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 (MICCAI (2), 2017)

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 (Computer-Aided Diagnosis, 2018)

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 (Computer-Aided Diagnosis, 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 (Image-Guided Procedures, 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)

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 (Computer-Aided Diagnosis, 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; et al. (Computer-Aided Diagnosis, 2020)

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 (Computer-Aided Diagnosis, 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 (Computer-Aided Diagnosis, 2022)

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; et al. (Image-Guided Procedures, 2023)