- BS, Engineering, University of Iowa, Iowa City, IA
10/2013 - 08/2015
- Undergraduate research assistant (under Jessica Sieren)
08/2015 - Present
- Graduate research assistant (under Jessica Sieren)
Johanna Uthoff is entering as a PhD student at the University of Iowa in the Fall of 2015 semester. While with the lab, she has helped to develop a computed aided diagnosis (CAD) tool that uses computed tomography (CT) image features of lung nodules and surrounding lung tissue to quantify malignancy risk. This tool has been developed and tested on both high-resolution research and lower-resolution clinical cohorts. She retrospectively collected clinical lung nodule subjects with a solitary lung nodule that had been pathologically confimed to be either primary lung cancer or a benign process. Recently she has also worked on the integration of a semi-automatic segmentation tool to replace the current manual tracing method; this will decrease the time required to preprocess the cases and eliminate some user-bias. Future plans include the further validation of the algorithm, the implementation of a graphical user interface, and the inclusion and analysis of established risk calculators.
Johanna has also aided in the Head-Neck Study, a multi-disciplinary effort to determine minimally effective positron emission tomography (PET) and CT scan parameters for head-neck cancer with no local lymph node metastasis. She has written an automatic CT segmentation tool using the maximum standard uptake value (SUVmax) of the primary tumor in PET. She will analyze these segmentations with a modified version of the lung cancer CAD tool to predict the malignancy risk of PET positive lymph nodes.
- The use of CT for lung cancer screening leads to improved lung cancer survival; however, CT for lung nodule characterization suffers from poor specificity and many suspicious nodules identified are found to be benign upon follow-up. The group is seeking to improve the specificity of CT screening for lung cancer through the development of a computer aided diagnosis tool which extracts features from quantitative CT images of the lung nodule and its surrounding lung tissue.
- S. Dilger, A. Judisch, J. Uthoff, E.A. Hoffman, J.D. Newell Jr, J.C. Sieren, "A Systematic Investigation Into Lung Tissue Feature Extraction to Improve the Classification of Pulmonary Nodules", American Thoracic Society (San Diego, CA, USA, 2014)
- S. Dilger, A. Judisch, J. Uthoff, E. Hammond, J.D. Newell Jr, J.C. Sieren, "Improved Pulmonary Nodule Classification Utilizing Lung Parenchyma Texture Features", Proceedings of SPIE Medical Imaging, 2015
- S. Dilger, J. Uthoff, A. Judisch, E. Hammond, S. Mott, B. Smith, J.D. Newell Jr, E.A. Hoffman, J.C. Sieren, for the COPDGene Investigators, "Improved Pulmonary Nodule Classification Utilizing Quantitative Lung Parenchyma Features", Journal of Medical Imaging (accepted 2015), In Press
J. Uthoff, S.K.N. Dilger, F. De Stefano, N. Koehn, J.C. Sieren, "Survey of Semi-Automatic Segmentation Tools for Computed Tomography Lung Nodule Assessment", American Thoracic Society (ATS), ID# 10926, 2016
M. Muralidharan, S.K.N. Dilger, J. Uthoff, J.C. Sieren, "A Graphical User Interface for Comparison of Lung Cancer Risk Prediction Models", American Thoracic Society (ATS), ID# 8086, 2016
S.K.N. Dilger, J. Uthoff, E. Hammond, S.L. Mott, B.J. Smith, M. Ahuja, M. Gailey, A. McGruder, J.D. Newell, Jr., E.A. Hoffman, J.C. Sieren, "Clinical Computer- Aided Diagnosis Tool for Pulmonary Nodule Characterizations Shows Improved Performance with the Inclusion of Nodule-Associated Parenchymal Features", American Thoracic Society (ATS), ID# 6105, 2016
S.K.N. Dilger, J. Uthoff, E. Hammond, M. Ahuja, M. Gailey, A. McGruder, J.D. Newell, Jr., E.A. Hoffman, J.C. Sieren, "Identifying Longitudinal Computed Tomography Biomarkers of Malignancy to Improve Noninvasive Lung Cancer Diagnosis, American Thoracic Society (ATS), ID# 8320, 2016
N. Koehn, S.K.N. Dilger, J. Uthoff, E. Hammond, F. De Stefano, M. Muralidharan, E.A. Hoffman, J.D. Newell, Jr., R. Sanchez, J.C. Sieren, American Thoracic Society (ATS), ID# 8135, 2016