Mr. Owen Mitchell Profile

Owen Mitchell

at Univ of Chicago Medicine

SPIE Involvement:

Author

Publications (3)

SPIE Journal Paper | 11 December 2024 Open Access

Radiomics for differentiation of somatic BAP1 mutation on CT scans of patients with pleural mesothelioma

Mena Shenouda, Abbas Shaikh, Ilana Deutsch, Owen Mitchell, Hedy Kindler, Samuel Armato

JMI, Vol. 11, Issue 06, 064501, (December 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.6.064501

KEYWORDS: Tumors, Radiomics, Feature extraction, Image segmentation, Cross validation, Decision trees, Computed tomography, Tunable filters, Performance modeling, Education and training

Read Abstract +

DOWNLOAD PAPER

Proceedings Article | 3 April 2024 Poster + Paper

The use of radiomics on computed tomography scans for differentiation of somatic BAP1 mutation status for patients with pleural mesothelioma

Mena Shenouda, Abbas Shaikh, Ilana Deutsch, Owen Mitchell, Hedy Kindler, Samuel Armato

Proceedings Volume 12927, 1292732 (2024) https://doi.org/10.1117/12.3000085

KEYWORDS: Tumors, Feature extraction, Radiomics, Computed tomography, Decision trees, Mesothelioma, Image segmentation, Machine learning, Cross validation

Read Abstract +

The linking of image analysis, in particular radiomics, with genetic profiles, known as “imaging genomics,” has been successfully applied to many diseases and anatomic regions. The application of imaging genomics in mesothelioma, however, remains limited. Pleural mesothelioma (PM) is a devastating cancer associated with asbestos exposure and has a poor prognosis. The BRCA1-associated protein 1 (BAP1 ) gene is of considerable interest because somatic BAP1 mutations are the most common alteration associated with PM. Further, germline mutation of the BAP1 gene has been linked to PM. This study aims to explore the potential of radiomics to identify somatic BAP1 mutations and assess the feasibility of radiomics in future research in identifying germline mutations. A cohort of 149 patients with PM and known somatic BAP1 mutation status was collected, and a previously published deep learning model was used to automatically segment tumor on three representative sections from each patient’s computed tomography scans. Preprocessing, including gray-level discretization and resampling, was performed, and intensity-based and 2D texture features were extracted from the segmented tumor regions. Synthetic Minority Over-sampling Technique (SMOTE) combined with Tomek links was employed to address data imbalance. The top features were selected and used to train 18 separate machine learning models. The performance of the models in distinguishing between BAP1 -mutated (BAP1+) versus BAP1 wild-type (BAP1 -) tumors were evaluated using area under the receiver operating characteristic curve (ROC AUC) as the figure of merit. This study achieved an AUC value of 0.69 (95% confidence interval: 0.60, 0.77) using a decision tree classifier. Overall, this novel, proof-of-concept work demonstrates the potential of radiomics in differentiating between BAP1+/- in patients with PM. Future work will extend these methods to the assessment of germline BAP1 mutation status through image analysis for improved patient prognostication.

Proceedings Article | 3 April 2024 Poster + Paper

Assessing radiomic feature robustness using agreement over image perturbation

Abbas Shaikh, Ilana Deutsch, Mena Shenouda, Owen Mitchell, Hedy Kindler, Samuel Armato

Proceedings Volume 12927, 129272Z (2024) https://doi.org/10.1117/12.3006291

KEYWORDS: Radiomics, Feature extraction, Image segmentation, Feature selection, Tumors, Reproducibility, Image acquisition, Correlation coefficients, Computed tomography

Read Abstract +

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years