Dr. Andrei S. Purysko Profile

Dr. Andrei S. Purysko

at Cleveland Clinic

SPIE Involvement:

Author

Publications (10)

Proceedings Article | 3 April 2024 Presentation + Paper

Human-in-the-loop informed deep learning rectal tumor segmentation on pre-treatment MRI

Michael Kong, Thomas DeSilvio, Leo Bao, Brennan Flannery, Benjamin Parker, Stephen Tang, Murad Labbad, Gregory O'Connor, Amit Gupta, Emily Steinhagen, Andrei Purysko, William Hall, David Liska, Eric Marderstein, Aaron Carroll, Marka Crittenden, Michael Gough, Kristina Young, Satish Viswanath

Proceedings Volume 12927, 1292707 (2024) https://doi.org/10.1117/12.3008637

KEYWORDS: Tumors, Image segmentation, Magnetic resonance imaging, Cancer, Deep learning, Medical research, Colorectal cancer

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

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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 | 4 April 2022 Presentation + Paper

Evaluating the sensitivity of deep learning to inter-reader variations in lesion delineations on bi-parametric MRI in identifying clinically significant prostate cancer

Ansh Roge, Amogh Hiremath, Michael Sobota, Sree Harsha Tirumani, Leonardo Kayat Bittencourt, Justin Ream, Ryan Ward, Halimat Olaniyan, Sadhna Verma, Andrei Purysko, Anant Madabhushi, Rakesh Shiradkar

Proceedings Volume 12033, 120330Z (2022) https://doi.org/10.1117/12.2613245

KEYWORDS: Principal component analysis, Magnetic resonance imaging, Prostate cancer, Biopsy, Medical research, Pathology, Prostate, Performance modeling, Diffusion, Convolutional neural networks

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Deep learning based convolutional neural networks (CNNs) for prostate cancer (PCa) risk stratification employ radiologist delineated regions of interest (ROIs) on MRI. These ROIs contain the reader’s interpretation of the region of PCa. Variations in reader annotations change the features that are extracted from the ROIs, which may in turn affect classification performance of CNNs. In this study, we sought to analyze the effect of variations in inter-reader delineations of PCa ROIs on training of CNNs with regards to distinguishing clinically significant (csPCa) and insignificant PCa (ciPCa). We employed 180 patient studies (n=274 lesions) from 3 cohorts who underwent 3T multi-parametric MRI followed by MRI-targeted biopsy and/or radical prostatectomy. ISUP Gleason grade groups (GGG) obtained from pathology were used to determine csPCa (GGG≥2) and ciPCa (GGG=1). 5 experienced radiologists, with over 5 years of experience in prostate imaging, delineated PCa ROIs on bi-parametric MRI (bpMRI including T2 weighted (T2W) and diffusion weighted (DWI) sequences) within the training set (n₁=160 lesions) using targeted biopsy locations. Patches were extracted using the ROIs which were then used to train individual CNNs (N₁-N₅) using the SqueezeNet architecture. The average volume for readerdelineated ROIs used for training varied greatly, ranging between 1106 and 2107 mm across all readers. The resulting networks showed no significant difference in classification performance (AUC= 0.82 ± 0.02) indicating that they were relatively robust to inter-reader variations in ROI. These models were evaluated on independent test sets (n₂=85 lesions, n₃=29 lesions) where ROIs were obtained by co-registration of MRI with post-surgical pathology, unaffected by inter-reader variations in ROIs. Network performance across D₂ and D₃ was 0.80±0.02 and 0.62 ± 0.03, respectively. The CNN predictions were moderately consistent, with ICC(2,1) scores across D₂ and D₃ being 0.74 and 0.54, respectively. Higher agreement in ROI overlap produced higher correlation in predictions on external test sets (R = 0.89, p < 0.05). Furthermore, higher average ROI volume produced greater AUC scores on D₃, indicating that comprehensive ROIs may provide more features for DL networks to use in classification. Inter-reader variations in ROIs on MRI may influence the reliability and generalizability of CNNs trained for PCa risk stratification.

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

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.

SPIE Journal Paper | 14 June 2019 Open Access

Multisite evaluation of radiomic feature reproducibility and discriminability for identifying peripheral zone prostate tumors on MRI

Prathyush Chirra, Patrick Leo, Michael Yim, B. Nicolas Bloch, Ardeshir R. Rastinehad, Andrei Purysko, Mark Rosen, Anant Madabhushi, Satish Viswanath

JMI, Vol. 6, Issue 02, 024502, (June 2019) https://doi.org/10.1117/12.10.1117/1.JMI.6.2.024502

KEYWORDS: Magnetic resonance imaging, Tumors, Prostate, Feature extraction, Scanners, Tissues, Prostate cancer, Computed tomography, Visualization, Magnetism

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Recent advances in the field of radiomics have enabled the development of a number of prognostic and predictive imaging-based tools for a variety of diseases. However, wider clinical adoption of these tools is contingent on their generalizability across multiple sites and scanners. This may be particularly relevant in the context of radiomic features derived from T1- or T2-weighted magnetic resonance images (MRIs), where signal intensity values are known to lack tissue-specific meaning and vary based on differing acquisition protocols between institutions. We present the first empirical study of benchmarking five different radiomic feature families in terms of both reproducibility and discriminability in a multisite setting, specifically, for identifying prostate tumors in the peripheral zone on MRI. Our cohort comprised 147 patient T2-weighted MRI datasets from four different sites, all of which are first preprocessed to correct for acquisition-related artifacts such as bias field, differing voxel resolutions, and intensity drift (nonstandardness). About 406 three-dimensional voxel-wise radiomic features from five different families (gray, Haralick, gradient, Laws, and Gabor) were evaluated in a cross-site setting to determine (a) how reproducible they are within a relatively homogeneous nontumor tissue region and (b) how well they could discriminate tumor regions from nontumor regions. Our results demonstrate that a majority of the popular Haralick features are reproducible in over 99% of all cross-site comparisons, as well as achieve excellent cross-site discriminability (classification accuracy of ≈0.8). By contrast, a majority of Laws features are highly variable across sites (reproducible in <75 % of all cross-site comparisons) as well as resulting in low cross-site classifier accuracies (<0.6), likely due to a large number of noisy filter responses that can be extracted. These trends suggest that only a subset of radiomic features and associated parameters may be both reproducible and discriminable enough for use within machine learning classifier schemes.

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Proceedings Article | 13 March 2019 Paper

Radiomic features derived from pre-operative multi-parametric MRI of prostate cancer are associated with Decipher risk score

Lin Li, Rakesh Shiradkar, Ahmad Algohary, Patrick Leo, Cristina Magi-Galluzzi, Eric Klein, Andrei Purysko, Anant Madabhushi

Proceedings Volume 10950, 109503Y (2019) https://doi.org/10.1117/12.2512606

KEYWORDS: Feature extraction, Prostate cancer, Cancer, Magnetic resonance imaging, Pathology, Prostate, Tissues, Diffusion, Image registration

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Decipher, a genomic test, is used to predict the likelihood of metastasis and prostate cancer (PCa) specific mortality based on expression patterns of 22 RNA markers from radical prostatectomy (RP) specimens. It has been shown to be strongly correlated with metastasis-free prognosis and has been integrated with the National Comprehensive Cancer Network (NCCN) guidelines. However, Decipher is expensive and tissue destructive. Radiomic features refer to the high-throughput computational texture or shape features extracted from radiographic scans. Radiomic features derived from multi-parametric magnetic resonance imaging (mpMRI) of prostate cancer have been shown to be associated with clinically significant PCa. In this study, we sought to evaluate whether radiomic features derived from T2-weighted MRI (T2WI) and apparent diffusion coefficient (ADC) maps of the prostate could distinguish different Decipher risk groups (low, intermediate and high). We also explored correlations between Decipher risk associated radiomic features and features relating to gland morphology on corresponding digitized surgical specimens. A retrospectively acquired, de-identified cohort of 70 PCa patients (N = 74 lesions) who underwent 3T mpMRI prior to RP and Decipher tests after RP were used in this study. The Decipher risk score, ranging from 0 to 1, was used to categorize patients into low/intermediate (D₁) and high (D₂) risk groups. A multivariate logistic regression model was trained (N = 37 lesions) using radiomic features selected via elastic-net regularization to predict the Decipher risk groups. The model was evaluated on a hold-out test set (N = 37 lesions) and resulted in an area under the receiver operating characteristic curve (AUC) = 0:80. Our model outperformed the prediction using PIRADS v2 (AUC = 0:67), but showed comparable performance with Gleason Grade Group (GGG) (AUC = 0:80). We observed that the best discriminating radiomic features were correlated with gland morphology and gland packing on corresponding histopathology (R = 0.43, p < 0.05).

Proceedings Article | 8 March 2019 Paper

Radiomic characterization of perirectal fat on MRI enables accurate assessment of tumor regression and lymph node metastasis in rectal cancers after chemoradiation

Michael Yim, Zhouping Wei, Jacob Antunes, Neil K. Sehgal, Kaustav Bera, Justin Brady, Kenneth Friedman, Joseph Willis, Andrei Purysko, Raj Paspulati, Anant Madabhushi, Satish Viswanath

Proceedings Volume 10951, 109512A (2019) https://doi.org/10.1117/12.2512612

KEYWORDS: Tumors, Magnetic resonance imaging, Cancer, Lymphatic system, Medical research, Feature extraction, Feature selection, Data analysis, Oncology

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Proceedings Article | 27 February 2018 Presentation + Paper

Empirical evaluation of cross-site reproducibility in radiomic features for characterizing prostate MRI

Prathyush Chirra, Patrick Leo, Michael Yim, B. Nicolas Bloch, Ardeshir Rastinehad, Andrei Purysko, Mark Rosen, Anant Madabhushi, Satish Viswanath

Proceedings Volume 10575, 105750B (2018) https://doi.org/10.1117/12.2293992

KEYWORDS: Machine learning, Magnetism, Data acquisition, Image resolution, Tissues, Scanners, Computed tomography, Feature extraction, Prostate, Magnetic resonance imaging

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