Dr. Michelle Hershman Profile

Dr. Michelle Hershman

at Univ of Pennsylvania

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Publications (3)

Proceedings Article | 27 May 2022 Presentation + Paper

Multimodal radiothermomic biomarkers for breast cancer screening

Madeline Barry, Mélina Khansari, Patrick Tallon, Carina Carino, Michelle Hershman, Henrique Fernandes, Omid Maghsoudi, Xavier P. Maldague, Bardia Yousefi

Proceedings Volume 12109, 121090F (2022) https://doi.org/10.1117/12.2631751

KEYWORDS: Thermography, Breast cancer, Mammography, Breast, Thermal modeling, Diagnostics, Multimodal imaging, Cancer, Biopsy, Stochastic processes

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Proceedings Article | 4 April 2022 Presentation + Paper

Resampling and harmonization for mitigation of heterogeneity in imaging parameters: a comparative study

Apurva Singh, Hannah Horng, Leonid Roshkovan, Michelle Hershman, Russell Shinohara, Sharyn Katz, Despina Kontos

Proceedings Volume 12033, 1203336 (2022) https://doi.org/10.1117/12.2611467

KEYWORDS: Image acquisition, Statistical modeling, Feature extraction, Tumors, Image contrast enhancement, Medical imaging, Image enhancement, CT reconstruction, Resolution enhancement technologies, Image resolution

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We compare techniques for addressing heterogeneity in image physical dimensions and acquisition parameters, and how these methods affect the predictive performance of radiomic features. We further combine radiomic signatures with established clinical prognostic factors to predict progression-free survival (PFS) in stage four NSCLC patients undergoing first-line immunotherapy. Our study includes 124 stage 4 NSCLC patients treated with pembrolizumab (monotherapy:30.65%, combination therapy:69.35%). The Captk software was used to extract radiomic features (n=102) from 3D tumor volumes segmented from lung CT scans with ITK-SNAP. The ability of the following approaches to mitigate the heterogeneity in image physical dimensions (voxel spacing parameters) and acquisition parameters (contrast enhancement and CT reconstruction kernel) were evaluated: resampling the images (to minimum/maximum voxel spacing parameters), harmonization of radiomic features using a nested ComBat technique (taking voxel spacing and/or image acquisition parameters as batch variables) or a combination of resampling the images to the minimum voxel spacing parameters and applying nested harmonization by image acquisition parameters. Two radiomic phenotypes were identified using unsupervised hierarchical clustering of the extracted radiomic features derived from each of these scenarios. Established prognostic factors, including PDL1 expression, ECOG status, BMI and smoking status, were combined with radiomic phenotypes in five-fold cross-validated multivariate Cox proportional hazards models (200 iterations) of progression-free survival. A Cox model based only on clinical factors had a cstatistic (mean, 95% CI) of 0.53[0.50,0.57], which increased to 0.62[0.55,0.64] upon the addition of radiomic phenotypes derived from images which had been resampled to minimum voxel spacing and harmonized by image acquisition parameters. In addition to the cross-validated cstatistics, we also built a model on the complete dataset of features corresponding to each of the approaches to evaluate the Kaplan Meier performance in separating patients above versus below the median prognostic score. This preliminary study aims to draw comparisons between the various techniques used to address the issue of reproducibility in radiomic features derived from medical images with heterogeneous parameters.

Proceedings Article | 15 February 2021 Poster + Presentation + Paper

Radiomic features predict local failure-free survival in stage III NSCLC adenocarcinoma treated with chemoradiation

José Marcio Luna, Andrew Barsky, Russell Shinohara, Alexandra Dreyfuss, Leonid Roshkovan, Michelle Hershman, Babak Haghighi, Bardia Yousefi, Peter Noel, Keith Cengel, Sharyn Katz, Eric Diffenderfer, Despina Kontos

Proceedings Volume 11597, 1159720 (2021) https://doi.org/10.1117/12.2581873

KEYWORDS: Computed tomography, Tumors, Lung cancer, Feature extraction, Principal component analysis, Failure analysis, Biopsy

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