Dr. Sharon L. Stein Profile

Dr. Sharon L. Stein

at Univ. Hospitals Cleveland Medical Ctr

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Author

Publications (3)

Proceedings Article | 3 April 2023 Presentation + Paper

Integrating multi-plane and multi-region radiomic features to predict pathologic response to neoadjuvant chemoradiation in rectal cancers via pre-treatment MRI

Thomas DeSilvio, Leo Bao, Dhruv Seth, Prathyush Chirra, Sneha Singh, Atreya Sridharan, Murad Labbad, Katie Bingmer, Diana Jodeh, Eric Marderstein, Rajmohan Paspulati, David Liska, Kenneth Friedman, Smitha Krishnamurthi, Sharon Stein, Andrei Purysko, Satish Viswanath

Proceedings Volume 12466, 124660K (2023) https://doi.org/10.1117/12.2655787

KEYWORDS: Tumors, Radiomics, Magnetic resonance imaging, Cancer, Oncology, Feature extraction, Visualization, Performance modeling, Surgery, Rectum

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Proceedings Article | 4 April 2022 Presentation + Paper

Deep hybrid convolutional wavelet networks: application to predicting response to chemoradiation in rectal cancers via MRI

Amir Reza Sadri, Thomas DeSilvio, Prathyush Chirra, Andrei Purysko, Rajmohan Paspulati, Kenneth Friedman, Smitha Krishnamurthi, David Liska, Sharon Stein, Satish Viswanath

Proceedings Volume 12033, 120330H (2022) https://doi.org/10.1117/12.2613035

KEYWORDS: Wavelets, Magnetic resonance imaging, Cancer, Tumors, 3D modeling, Network architectures, Neural networks, Data modeling, Oncology, Neurons

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Proceedings Article | 16 March 2020 Presentation + Paper

Multi-site evaluation of stable radiomic features for more accurate evaluation of pathologic downstaging on MRI after chemoradiation for rectal cancers

Amrish Selvam, Jacob Antunes, Kaustav Bera, Asya Ofshteyn, Justin Brady, Katherine Bingmer, Kenneth Friedman, Sharon Stein, Rajmohan Paspulati, Andrei Purysko, Matthew Kalady, Anant Madabhushi, Satish Viswanath

Proceedings Volume 11314, 113140V (2020) https://doi.org/10.1117/12.2549085

KEYWORDS: Cancer, Tumors, Magnetic resonance imaging, Rectum, Feature selection, Feature extraction, Visualization, Oncology, CRTs, Tissues

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Tumor downstaging after neoadjuvant chemoradiation (CRT) in rectal cancer patients is typically assessed via Magnetic Resonance Imaging (MRI) in order to determine follow-up surgical interventions, but is associated with marked inter-reader variability and limited performance. While radiomic features have shown promise for evaluating chemoradiation response and tumor stage in rectal cancers, there is a need to determine how reproducible these features are across different MRI scanners and acquisitions. In this study, we evaluated radiomic feature reproducibility in terms of feature instability within a uniquely curated true healthy" rectum cohort in order to construct a stability-informed radiomic classifier for differentiating poorly from markedly down-staged rectal tumors after chemoradiation in a multi-site setting. We utilized a cohort of 156 patients, with (a) 74 MRIs visualizing the healthy rectum, (b) 52 post-CRT MRI scans in the discovery cohort, and (c) 30 post-CRT MRI scans in a second-site validation cohort; the latter 2 being from rectal cancer patients. 764 radiomic features were extracted from within the entire rectal wall on each MRI scan. Feature instability was used to quantify how reproducible each radiomic feature was between the discovery cohort and the healthy rectum cohort, using locations along the rectum that were spatially distinct from the treated tumor region. From the resulting stability-informed" feature set, the most relevant features were identified to distinguish pathologic tumor stage groups in the discovery cohort via a QDA classifier with cross-validation to ensure robustness. The top 4 radiomic features were then evaluated in hold-out fashion on scans from the validation cohort. We found that utilizing a stability-informed radiomic model (which comprised features that were reproducible in 100% of all comparisons) was significantly more accurate in identifying pathological tumor stage regression in both discovery (AUC=0:66 ± 0:09) and validation (AUC=0.73) cohorts, compared to a basic radiomic model that used all extracted features (AUC=0:60 ± 0:07 in discovery, AUC=0.62 in validation). Evaluating feature instability with respect to healthy rectal tissue may thus enhance the performance of radiomic models in characterizing pathologic downstaging in rectal cancers, via MRI.

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