Mr. Joseph Bae Profile

Joseph Bae

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Proceedings Article | 4 April 2022 Presentation + Paper

Pre-treatment radiomics from radiotherapy dose regions predict distant brain metastases in stereotactic radiosurgery

Joseph Bae, Renee Cattell, Ewa Zabrocka, John Roberson, David Payne, Kartik Mani, Prateek Prasanna

Proceedings Volume 12031, 120311O (2022) https://doi.org/10.1117/12.2612088

KEYWORDS: Machine learning, Brain cancer, Magnetic resonance imaging, Brain, Radiotherapy, Tumors, Feature extraction, Neuroimaging, Radiation oncology, Statistical analysis

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Stereotactic radiosurgery (SRS) is frequently employed to treat brain metastases. However, <50% of patients treated with this method develop distant brain metastases (DBMs). As a result, these patients are followed using Magnetic Resonance Imaging (MRI) to identify DBM development. There is no current pre-treatment risk metric to identify which patients might be likely to develop DBMs. In this study, pre-treatment MRIs and radiotherapy planning data including structure sets and radiation dose maps were obtained for 81 SRS brain metastases treatment courses. Clinical variables including performance status, age, number of tumors, and primary tumor type were also collected. Pre-treatment MRIs were skull-stripped and normalized. 3D radiomic features from grey-intensity, Laws Energy, Gabor, Haralick, and CoLlAGe feature families were extracted from T1, T1 contrast-enhanced (T1w), T2, and FLAIR pre-treatment MRI sequences in brain regions receiving 0-25%, 25-50%, 50-75%, and 75-100% of prescribed radiation dose. A baseline classification model for DBM was created using clinical variables. Ablation studies were performed to determine which dose region and MRI sequence contained radiomic features most predictive for DBM development using machine learning (ML) classifiers. An ML classifier trained on 3D radiomic features from the 50-75% dose region of pre-treatment T1w MRI (AUC: 0.71, 95% CI: 0.68-0.74) outperformed the baseline model (AUC: 0.50, 95% CI: 0.47-0.53) for DBM prediction. In conclusion, we leverage radiotherapy dose regions to identify subcompartments for radiomic feature extraction from multi-parametric pre-treatment MRI data. We demonstrate that radiomic features from these dose regions can be used to predict DBM for SRS-treated brain metastases.

Proceedings Article | 15 February 2021 Presentation + Paper

Evolution of chest radiograph radiomics and association with respiratory and inflammatory parameters in COVID-19 patients undergoing prone ventilation: preliminary findings

Connor Cowan, Joseph Bae, Gagandeep Singh, Rohit Khullar, Shrey Shah, Nikhil Madan, Prateek Prasanna

Proceedings Volume 11597, 1159709 (2021) https://doi.org/10.1117/12.2582292

KEYWORDS: Chest imaging, Radiography, Statistical analysis, Proteins, Pathophysiology, Oxygen, Minerals, Computational modeling, Chest

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The coronavirus disease 2019 (COVID-19) pandemic continues to spread internationally as deaths climb worldwide. Patients with severe disease may progress to acute respiratory distress syndrome (ARDS), often requiring mechanical ventilation. Proning may be employed for patients undergoing mechanical ventilation in order to improve oxygenation. Hospitalized COVID-19 patients are often monitored through the use of portable chest radiography (CXR). Computational modeling of CXRs has been explored in COVID-19, and radiomic features have been used to predict disease progression and severity. However, studies have not yet drawn strong connections between specific radiomic features and clinical manifestations of the disease. In this retrospective study we analyze 23 COVID-19 patients hospitalized between March and May 2020. Each of these patients underwent mechanical ventilation with proning. CXRs were taken before, during, and after proning. Clinical laboratory data including blood gas levels, severity assessment scores, serological markers for inflammation, etc. were also measured for patients before and after proning. In our analysis, 1) we identify CXR radiomic features that change significantly after proning and 2) we demonstrate correlations between radiomic features and the progression of several clinical parameters implicated in COVID-19 pathophysiology. We report statistically significant positive correlations between several radiomic features extracted from pre-proning CXRs with changes in clinical measurements of LDH, oxygen saturation, ferritin, tidal volume, arterial oxygen partial pressure (PaO2) to fraction of inspired oxygen (FIO2) ratio, and FIO2. These preliminary findings suggest that radiomic features from CXRs might reflect changes in measurable clinical variables, thus indicating the potential for portable radiography to monitor the efficacy of the proning procedure in COVID-19 patients undergoing mechanical ventilation.

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