Search Results - "Li, Thomas Z"

Lung CT harmonization of paired reconstruction kernel images using generative adversarial networks by Krishnan, Aravind R., Xu, Kaiwen, Li, Thomas Z., Remedios, Lucas W., Sandler, Kim L., Maldonado, Fabien, Landman, Bennett A.

“…Background The kernel used in CT image reconstruction is an important factor that determines the texture of the CT image. Consistency of reconstruction kernel…”
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AI Body Composition in Lung Cancer Screening: Added Value Beyond Lung Cancer Detection by Xu, Kaiwen, Khan, Mirza S, Li, Thomas Z, Gao, Riqiang, Terry, James G, Huo, Yuankai, Lasko, Thomas A, Carr, John Jeffrey, Maldonado, Fabien, Landman, Bennett A, Sandler, Kim L

“…Background An artificial intelligence (AI) algorithm has been developed for fully automated body composition assessment of lung cancer screening noncontrast…”
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Quantifying emphysema in lung screening computed tomography with robust automated lobe segmentation by Li, Thomas Z., Hin Lee, Ho, Xu, Kaiwen, Gao, Riqiang, Dawant, Benoit M., Maldonado, Fabien, Sandler, Kim L., Landman, Bennett A.

“…Anatomy-based quantification of emphysema in a lung screening cohort has the potential to improve lung cancer risk stratification and risk communication…”
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The Lung Cancer Prediction Model "Stress Test": Assessment of Models' Performance in a High-Risk Prospective Pulmonary Nodule Cohort by Heideman, Brent E, Kammer, Michael N, Paez, Rafael, Swanson, Terra, Godfrey, Caroline M, Low, See-Wei, Xiao, David, Li, Thomas Z, Richardson, Jacob R, Knight, Michael A, Shojaee, Samira, Deppen, Stephen A, Lentz, Robert J, Grogan, Eric L, Maldonado, Fabien

“…Pulmonary nodules represent a growing health care burden because of delayed diagnosis of malignant lesions and overtesting for benign processes. Clinical…”
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The Lung Cancer Prediction Model “Stress Test” by Heideman, Brent E., Kammer, Michael N., Paez, Rafael, Swanson, Terra, Godfrey, Caroline M., Low, See-Wei, Xiao, David, Li, Thomas Z., Richardson, Jacob R., Knight, Michael A., Shojaee, Samira, Deppen, Stephen A., Lentz, Robert J., Grogan, Eric L., Maldonado, Fabien

“…Pulmonary nodules represent a growing health care burden because of delayed diagnosis of malignant lesions and overtesting for benign processes. Clinical…”
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Zero-shot CT Field-of-view Completion with Unconditional Generative Diffusion Prior by Xu, Kaiwen, Krishnan, Aravind R, Li, Thomas Z, Huo, Yuankai, Sandler, Kim L, Maldonado, Fabien, Landman, Bennett A

“…Anatomically consistent field-of-view (FOV) completion to recover truncated body sections has important applications in quantitative analyses of computed…”
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Unsupervised Discovery of Clinical Disease Signatures Using Probabilistic Independence by Lasko, Thomas A, Still, John M, Li, Thomas Z, Mota, Marco Barbero, Stead, William W, Strobl, Eric V, Landman, Bennett A, Maldonado, Fabien

“…Insufficiently precise diagnosis of clinical disease is likely responsible for many treatment failures, even for common conditions and treatments. With a large…”
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Time-distance vision transformers in lung cancer diagnosis from longitudinal computed tomography by Li, Thomas Z, Xu, Kaiwen, Gao, Riqiang, Tang, Yucheng, Lasko, Thomas A, Maldonado, Fabien, Sandler, Kim, Landman, Bennett A

“…Features learned from single radiologic images are unable to provide information about whether and how much a lesion may be changing over time. Time-dependent…”
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No winners: Performance of lung cancer prediction models depends on screening-detected, incidental, and biopsied pulmonary nodule use cases by Li, Thomas Z, Xu, Kaiwen, Krishnan, Aravind, Gao, Riqiang, Kammer, Michael N, Antic, Sanja, Xiao, David, Knight, Michael, Martinez, Yency, Paez, Rafael, Lentz, Robert J, Deppen, Stephen, Grogan, Eric L, Lasko, Thomas A, Sandler, Kim L, Maldonado, Fabien, Landman, Bennett A

“…Statistical models for predicting lung cancer have the potential to facilitate earlier diagnosis of malignancy and avoid invasive workup of benign disease…”
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Longitudinal Multimodal Transformer Integrating Imaging and Latent Clinical Signatures From Routine EHRs for Pulmonary Nodule Classification by Li, Thomas Z, Still, John M, Xu, Kaiwen, Lee, Ho Hin, Cai, Leon Y, Krishnan, Aravind R, Gao, Riqiang, Khan, Mirza S, Antic, Sanja, Kammer, Michael, Sandler, Kim L, Maldonado, Fabien, Landman, Bennett A, Lasko, Thomas A

“…The accuracy of predictive models for solitary pulmonary nodule (SPN) diagnosis can be greatly increased by incorporating repeat imaging and medical context,…”
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