Center for Computational Imaging and Personalized Diagnostics

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2021

Hiremath, A, Shiradkar R, Fu P, Mahran A, Rastinehad AR, Tewari A, Tirumani S, Purysko A, Ponsky L, Madabhushi A. 2021. An integrated nomogram combining deep learning, Prostate Imaging-Reporting and Data System (PI-RADS) scoring, and clinical variables for identification of clinically significant prostate cancer on biparametric MRI: a retrospective multicentre study.. The Lancet. Digital health. 3(7):e445-e454. Abstract

Liu, JTC, Glaser AK, Bera K, True LD, Reder NP, Eliceiri KW, Madabhushi A. 2021. Harnessing non-destructive 3D pathology.. Nature biomedical engineering. 5(3):203-218. Abstract

2020

Unternaehrer, J, Grobholz R, Janowczyk A, Zlobec I. 2020. Current opinion, status and future development of digital pathology in Switzerland.. Journal of clinical pathology. 73(6):341-346. Abstract

Zhao, Z, Bian Y, Jiang H, Fang X, Li J, Cao K, Ma C, Wang L, Zheng J, Yue X et al.. 2020. CT-Radiomic Approach to Predict G1/2 Nonfunctional Pancreatic Neuroendocrine Tumor.. Academic radiology. 27(12):e272-e281. Abstract

Lu, C, Bera K, Wang X, Prasanna P, Xu J, Janowczyk A, Beig N, Yang M, Fu P, Lewis J et al.. 2020. A prognostic model for overall survival of patients with early-stage non-small cell lung cancer: a multicentre, retrospective study.. The Lancet. Digital health. 2(11):e594-e606. Abstract

Bhargava, HK, Leo P, Elliott R, Janowczyk A, Whitney J, Gupta S, Fu P, Yamoah K, Khani F, Robinson BD et al.. 2020. Computationally Derived Image Signature of Stromal Morphology Is Prognostic of Prostate Cancer Recurrence Following Prostatectomy in African American Patients.. Clinical cancer research : an official journal of the American Association for Cancer Research. 26(8):1915-1923. Abstract

Sandulache, VC, Lei YL, Heasley LE, Chang M, Amos CI, Sturgis EM, Graboyes E, Chiao EY, Rogus-Pulia N, Lewis J et al.. 2020. Innovations in risk-stratification and treatment of Veterans with oropharynx cancer; roadmap of the 2019 Field Based Meeting.. Oral oncology. 102:104440.

Madabhushi, A, Feldman MD, Leo P. 2020. Deep-learning approaches for Gleason grading of prostate biopsies.. The Lancet. Oncology. 21(2):187-189.

Khorrami, MH, Prasanna P, Gupta A, Patil P, Velu PD, Thawani R, Corredor G, Alilou M, Bera K, Fu P et al.. 2020. Changes in CT Radiomic Features Associated with Lymphocyte Distribution Predict Overall Survival and Response to Immunotherapy in Non-Small Cell Lung Cancer.. Cancer immunology research. 8(1):108-119. Abstract

2019

Bera, K, Schalper KA, Rimm DL, Velcheti V, Madabhushi A. 2019. Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology.. Nature reviews. Clinical oncology. 16(11):703-715. Abstract

Li, H, Whitney J, Bera K, Gilmore H, Thorat MA, Badve S, Madabhushi A. 2019. Quantitative nuclear histomorphometric features are predictive of Oncotype DX risk categories in ductal carcinoma in situ: preliminary findings.. Breast cancer research : BCR. 21(1):114. Abstract

Khorrami, MH, Jain P, Bera K, Alilou M, Thawani R, Patil P, Ahmad U, Murthy S, Stephans K, Fu P et al.. 2019. Predicting pathologic response to neoadjuvant chemoradiation in resectable stage III non-small cell lung cancer patients using computed tomography radiomic features.. Lung cancer (Amsterdam, Netherlands). 135:1-9. Abstract

Feeny, AK, Rickard J, Patel D, Toro S, Trulock KM, Park CJ, LaBarbera MA, Varma N, Niebauer MJ, Sinha S et al.. 2019. Machine Learning Prediction of Response to Cardiac Resynchronization Therapy: Improvement Versus Current Guidelines.. Circulation. Arrhythmia and electrophysiology. 12(7):e007316. Abstract

Vamathevan, J, Clark D, Czodrowski P, Dunham I, Ferran E, Lee G, Li B, Madabhushi A, Shah P, Spitzer M et al.. 2019. Applications of machine learning in drug discovery and development.. Nature reviews. Drug discovery. 18(6):463-477. Abstract

Braman, N, Prasanna P, Whitney J, Singh S, Beig N, Etesami M, Bates DDB, Gallagher K, Bloch NB, Vulchi M et al.. 2019. Association of Peritumoral Radiomics With Tumor Biology and Pathologic Response to Preoperative Targeted Therapy for HER2 (ERBB2)-Positive Breast Cancer.. JAMA network open. 2(4):e192561. Abstract

Janowczyk, A, Zuo R, Gilmore H, Feldman M, Madabhushi A. 2019. HistoQC: An Open-Source Quality Control Tool for Digital Pathology Slides.. JCO clinical cancer informatics. 3:1-7. Abstract

2018

Whitney, J, Corredor G, Janowczyk A, Ganesan S, Doyle S, Tomaszewski J, Feldman M, Gilmore H, Madabhushi A. 2018. Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer.. BMC cancer. 18(1):610. Abstract

Bera, K, Velcheti V, Madabhushi A. 2018. Novel Quantitative Imaging for Predicting Response to Therapy: Techniques and Clinical Applications.. American Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting. 38:1008-1018. Abstract

Peyster, EG, Madabhushi A, Margulies KB. 2018. Advanced Morphologic Analysis for Diagnosing Allograft Rejection: The Case of Cardiac Transplant Rejection.. Transplantation. 102(8):1230-1239. Abstract

Nirschl, JJ, Janowczyk A, Peyster EG, Frank R, Margulies KB, Feldman MD, Madabhushi A. 2018. A deep-learning classifier identifies patients with clinical heart failure using whole-slide images of H&E tissue.. PloS one. 13(4):e0192726. Abstract

Cruz-Roa, A, Gilmore H, Basavanhally A, Feldman M, Ganesan S, Shih N, Tomaszewski J, Madabhushi A, González F. 2018. High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: Application to invasive breast cancer detection.. PloS one. 13(5):e0196828. Abstract

Penzias, G, Singanamalli A, Elliott R, Gollamudi J, Shih N, Feldman M, Stricker PD, Delprado W, Tiwari S, Böhm M et al.. 2018. Identifying the morphologic basis for radiomic features in distinguishing different Gleason grades of prostate cancer on MRI: Preliminary findings.. PloS one. 13(8):e0200730. Abstract

2017

2016

Toth, R, Sperling D, Madabhushi A. 2016. Quantifying Post- Laser Ablation Prostate Therapy Changes on MRI via a Domain-Specific Biomechanical Model: Preliminary Findings.. PloS one. 11(4):e0150016. Abstract

2014

Agner, SC, Rosen MA, Englander S, Tomaszewski JE, Feldman MD, Zhang P, Mies C, Schnall MD, Madabhushi A. 2014. Computerized image analysis for identifying triple-negative breast cancers and differentiating them from other molecular subtypes of breast cancer on dynamic contrast-enhanced MR images: a feasibility study.. Radiology. 272(1):91-99. Abstract

2013

Agner, SC, Xu J, Madabhushi A. 2013. Spectral embedding based active contour (SEAC) for lesion segmentation on breast dynamic contrast enhanced magnetic resonance imaging.. Medical physics. 40(3):032305. Abstract

Ghaznavi, F, Evans A, Madabhushi A, Feldman MD. 2013. Digital imaging in pathology: whole-slide imaging and beyond.. Annual review of pathology. 8:331-59. Abstract

Tiwari, P, Kurhanewicz J, Madabhushi A. 2013. Multi-kernel graph embedding for detection, Gleason grading of prostate cancer via MRI/MRS.. Medical image analysis. 17(2):219-35. Abstract

2012

Doyle, S, Feldman MD, Tomaszewski JE, Madabhushi A. 2012. A boosted Bayesian multiresolution classifier for prostate cancer detection from digitized needle biopsies.. IEEE transactions on bio-medical engineering. 59(5):1205-18. Abstract

Cerutti, S, Madabhushi A, Shah SK, Chon KH. 2012. Editorial: TBME Letters special section on multiscale biomedical signal and image modeling and analysis.. IEEE transactions on bio-medical engineering. 59(1):4-7.

Monaco, JP, Madabhushi A. 2012. Class-specific weighting for Markov random field estimation: application to medical image segmentation.. Medical image analysis. 16(8):1477-89. Abstract

Doyle, S, Feldman MD, Shih N, Tomaszewski JE, Madabhushi A. 2012. Cascaded discrimination of normal, abnormal, and confounder classes in histopathology: Gleason grading of prostate cancer.. BMC bioinformatics. 13:282. Abstract

Monaco, JP, Hipp J, Lucas D, Smith SC, Balis U, Madabhushi A. 2012. Image segmentation with implicit color standardization using spatially constrained expectation maximization: detection of nuclei.. International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). 15(Pt 1):365-72. Abstract

Cruz-Roa, A, González F, Galaro J, Judkins AR, Ellison D, Baccon J, Madabhushi A, Romero E. 2012. A visual latent semantic approach for automatic analysis and interpretation of anaplastic medulloblastoma virtual slides.. International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). 15(Pt 1):157-64. Abstract

2009

Janowczyk, A, Chandran S, Singh R, Sasaroli D, Coukos G, Feldman MD, Madabhushi A. 2009. Hierarchical Normalized Cuts: Unsupervised Segmentation of Vascular Biomarkers from Ovarian Cancer Tissue Microarrays. International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). 5761:230-238. Abstract

Tiwari, P, Rosen M, Reed G, Kurhanewicz J, Madabhushi A. 2009. Spectral embedding based probabilistic boosting tree (ScEPTre): classifying high dimensional heterogeneous biomedical data. International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). 5762:844-51. Abstract

2008

Viswanath, SE, Bloch NB, Genega EM, Rofsky NM, Lenkinski RE, Chappelow J, Toth R, Madabhushi A. 2008. A Comprehensive Segmentation, Registration, and Cancer Detection Scheme on 3 Tesla In Vivo Prostate DCE-MRI. International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). 5241:662-669. Abstract

Tiwari, P, Rosen M, Madabhushi A. 2008. Consensus-Locally Linear Embedding (C-LLE): Application to Prostate Cancer Detection on Magnetic Resonance Spectroscopy. International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). 5242:330-338. Abstract

Toth, R, Chappelow J, Rosen M, Pungavkar S, Kalyanpur A, Madabhushi A. 2008. Multi-attribute Non-Initializing Texture Reconstruction based Active Shape Model (MANTRA). International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). 5241:653-61. Abstract

2007

Tiwari, P, Madabhushi A, Rosen M. 2007. A hierarchical unsupervised spectral clustering scheme for detection of prostate cancer from magnetic resonance spectroscopy (MRS). International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). 4792:278-86. Abstract

2006

Doyle, S, Madabhushi A, Feldman MD, Tomaszewski JE. 2006. A Boosting Cascade for Automated Detection of Prostate Cancer from Digitized Histology. International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). 4191:504-11. Abstract

Doyle, S, Rodriguez C, Madabhushi A, Tomaszewski JE, Feldman MD. 2006. Detecting Prostatic Adenocarcinoma from Digitized Histology Using a Multi-Scale, Hierarchical Classification Approach. IEEE International Conference of Engineering in Medicine and Biology Society (EMBS). 1:4759-62. Abstract

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