Sachin R Jambawalikar, PHD

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Overview

Dr. Sachin Jambawalikar is chief medical physicist in Department of Radiology at Columbia University Irving Medical Center and NewYork-Presbyterian and associate chair of research informatics/information technology for the Department of Radiology. He is a faculty member in both the Department of Radiology and the Department of Biomedical Engineering. His background and training have focused on MR physics, machine learning, and medical image feature analysis. As an image analysis scientist, he is interested in developing noninvasive post processing and image analysis techniques for disease detection and evaluation of disease therapy outcomes. His research is focused on evaluating the use of multiparametric MR feature analysis techniques and developing classification and regression machine learning models for disease and outcome prediction.

Academic Appointments

  • Assistant Professor of Radiology (Physics) at CUMC

Administrative Titles

  • Chief, Division of Medical Physics
  • Associate Chair of Research Informatics/IT

Hospital Affiliations

  • NewYork-Presbyterian / Columbia University Irving Medical Center
  • NewYork-Presbyterian Allen Hospital

Languages

  • Hindi
  • Marathi

Gender

  • Male

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Credentials & Experience

Education & Training

  • State University of New York at Stony Brook
  • Residency: Stony Brook University Medical Center (SUNY), NY

Board Certifications

  • Medical Physics

Research

Selected Publications

  1. Mutasa S, Chang P, Van Sant EP, Nemer J, Liu M, Karcich J, Patel G, Jambawalikar S, Ha R. Potential Role of Convolutional Neural Network Based Algorithm in Patient Selection for DCIS Observation Trials Using a Mammogram Dataset. Acad Radiol. 2019 Sep 13. pii: S1076-6332(19)30419-2. doi: 10.1016/j.acra.2019.08.012. [Epub ahead of print] PubMed PMID: 31526687.
  2. Winther HB, Gutberlet M, Hundt C, Kaireit TF, Alsady TM, Schmidt B, Wacker F, Sun Y, Dettmer S, Maschke SK, Hinrichs JB, Jambawalikar S, Prince MR, Barr RG, Vogel-Claussen J. Deep semantic lung segmentation for tracking potential pulmonary perfusion biomarkers in chronic obstructive pulmonary disease (COPD): The multi-ethnic study of atherosclerosis COPD study. J Magn Reson Imaging. 2019 Jul 5. doi: 10.1002/jmri.26853. [Epub ahead of print] PubMed PMID: 31276264.
  3. Stember JN, Celik H, Krupinski E, Chang PD, Mutasa S, Wood BJ, Lignelli A, Moonis G, Schwartz LH, Jambawalikar S, Bagci U. Eye Tracking for Deep Learning Segmentation Using Convolutional Neural Networks. J Digit Imaging. 2019 Aug;32(4):597-604. doi: 10.1007/s10278-019-00220-4. PubMed PMID: 31044392; PubMed Central PMCID: PMC6646645.
  4. Ha R, Mutasa S, Sant EPV, Karcich J, Chin C, Liu MZ, Jambawalikar S. Accuracy of Distinguishing Atypical Ductal Hyperplasia From Ductal Carcinoma In Situ With Convolutional Neural Network-Based Machine Learning Approach Using Mammographic Image Data. AJR Am J Roentgenol. 2019 Mar 12:1-6. doi: 10.2214/AJR.18.20250. [Epub ahead of print] PubMed PMID: 30860901.
  5. Malyarenko DI, Swanson SD, Konar AS, LoCastro E, Paudyal R, Liu MZ, Jambawalikar SR, Schwartz LH, Shukla-Dave A, Chenevert TL. Multicenter Repeatability Study of a Novel Quantitative Diffusion Kurtosis Imaging Phantom. Tomography. 2019 Mar;5(1):36-43. doi: 10.18383/j.tom.2018.00030. PubMed PMID: 30854440; PubMed Central PMCID: PMC6403043.
  6. Paudyal R, Konar AS, Obuchowski NA, Hatzoglou V, Chenevert TL, Malyarenko DI, Swanson SD, LoCastro E, Jambawalikar S, Liu MZ, Schwartz LH, Tuttle RM, Lee N, Shukla-Dave A. Repeatability of Quantitative Diffusion-Weighted Imaging Metrics in Phantoms, Head-and-Neck and Thyroid Cancers: Preliminary Findings. Tomography. 2019 Mar;5(1):15-25. doi: 10.18383/j.tom.2018.00044. PubMed PMID: 30854438; PubMed Central PMCID: PMC6403035.
  7. Ha R, Mutasa S, Karcich J, Gupta N, Pascual Van Sant E, Nemer J, Sun M, Chang P, Liu MZ, Jambawalikar S. Predicting Breast Cancer Molecular Subtype with MRI Dataset Utilizing Convolutional Neural Network Algorithm. J Digit Imaging. 2019 Apr;32(2):276-282. doi: 10.1007/s10278-019-00179-2. PubMed PMID: 30706213; PubMed Central PMCID: PMC6456631.
  8. Farooqi KM, Cooper C, Chelliah A, Saeed O, Chai PJ, Jambawalikar SR, Lipson H, Bacha EA, Einstein AJ, Jorde UP. 3D Printing and Heart Failure: The Present and the Future. JACC Heart Fail. 2019 Feb;7(2):132-142. doi: 10.1016/j.jchf.2018.09.011. Epub 2018 Dec 12. Review. PubMed PMID: 30553901.
  9. Stember JN, Chang P, Stember DM, Liu M, Grinband J, Filippi CG, Meyers P, Jambawalikar S. Convolutional Neural Networks for the Detection and Measurement of Cerebral Aneurysms on Magnetic Resonance Angiography. J Digit Imaging. 2019 Oct;32(5):808-815. doi: 10.1007/s10278-018-0162-z. PubMed PMID: 30511281; PubMed Central PMCID: PMC6737124.
  10. Jambawalikar S, Liu MZ, Moonis G. Advanced MR Imaging of the Temporal Bone. Neuroimaging Clin N Am. 2019 Feb;29(1):197-202. doi: 10.1016/j.nic.2018.09.009. Epub 2018 Oct 31. Review. PubMed PMID: 30466642.
  11. Shukla-Dave A, Obuchowski NA, Chenevert TL, Jambawalikar S, Schwartz LH, Malyarenko D, Huang W, Noworolski SM, Young RJ, Shiroishi MS, Kim H, Coolens C, Laue H, Chung C, Rosen M, Boss M, Jackson EF. Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE-MRI derived biomarkers in multicenter oncology trials. J Magn Reson Imaging. 2019 Jun;49(7):e101-e121. doi: 10.1002/jmri.26518. Epub 2018 Nov 19. Review. PubMed PMID: 30451345; PubMed Central PMCID: PMC6526078.
  12. Ha R, Chin C, Karcich J, Liu MZ, Chang P, Mutasa S, Pascual Van Sant E, Wynn RT, Connolly E, Jambawalikar S. Prior to Initiation of Chemotherapy, Can We Predict Breast Tumor Response? Deep Learning Convolutional Neural Networks Approach Using a Breast MRI Tumor Dataset. J Digit Imaging. 2019 Oct;32(5):693-701. doi: 10.1007/s10278-018-0144-1. PubMed PMID: 30361936; PubMed Central PMCID: PMC6737125.
  13. Ha R, Chang P, Mutasa S, Karcich J, Goodman S, Blum E, Kalinsky K, Liu MZ, Jambawalikar S. Convolutional Neural Network Using a Breast MRI Tumor Dataset Can Predict Oncotype Dx Recurrence Score. J Magn Reson Imaging. 2019 Feb;49(2):518-524. doi: 10.1002/jmri.26244. Epub 2018 Aug 21. PubMed PMID: 30129697.
  14. Ha R, Chang P, Mema E, Mutasa S, Karcich J, Wynn RT, Liu MZ, Jambawalikar S. Fully Automated Convolutional Neural Network Method for Quantification of Breast MRI Fibroglandular Tissue and Background Parenchymal Enhancement. J Digit Imaging. 2019 Feb;32(1):141-147. doi: 10.1007/s10278-018-0114-7. PubMed PMID: 30076489; PubMed Central PMCID: PMC6382627.
  15. Ha R, Chang P, Karcich J, Mutasa S, Pascual Van Sant E, Liu MZ, Jambawalikar S. Convolutional Neural Network Based Breast Cancer Risk Stratification Using a Mammographic Dataset. Acad Radiol. 2019 Apr;26(4):544-549. doi: 10.1016/j.acra.2018.06.020. Epub 2018 Jul 31. PubMed PMID: 30072292.

For a complete list of publications, please visit PubMed.gov