no code implementations • 16 May 2024 • Thomas Z. Li, Kaiwen Xu, Aravind Krishnan, Riqiang Gao, Michael N. Kammer, Sanja Antic, David Xiao, Michael Knight, Yency Martinez, Rafael Paez, Robert J. Lentz, Stephen Deppen, Eric L. Grogan, Thomas A. Lasko, Kim L. Sandler, Fabien Maldonado, Bennett A. Landman
This study retrospectively evaluated promising predictive models for lung cancer prediction in three clinical settings: lung cancer screening with low-dose computed tomography, incidentally detected pulmonary nodules, and nodules deemed suspicious enough to warrant a biopsy.
no code implementations • 8 Feb 2024 • Thomas A. Lasko, John M. Still, Thomas Z. Li, Marco Barbero Mota, William W. Stead, Eric V. Strobl, Bennett A. Landman, Fabien Maldonado
Insufficiently precise diagnosis of clinical disease is likely responsible for many treatment failures, even for common conditions and treatments.
no code implementations • 7 Apr 2023 • Kaiwen Xu, Aravind R. Krishnan, Thomas Z. Li, Yuankai Huo, Kim L. Sandler, Fabien Maldonado, Bennett A. Landman
Anatomically consistent field-of-view (FOV) completion to recover truncated body sections has important applications in quantitative analyses of computed tomography (CT) with limited FOV.
1 code implementation • 6 Apr 2023 • Thomas Z. Li, John M. Still, Kaiwen Xu, Ho Hin Lee, Leon Y. Cai, Aravind R. Krishnan, Riqiang Gao, Mirza S. Khan, Sanja Antic, Michael Kammer, Kim L. Sandler, Fabien Maldonado, Bennett A. Landman, Thomas A. Lasko
In this work, we propose a transformer-based multimodal strategy to integrate repeat imaging with longitudinal clinical signatures from routinely collected EHRs for SPN classification.
1 code implementation • 4 Sep 2022 • Thomas Z. Li, Kaiwen Xu, Riqiang Gao, Yucheng Tang, Thomas A. Lasko, Fabien Maldonado, Kim Sandler, Bennett A. Landman
In cross-validation on screening chest CTs from the NLST, our methods (0. 785 and 0. 786 AUC respectively) significantly outperform a cross-sectional approach (0. 734 AUC) and match the discriminative performance of the leading longitudinal medical imaging algorithm (0. 779 AUC) on benign versus malignant classification.