Mr. Nathaniel M. Braman Profile

Nathaniel M. Braman

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Publications (5)

Proceedings Article | 16 March 2020 Presentation + Paper

A combination of intra- and peri-tumoral deep features from prostate bi-parametric MRI can distinguish clinically significant and insignificant prostate cancer

Amogh Hiremath, Rakesh Shiradkar, Nathaniel Braman, Prateek Prasanna, Art Rastinehad, Andrei Purysko, Anant Madabhushi

Proceedings Volume 11314, 113140M (2020) https://doi.org/10.1117/12.2550963

KEYWORDS: Magnetic resonance imaging, Prostate cancer, Feature extraction, Convolutional neural networks, Prostate, Diffusion weighted imaging

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Proceedings Article | 16 March 2020 Presentation + Paper

Computer extracted features related to the spatial arrangement of tumor-infiltrating lymphocytes predict overall survival in epithelial ovarian cancer receiving adjuvant chemotherapy

Sepideh Azarianpour, Germán Corredor, Kaustav Bera, Patrick Leo, Nathaniel Braman, Pingfu Fu, Haider Mahdi, Anant Madabhushi

Proceedings Volume 11320, 113200Q (2020) https://doi.org/10.1117/12.2550188

KEYWORDS: Tumors, Ovarian cancer, Cancer, Feature extraction, Lung cancer, Statistical analysis, Tumor growth modeling, Tissues, Data modeling, Medical research

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Proceedings Article | 16 March 2020 Presentation + Paper

Computationally derived cytological image markers for predicting risk of relapse in acute myeloid leukemia patients following bone marrow transplantation

Sara ArabYarmohammadi, Zelin Zhang, Patrick Leo, Marjan Firouznia, Andrew Janowczyk, Haojia Li, Nathaniel Braman, Kaustav Bera, Behtash Nezami, Howard Meyerson, Jun Xu, Leland Metheny, Anant Madabhushi

Proceedings Volume 11320, 1132004 (2020) https://doi.org/10.1117/12.2549730

KEYWORDS: Fractal analysis, Image segmentation, Feature extraction, Bone, Leukemia, Blood, Shape analysis, Visualization, Transplantation, Visual process modeling

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Proceedings Article | 15 March 2019 Paper

Morphology of vascular network in eyes with diabetic macular edema varies based on tolerance of aflibercept treatment interval length: preliminary findings

Prateek Prasanna, Justis Ehlers, Nathaniel Braman, Natalia Figueredo, Vishal Bobba, Sumit Sharma, Sunil Srivastava, Anant Madabhushi

Proceedings Volume 10953, 1095312 (2019) https://doi.org/10.1117/12.2513419

KEYWORDS: Angiography, Visualization, Tolerancing, Ischemia, Eye, Hough transforms, Medical research, Lung cancer, Blood, Capillaries

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Diabetic macular edema is a leading cause of vision loss in diabetic patients. The underlying cause for the onset of DME is 1) the long term presence of hyperglycemia and the eventual degradation of the blood-retinal barrier (BRB) via an uptick in vascular endothelial growth factor (VEGF); VEGF increases the permeability of the blood retinal barrier and alters the length of capillaries, thereby inhibiting the ability of these vessels in performing their primary function of filtration. The lack of a proper filtration system in combination with the ongoing change in intra-retinal vasculature that stems from it, results in the eventual loss of visual acuity in DME patients. Due to the large role in which VEGF plays in acting as a catalyst for the onset of DME, current treatments now focus on utilizing anti-VEGF therapy as a first line treatment for DME. Anti-VEGF therapy improves clinical outcomes in the form of improved visual acuity and reduction in macular edema. Anti-VEGF treatments also have a peripheral effect of modifying the disease burden and allowing for extended time in between treatments. However, there is still a void in understanding how anti-VEGF affects the underlying pathophysiology. This study focuses on using quantification of the geometric properties of vasculature on Fluorescein Angiography(FA) to understand the impact anti-VEGF treatment has on retinal vascular dynamics. We hypothesize that vasculature disorder, due to VEGF action, differs across patients and can be modeled mathematically to identify candidates for anti-VEGF treatment. We use VaNgOGH, a Hough transform-based descriptor to model the disorder of the retinal vascular network on baseline FA of patients subsequently treated with intravitreal anti-VEGF therapy (aibercept). VaNgOGH computes local measures of vessel-curvature and identifies dominant peaks in the accumulator space. We explored the differences in such features on baseline FA between eyes tolerating extended dosing interval (N=15) and those eyes requiring more frequent dosing (N=12), based on initial response following treatment interval extension. The cross-validated AUC was found to be 0.73±0.1 using VaNgOGH. The variance of local orientations showed a statistically significant difference (p=0.008) between the two categories, unlike clinical parameters on baseline OCT. Our results suggest there may be fundamental differences in localized vessel orientations between eyes that will exhibit favorable response to extended interval aibercept dosing and eyes that require more frequent dosing.

Proceedings Article | 27 February 2018 Presentation + Paper

A deep learning classifier for prediction of pathological complete response to neoadjuvant chemotherapy from baseline breast DCE-MRI

Kavya Ravichandran, Nathaniel Braman, Andrew Janowczyk, Anant Madabhushi

Proceedings Volume 10575, 105750C (2018) https://doi.org/10.1117/12.2294056

KEYWORDS: Tumors, Breast, Receptors, Magnetic resonance imaging, Breast cancer, Network architectures, Surgery, Cancer, Lymphatic system, Visualization

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