Publications

This page highlights a selection of publications from the lab. For a complete list of publications, please see the links below.

Selected Publications

2024

Lin Y, Hoyt AC, Manuel VG, Inkelas M, Hsu W. Using Discrete Event Simulation to Design and Assess an AI-aided Workflow for Same-day Diagnostic Testing of Women Undergoing Breast Screening. AMIA Jt Summits Transl Sci Proc. 2024 May 31;2024:314-323. 📄 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11141813/

AI-aided same-day diagnostic workups for abnormal mammograms can reduce patient anxiety and unnecessary recalls but may also decrease daily patient volume by 4% and increase clinic time by 24%, simulations show. Adjusting hours and resources could help. Discrete event simulations can help with planning for these unintended consequences.

Ramwala OA, Lowry KP, Cross NM, Hsu W, Austin CC, Mooney SD, Lee CI. Establishing a Validation Infrastructure for Imaging-Based Artificial Intelligence Algorithms Before Clinical Implementation. J Am Coll Radiol. 2024 May 22:S1546-1440(24)00451-4. 📄 https://doi.org/10.1016/j.jacr.2024.04.027

AI algorithms, even FDA-cleared ones, need local validation to ensure accuracy and safety. Challenges include data privacy and ensuring models perform consistently across diverse populations. In this manuscript, we suggest steps for creating an efficient validation infrastructure to improve radiology workflows and patient outcomes.

Rahrooh A, Garlid AO, Bartlett K, Coons W, Petousis P, Hsu W, Bui AAT. Towards a framework for interoperability and reproducibility of predictive models. J Biomed Inform. 2024 Jan;149:104551. 📄 http://dx.doi.org/10.1016/j.jbi.2023.104551

Machine learning models in healthcare are not disseminated in a standardized manner, hindering reproducibility. We describe an Automated Metadata Pipeline (AMP), which converts models into extended PMML files, enhancing interoperability and reproducibility.

2023

Wei L, Yadav A, Hsu W. CTFlow: Mitigating Effects of Computed Tomography Acquisition and Reconstruction with Normalizing Flows. Med Image Comput Comput Assist Interv. 2023 Oct;14226:413-422. 📄 https://doi.org/10.1007/978-3-031-43990-2_39 🧑‍💻 https://github.com/hsu-lab/ctflow
This paper introduces CTFlow, a normalizing flows-based method to harmonize CT scans across different doses and kernels. It reduces image variability and improves lung nodule detection.

Prosper AE, Kammer MN, Maldonado F, Aberle DR, Hsu W. Expanding Role of Advanced Image Analysis in CT-detected Indeterminate Pulmonary Nodules and Early Lung Cancer Characterization. Radiology. 2023;309(1):e222904. 📄 http://dx.doi.org/10.1148/radiol.222904
Implementing low-dose chest CT for lung screening can advance lung cancer care through early detection. However, millions of pulmonary nodules will be detected annually, which must be managed. Accurate image analysis using AI/ML is key to minimizing potential harms that may result from low-risk nodules. Challenges towards clinically useful AI/ML include heterogeneous imaging parameters and the need for well-annotated data sets.

Lin Y, Liang LJ, Ding R, Prosper AE, Aberle DR, Hsu W. Factors Associated With Nonadherence to Lung Cancer Screening Across Multiple Screening Time Points. JAMA Netw Open. 2023 May 1;6(5):e2315250. 📄 https://doi.org/10.1001/jamanetworkopen.2023.15250
Low adherence to Lung-RADS recommendations in practice contrasts with high adherence in trials. Identifying nonadherent patients is important to realize the reduction in mortality as observed in randomized trials. Our study found temporal factors linked to nonadherence to develop tailored interventions to improve adherence.

Marasinou C, Li B, Paige J, Omigbodun A, Nakhaei N, Hoyt A, et al. Improving the Quantitative Analysis of Breast Microcalcifications: A Multiscale Approach. Journal of Digital Imaging [Internet]. 2023;36(3):1016–28. Available from: http://dx.doi.org/10.1007/s10278-022-00751-3

Ding R, Yadav A, Rodriguez E, Silva ACAL da, Hsu W. Tailoring pretext tasks to improve self-supervised learning in histopathologic subtype classification of lung adenocarcinomas. Comput Biol Med. 2023;166:107484. Available from: http://dx.doi.org/10.1016/j.compbiomed.2023.107484

Paige J, Lee C, Wang PC, Brentnall A, Naeim A, Hsu W, et al. Variability Among Breast Cancer Risk Classification Models When Applied at the Level of the Individual Woman. Journal of General Internal Medicine. 2023;38(11).

2022

Marathe K, Marasinou C, Li B, Nakhaei N, Li B, Elmore JG, et al. Automated quantitative assessment of amorphous calcifications: Towards improved malignancy risk stratification. Comput Biol Med. 2022;146:105504. Available from: https://doi.org/10.1016%2Fj.compbiomed.2022.105504

Hsu W, Hippe DS, Nakhaei N, Wang PC, Zhu B, Siu N, et al. External Validation of an Ensemble Model for Automated Mammography Interpretation by Artificial Intelligence. JAMA Network Open. 2022;5(11):e2242343. Available from: https://doi.org/10.1001%2Fjamanetworkopen.2022.42343

Lin Y, Fu M, Ding R, Inoue K, Jeon C, Hsu W, et al. Patient Adherence to Lung CT Screening Reporting & Data System–Recommended Screening Intervals in the United States: A Systematic Review and Meta-Analysis. J Thorac Oncol. 2022;17(1):38–55.

Hendrix N, Lowry KP, Elmore JG, Lotter W, Sorensen G, Hsu W, et al. Radiologist Preferences for Artificial Intelligence-Based Decision Support During Screening Mammography Interpretation. Journal of the American College of Radiology. 2022;19(10):1098–110. Available from: http://dx.doi.org/10.1016/j.jacr.2022.06.019

Hsu W, Sohn JH. Using Radiomics for Risk Stratification: Where We Need to Go. Radiology. 2022;302(2):435–7. Available from: https://doi.org/10.1148%2Fradiol.2021212085

Older

Prosper A, Inoue K, Brown K, Bui A, Aberle D, Hsu W. Association of Inclusion of More Black Individuals in Lung Cancer Screening With Reduced Mortality. Jama Netw Open. 2021;4(8):e2119629.

Peterson E, May F, Kachikian O, Soroudi C, Naini B, Kang Y, et al. Automated identification and assignment of colonoscopy surveillance recommendations for individuals with colorectal polyps. Gastrointest Endosc. 2021;94(5):978–87.

Matiasz N, Wood J, Wang W, Silva A, Hsu W. Experiment Selection in Meta-Analytic Piecemeal Causal Discovery. Ieee Access. 2021;9:97929–41.

Emaminejad N, Wahi‐Anwar M, Kim G, Hsu W, Brown M, McNitt‐Gray M. Reproducibility of lung nodule radiomic features: Multivariable and univariable investigations that account for interactions between CT acquisition and reconstruction parameters. Med Phys. 2021;48(6):2906–19.

Smedley N, Aberle D, Hsu W. Using deep neural networks and interpretability methods to identify gene expression patterns that predict radiomic features and histology in non-small cell lung cancer. J Medical Imaging. 2021;8(3):031906.

Smedley NF, El-Saden S, Hsu W. Discovering and interpreting transcriptomic drivers of imaging traits using neural networks. Bioinform. 2020;36(11):3537–48. Available from: http://dx.doi.org/10.1093/bioinformatics/btaa126

Li M, Hsu W, Xie X, Cong J, Gao W. SACNN: Self-Attention Convolutional Neural Network for Low-Dose CT Denoising With Self-Supervised Perceptual Loss Network. Ieee T Med Imaging. 2020;39(7):2289–301. Available from: http://dx.doi.org/10.1109/tmi.2020.2968472

Gao Y, Kalbasi A, Hsu W, Ruan D, Fu J, Shao J, et al. Treatment effect prediction for sarcoma patients treated with preoperative radiotherapy using radiomics features from longitudinal diffusion-weighted MRI. Phys Medicine Biology. 2020;65(17):175006. Available from: https://doi.org/10.1088%2F1361-6560%2Fab9e58

Shen S, Han SX, Aberle DR, Bui AA, Hsu W. An interpretable deep hierarchical semantic convolutional neural network for lung nodule malignancy classification. Expert Systems with Applications. 2019;128:84–95. Available from: https://doi.org/10.1016%2Fj.eswa.2019.01.048

Winter A, Aberle DR, Hsu W. External validation and recalibration of the Brock model to predict probability of cancer in pulmonary nodules using NLST data. Thorax. 2019;74(6):551–63. Available from: https://doi.org/10.1136%2Fthoraxjnl-2018-212413

Hsu W, Elmore JG. Shining Light Into the Black Box of Machine Learning. JNCI: Journal of the National Cancer Institute. 2019;111(9):877–9. Available from: http://dx.doi.org/10.1093/jnci/djy226

Petousis P, Winter A, Speier W, Aberle D, Hsu W, Bui A. Using Sequential Decision Making to Improve Lung Cancer Screening Performance. Using Sequential Decision Making to Improve Lung Cancer Screening Performance. 2019;7(119403–119419).

Hsu W, Hoyt AC. Using Time as a Measure of Impact for AI Systems: Implications in Breast Screening. Radiology Artif Intell. 2019;1(4):e190107. Available from: http://dx.doi.org/10.1148/ryai.2019190107

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