Dr. Meng-Kang Hsieh Profile

Dr. Meng-Kang Hsieh

Senior Data Analyst at Penn Medicine

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Publications (13)

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

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 | 13 March 2019 Paper

Associations between mammographic phenotypes and histopathologic features in ductal carcinoma in situ

Ruvini Navaratna, Aimilia Gastounioti, Meng-Kang Hsieh, Lauren Pantalone, Marie Shelanski, Emily Conant, Despina Kontos

Proceedings Volume 10950, 109502K (2019) https://doi.org/10.1117/12.2512464

KEYWORDS: Breast, Breast cancer, Receptors, Mammography, Statistical analysis, Principal component analysis, Biopsy, Feature extraction

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Proceedings Article | 6 July 2018 Paper

Validation of the textural realism of a 3D anthropomorphic phantom for digital breast tomosynthesis

Raymond Acciavatti, Meng-Kang Hsieh, Aimilia Gastounioti, Yifan Hu, Jinbo Chen, Andrew D. Maidment, Despina Kontos

Proceedings Volume 10718, 107180R (2018) https://doi.org/10.1117/12.2318029

KEYWORDS: Digital breast tomosynthesis, Mammography, Breast, Image analysis, Cancer, Image acquisition, 3D acquisition, 3D image reconstruction

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Proceedings Article | 22 March 2018 Paper

Automatic segmentation of fibroglandular tissue in breast MRI using anatomy-driven three-dimensional spatial context

Dong Wei, Susan Weinstein, Meng-Kang Hsieh, Lauren Pantalone, Despina Kontos

Proceedings Volume 10574, 105742X (2018) https://doi.org/10.1117/12.2292483

KEYWORDS: Breast, Magnetic resonance imaging, Image segmentation, Breast cancer, Wavelet transforms

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Proceedings Article | 2 March 2018 Paper

Volumetric versus area-based density assessment: comparisons using automated quantitative measurements from a large screening cohort

Aimilia Gastounioti, Meng-Kang Hsieh, Lauren Pantalone, Emily Conant, Despina Kontos

Proceedings Volume 10574, 105742H (2018) https://doi.org/10.1117/12.2293051

KEYWORDS: Breast, Digital breast tomosynthesis, Breast cancer, Digital mammography, Image segmentation

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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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Proceedings Article | 3 March 2017 Presentation + Paper

Mammographic phenotypes of breast cancer risk driven by breast anatomy

Aimilia Gastounioti, Andrew Oustimov, Meng-Kang Hsieh, Lauren Pantalone, Emily Conant, Despina Kontos

Proceedings Volume 10134, 1013417 (2017) https://doi.org/10.1117/12.2254630

KEYWORDS: Nipple, Volume rendering, Image segmentation, Data modeling, Breast cancer, Digital mammography

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Proceedings Article | 3 March 2017 Presentation + Paper

Convolutional neural network approach for enhanced capture of breast parenchymal complexity patterns associated with breast cancer risk

Andrew Oustimov, Aimilia Gastounioti, Meng-Kang Hsieh, Lauren Pantalone, Emily Conant, Despina Kontos

Proceedings Volume 10134, 101340S (2017) https://doi.org/10.1117/12.2254506

KEYWORDS: Convolutional neural networks, Computer architecture, Receivers, Breast cancer, Neural networks, Digital mammography, Image fusion

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Proceedings Article | 24 February 2017 Presentation + Paper

Three-dimensional whole breast segmentation in sagittal MR images with dense depth field modeling and localized self-adaptation

Dong Wei, Susan Weinstein, Meng-Kang Hsieh, Lauren Pantalone, Mitchell Schnall, Despina Kontos

Proceedings Volume 10133, 1013314 (2017) https://doi.org/10.1117/12.2248626

KEYWORDS: Image segmentation, Breast, Magnetic resonance imaging, 3D image processing, 3D modeling, Mammography, Chest, Visual process modeling, Quantitative analysis, Breast cancer

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Proceedings Article | 24 March 2016 Paper

Parenchymal texture measures weighted by breast anatomy: preliminary optimization in a case-control study

Aimilia Gastounioti, Brad Keller, Meng-Kang Hsieh, Emily Conant, Despina Kontos

Proceedings Volume 9785, 97850M (2016) https://doi.org/10.1117/12.2217697

KEYWORDS: Breast, Breast cancer, Digital mammography, Mammography, Feature extraction, Volume rendering, Image segmentation, Nipple

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Showing 5 of 13 publications

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