Mr. Eric A. Cohen Profile

Eric A. Cohen

at Penn Medicine

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Author

Publications (9)

Proceedings Article | 3 April 2024 Poster + Paper

Radiomic phenotypes of the background lung parenchyma from [18]F-FDG PET/CT images can augment tumor radiomics and clinical factors in predicting response after surgical resection of tumors in patients with non-small cell lung cancer

Andrew William Chen, Ohm Patel, Eric Cohen, Leonid Roshkovan, Despina Kontos

Proceedings Volume 12927, 129272R (2024) https://doi.org/10.1117/12.3006944

KEYWORDS: Radiomics, Tumors, Lung, Positron emission tomography, Computed tomography, Lung cancer, Biological imaging

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Proceedings Article | 13 July 2022 Paper

External validation of an AI-driven breast cancer risk prediction model in a racially diverse cohort of women undergoing mammographic screening

Aimilia Gastounioti, Mikael Eriksson, Eric Cohen, Walter Mankowski, Lauren Pantalone, Anne Marie McCarthy, Despina Kontos, Per Hall, Emily Conant

Proceedings Volume 12286, 1228617 (2022) https://doi.org/10.1117/12.2627140

KEYWORDS: Breast cancer, Artificial intelligence, Breast, Digital mammography, Machine learning

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Proceedings Article | 4 April 2022 Presentation + Paper

Iterative ComBat methods for harmonization of radiomic features

Hannah Horng, Apurva Singh, Bardia Yousefi, Eric Cohen, Babak Haghighi, Sharyn Katz, Peter Noël, Russell Shinohara, Despina Kontos

Proceedings Volume 12033, 120331F (2022) https://doi.org/10.1117/12.2610831

KEYWORDS: Spatial resolution, Lung, Computed tomography, Image segmentation, Data modeling, Visualization, Statistical analysis, Radiology

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Proceedings Article | 22 May 2020 Paper

Calculation of radiomic features to validate the textural realism of physical anthropomorphic phantoms for digital mammography

Raymond Acciavatti, Eric Cohen, Omid Haji Maghsoudi, Aimilia Gastounioti, Lauren Pantalone, Meng-Kang Hsieh, Bruno Barufaldi, Predrag Bakic, Jinbo Chen, Emily Conant, Despina Kontos, Andrew D. Maidment

Proceedings Volume 11513, 1151309 (2020) https://doi.org/10.1117/12.2564363

KEYWORDS: Breast, Digital mammography, Mammography, Imaging systems, Wavelets, X-ray imaging, Feature extraction, Breast cancer

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Proceedings Article | 16 March 2020 Paper

Assessment of CT image reconstruction parameters on radiomic features in a lung cancer screening cohort: the PROSPR study

Babak Haghighi, Peter Noël, Eric Cohen, Lauren Pantalone, Anil Vachani, Katharine Rendle, Jocelyn Wainwright, Chelsea Saia, Eduardo Mortani Barbosa , Despina Kontos

Proceedings Volume 11314, 113142G (2020) https://doi.org/10.1117/12.2550161

KEYWORDS: Lung cancer, Lung, Computed tomography, Feature extraction, CT reconstruction, Scanners, Image restoration, Cancer, Medicine, Statistical analysis

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Proceedings Article | 16 March 2020 Paper

Robust radiomic feature selection in digital mammography: understanding the effect of imaging acquisition physics using phantom and clinical data analysis

Raymond Acciavatti, Eric Cohen, Omid Haji Maghsoudi, Aimilia Gastounioti, Lauren Pantalone, Meng-Kang Hsieh, Emily Conant, Christopher Scott, Stacey Winham, Karla Kerlikowske, Celine Vachon, Andrew D. Maidment, Despina Kontos

Proceedings Volume 11314, 113140W (2020) https://doi.org/10.1117/12.2549163

KEYWORDS: Digital mammography, Breast, Feature selection, Breast cancer, Feature extraction, Data analysis

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Proceedings Article | 15 March 2019 Paper

Correlative hierarchical clustering-based low-rank dimensionality reduction of radiomics-driven phenotype in non-small cell lung cancer

Bardia Yousefi, Nariman Jahani, Michael LaRiviere , Eric Cohen, Meng-Kang Hsieh, José Marcio Luna, Rhea Chitalia, Jeffrey Thompson , Erica Carpenter , Sharyn Katz, Despina Kontos

Proceedings Volume 10954, 1095416 (2019) https://doi.org/10.1117/12.2515609

KEYWORDS: Lung cancer, Tumors, Feature extraction, Principal component analysis, Medicine, Feature selection, Computed tomography, Cancer, Wavelets, Image segmentation

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Background: Lung cancer is one of the most common cancers in the United States and the most fatal, with 142,670 deaths in 2019. Accurately determining tumor response is critical to clinical treatment decisions, ultimately impacting patient survival. To better differentiate between non-small cell lung cancer (NSCLC) responders and non-responders to therapy, radiomic analysis is emerging as a promising approach to identify associated imaging features undetectable by the human eye. However, the plethora of variables extracted from an image may actually undermine the performance of computer-aided prognostic assessment, known as the curse of dimensionality. In the present study, we show that correlative-driven hierarchical clustering improves high-dimensional radiomics-based feature selection and dimensionality reduction, ultimately predicting overall survival in NSCLC patients. Methods: To select features for high-dimensional radiomics data, a correlation-incorporated hierarchical clustering algorithm automatically categorizes features into several groups. The truncation distance in the resulting dendrogram graph is used to control the categorization of the features, initiating low-rank dimensionality reduction in each cluster, and providing descriptive features for Cox proportional hazards (CPH)-based survival analysis. Using a publicly available non- NSCLC radiogenomic dataset of 204 patients’ CT images, 429 established radiomics features were extracted. Low-rank dimensionality reduction via principal component analysis (PCA) was employed (𝒌 = 𝟏, 𝒏 < 𝟏) to find the representative components of each cluster of features and calculate cluster robustness using the relative weighted consistency metric. Results: Hierarchical clustering categorized radiomic features into several groups without primary initialization of cluster numbers using the correlation distance metric (as a function) to truncate the resulting dendrogram into different distances. The dimensionality was reduced from 429 to 67 features (for truncation distance of 0.1). The robustness within the features in clusters was varied from -1.12 to -30.02 for truncation distances of 0.1 to 1.8, respectively, which indicated that the robustness decreases with increasing truncation distance when smaller number of feature classes (i.e., clusters) are selected. The best multivariate CPH survival model had a C-statistic of 0.71 for truncation distance of 0.1, outperforming conventional PCA approaches by 0.04, even when the same number of principal components was considered for feature dimensionality. Conclusions: Correlative hierarchical clustering algorithm truncation distance is directly associated with robustness of the clusters of features selected and can effectively reduce feature dimensionality while improving outcome prediction.

Proceedings Article | 27 February 2018 Paper

Deformable image registration as a tool to improve survival prediction after neoadjuvant chemotherapy for breast cancer: results from the ACRIN 6657/I-SPY-1 trial

Nariman Jahani, Eric Cohen, Meng-Kang Hsieh, Susan Weinstein, Lauren Pantalone, Christos Davatzikos, Despina Kontos

Proceedings Volume 10575, 105752S (2018) https://doi.org/10.1117/12.2293720

KEYWORDS: Image registration, Tumors, Statistical modeling, Breast cancer, Statistical analysis, Breast, Performance modeling, Magnetic resonance imaging, Feature extraction, Image segmentation

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SPIE Journal Paper | 11 January 2018 Open Access

Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome

Christos Davatzikos, Saima Rathore, Spyridon Bakas, Sarthak Pati, Mark Bergman, Ratheesh Kalarot, Patmaa Sridharan, Aimilia Gastounioti, Nariman Jahani, Eric Cohen, Hamed Akbari, Birkan Tunc, Jimit Doshi, Drew Parker, Michael Hsieh, Aristeidis Sotiras, Hongming Li, Yangming Ou, Robert K. Doot, Michel Bilello, Yong Fan, Russell T. Shinohara, Paul Yushkevich, Ragini Verma, Despina Kontos

JMI, Vol. 5, Issue 01, 011018, (January 2018) https://doi.org/10.1117/12.10.1117/1.JMI.5.1.011018

KEYWORDS: Tumors, Magnetic resonance imaging, Cancer, Breast, Analytics, Diagnostics, Image segmentation, Brain, Tissues, Feature extraction

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