Ms. Hira Shahzad Sardar Profile

Hira Shahzad Sardar

at Dartmouth College

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Publications (5)

Proceedings Article | 15 March 2023 Presentation + Paper

Correlation of fluorescence optomics method classification performance to varying expression level of epidermal growth factor receptor

Yao Chen, Samuel Streeter, Brady Hunt, Hira Sardar, Jason Gunn, Laura Tafe, Joseph Paydarfar, Keith Paulsen, Brian Pogue, Kimberley Samkoe

Proceedings Volume 12361, 123610G (2023) https://doi.org/10.1117/12.2649399

KEYWORDS: Tumors, Fluorescence, Surgery, Fluorescence imaging, Cancer detection, Radiomics, Image classification, Correlation coefficients, Machine learning

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Fluorescence molecular imaging using ABY-029, an epidermal growth factor receptor (EGFR)-targeted synthetic Affibody peptide labeled with a near-infrared fluorophore, is under investigation for surgical guidance during head and neck squamous cell carcinoma (HNSCC) resection. However, tumor-to-normal tissue contrast is confounded by intrinsic physiological limitations of heterogeneous EGFR expression. In this study, a machine learning-based optomics analysis, which interprets the textural pattern differences in EGFR expression conveyed by fluorescence, was applied to optical ABY-029 fluorescence image data of HNSCC surgical specimens. The study objective was to determine the correlations between optomics method classification performance and tissue inherent EGFR expression level. Fluorescence image data were collected through a Phase 0 clinical trial of ABY-029, which involved a total of 20,073 sub-image patches (size of 1.8×1.8 mm2) extracted from 24 bread-loafed slices of HNSCC surgical resections from 12 patients who were stratified into three dose groups (30, 90, and 171 nanomoles). The optomics approach utilized a supervised machine learning pipeline. Each dose group was randomly partitioned on the specimen-level 75%/25% into training/testing sets, then all training and testing sets were aggregated. A total of 1,472 standardized optomic features were extracted from each patch and evaluated by minimum redundancy maximum relevance feature selection, and 25 top-ranked features were used to train a support vector machine classifier. A conceptual framework of correlation analysis to evaluate the relationship between optomics tumor classification performance and underlying EGFR expression level was provided, but the present results are underpowered. Some generalized conclusions about the ABY-029 fluorescence optomics method correlating to varied levels of EGFR expression were summarized, suggesting that optomics method using fluorescence molecular imaging data offers a potentially stable image analysis technique for cancer detection for fluorescence-guided surgery applications; however, further study with additional samples is needed to validate this conclusion.

Proceedings Article | 19 February 2020 Presentation + Paper

Perspectives on the phase 0 clinical trial of microdose administration of ABY-029 for fluorescence guided surgery: stability testing

Kimberley Samkoe, Sally Hull, Jonathan Elliott, Hira Shahzad Sardar, Jason Gunn, Konstantinos Linos, Laura Tafe, Brent Harris, Joachim Feldwisch, Brian Pogue, David Roberts, Eric Henderson, Joseph Paydarfar, Keith Paulsen

Proceedings Volume 11222, 112220X (2020) https://doi.org/10.1117/12.2547110

KEYWORDS: Molecular imaging, Clinical trials, Luminescence, Surgery, Proteins, Manufacturing, Receptors, Absorbance

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Proceedings Article | 22 May 2019 Presentation + Paper

Measuring microdose ABY-029 fluorescence signal in a primary human soft-tissue sarcoma resection

Kimberley Samkoe, Hira Shahzad Sardar, Jason Gunn, Joachim Feldwisch, Konstantinos Linos, Eric Henderson, Brian Pogue, Keith Paulsen

Proceedings Volume 10862, 1086212 (2019) https://doi.org/10.1117/12.2510935

KEYWORDS: Luminescence, Tissues, Tumors, Imaging systems, Surgery, Pathology, Scanners, Receptors, Cancer, Signal to noise ratio

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Proceedings Article | 7 March 2019 Presentation + Paper

Comparison of detection sensitivity of near infrared (NIR) surgical imaging systems using a connective tissue phantom model

Hira Shahzad Sardar, Qais Zai, Jason Gunn, Brian Pogue, Keith Paulsen, Kimberley Samkoe, Eric Henderson

Proceedings Volume 10862, 108620Z (2019) https://doi.org/10.1117/12.2510650

KEYWORDS: Surgery, Tumors, Blood, Tissues, Near infrared, Visualization, Systems modeling, Imaging systems, Thermal modeling

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Proceedings Article | 4 March 2019 Presentation

comparison of fluorescence guidance systems for ICG & IRDye800 imaging (Conference Presentation)

Jason Gunn, Alberto Ruiz, Hira Shahzad Sardar, Kimberley Samkoe, Brian Pogue

Proceedings Volume 10862, 108620F (2019) https://doi.org/10.1117/12.2511487

KEYWORDS: Imaging systems, Luminescence, Clinical trials, Tissues, Proteins, In vivo imaging, Lymphatic system

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