tumor shrinkage. A promising strategy is to develop a framework, which given an initial state defined by patient-attributes, can predict future states based on patterns from a well-defined patient population. Here, we investigate the feasibility of predicting patient anatomical changes, defined as a ...
#1Noora Ba Sunbul (UM: University of Michigan)H-Index: 2
#2Wei Zhang (UM: University of Michigan)H-Index: 42
Last. Shaun D. Clarke (UM: University of Michigan)H-Index: 18
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PURPOSE Electron-based ultra-high dose rate radiation therapy (UHDR-RT), also known as Flash-RT, has shown the ability to improve the therapeutic index in comparison to conventional radiotherapy (CONV-RT) through increased sparing of normal tissue. However, the extremely high dose rates in UHDR-RT have raised the need for accurate real-time dosimetry tools. This work aims to demonstrate the potential of the emerging technology of Ionized Radiation Acoustic Imaging (iRAI) through simulation studi...
#1Issam El NaqaH-Index: 60
#2John M. Boone (UC Davis: University of California, Davis)H-Index: 71
Last. Berkman Sahiner (FDA: Food and Drug Administration)H-Index: 59
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The Abstract is intended to provide a concise summary of the study and its scientific findings. For AI/ML applications in medical physics, a problem statement and rationale for utilizing these algorithms are necessary while highlighting the novelty of the approach. A brief numerical description of how the data are partitioned into subsets for training of the AI/ML algorithm, validation (including tuning of parameters), and independent testing of algorithm performance is required. This is to be f...
#1Habib Zaidi (Geneva College)H-Index: 66
#2Issam El Naqa (McGill University)H-Index: 60
The widespread availability of high-performance computing and the popularity of artificial intelligence (AI) with machine learning and deep learning (ML/DL) algorithms at the helm have stimulated the development of many applications involving the use of AI-based techniques in molecular imaging research. Applications reported in the literature encompass various areas, including innovative design concepts in positron emission tomography (PET) instrumentation, quantitative image reconstruction and ...
Artificial intelligence and machine learning have the potential to make cancer care more accessible, efficient, cost-effective and personalized. However, meticulously planned prospective deployment strategies are required to validate the performance of these technologies in real-world clinical settings and overcome the human trust barrier.
#1Sunan Cui (UM: University of Michigan)H-Index: 6
#2Randall K. Ten Haken (UM: University of Michigan)H-Index: 82
Last. Issam El Naqa (UM: University of Michigan)H-Index: 60
view all 3 authors...
Abstract Introduction Novel actuarial deep learning neural network (ADNN) architectures are proposed for joint prediction of radiotherapy outcomes, i.e., radiation pneumonitis (RP) and local control (LC) in stage III non-small-cell lung cancer (NSCLC) patients. Unlike NTCP/TCP models that use dosimetric information solely, our proposed models consider complex interactions among multi-omics information including PET radiomics, cytokines and miRNAs. Additional time-to-event information is also uti...
#1Vitali Moiseenko (UCSD: University of California, San Diego)H-Index: 33
#2Jona A. Hattangadi-Gluth (UCSD: University of California, San Diego)H-Index: 23
Last. Issam El NaqaH-Index: 60
view all 10 authors...
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