Andrei S. Purysko

According to our database, Andrei S. Purysko authored at least 10 papers between 2017 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2023

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, Katherine Bingmer, Diana Jodeh, Eric L. Marderstein, Rajmohan Paspulati, David Liska, Kenneth Friedman, Smitha S. Krishnamurthi, Sharon L. Stein, Andrei S. Purysko, Satish E. Viswanath

Proceedings of the Medical Imaging 2023: Image-Guided Procedures, 2023

2022

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

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

Proceedings of the Medical Imaging 2022: Computer-Aided Diagnosis, 2022

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 S. Purysko, Anant Madabhushi, Rakesh Shiradkar

Proceedings of the Medical Imaging 2022: Computer-Aided Diagnosis, 2022

2020

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 T. Brady, Katherine Bingmer, Kenneth Friedman, Sharon L. Stein, Rajmohan Paspulati, Andrei S. Purysko, Matthew Kalady, Anant Madabhushi, Satish E. Viswanath

Proceedings of the Medical Imaging 2020: Computer-Aided Diagnosis, 2020

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, Ardeshir R. Rastinehad, Andrei S. Purysko, Anant Madabhushi

Proceedings of the Medical Imaging 2020: Computer-Aided Diagnosis, 2020

2019

Comparing radiomic classifiers and classifier ensembles for detection of peripheral zone prostate tumors on T2-weighted MRI: a multi-site study.

Satish Viswanath, Prathyush Chirra, Michael Yim, Neil M. Rofsky, Andrei S. Purysko, Mark A. Rosen, B. Nicolas Bloch, Anant Madabhushi

BMC Medical Imaging, 2019

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

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

Proceedings of the Medical Imaging 2019: Image-Guided Procedures, 2019

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 S. Purysko, Anant Madabhushi

Proceedings of the Medical Imaging 2019: Computer-Aided Diagnosis, 2019

2018

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

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

Proceedings of the Medical Imaging 2018: Computer-Aided Diagnosis, 2018

2017

Field Effect Induced Organ Distension (FOrge) Features Predicting Biochemical Recurrence from Pre-treatment Prostate MRI.

Soumya Ghose, Rakesh Shiradkar, Mirabela Rusu, Jhimli Mitra, Rajat Thawani, Michael D. Feldman, Amar Gupta, Andrei S. Purysko, Lee Ponsky, Anant Madabhushi

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2017, 2017