Sachin R. Jambawalikar, PhD

Board Certifications: 
Medical Physics
Profile Headshot

Overview

I am a faculty member in Radiology and Biomedical Engineering and Chief Medical Physicist in Department of Radiology at CUMC/NYP. My background and training have been in MR physics, machine learning and medical image feature analysis. As an image analysis scientist, I’m interested in developing noninvasive post processing and image analysis techniques for disease detection, and evaluation of disease therapy outcomes. My long-term research goals are to evaluate the use of multi- parametric MR feature analysis techniques and develop 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 Physics

Hospital Affiliations

  • NewYork-Presbyterian / Columbia University Irving Medical Center

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

Grants

INTEGRATINGRADIOMICS INTO S0819 AND LUNG-MAP, BIOMARKER DRIVEN CLINICAL TRIALS FOR LUNG CANCER (Federal Gov)

Jun 1 2018 - May 31 2023

IMAGING HUMAN BRAIN FUNCTION WITH MINIMAL MOBILITY RESTRICTIONS (Federal Gov)

Sep 30 2017 - Jun 30 2022

QUANTITATIVE IMAGING TOOLS TO DERIVE DW-MRI ONCOLOGICAL BIOMARKERS (Federal Gov)

Jul 1 2017 - Jun 30 2022

QUANTITATIVE T1RHO MAPPING IN MULTIPLE SCLEROSIS: DEVELOPMENT OF A POTENTIAL NOVEL BIOMARKER FOR DISEASE PROGNOSTICATION AND MANAGEMENT (Private)

Jul 1 2014 - Jun 30 2016

SARC SARCOMA SPORE (Federal Gov)

Sep 26 2012 - Aug 31 2013

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.