Thomas Polascik

Overview:

Prostate cancer imaging
Focal therapy of prostate cancer
Prostate cancer outcomes
Kidney cancer outcomes
Minimally invasive surgery
Nerve sparing cryotherapy

Positions:

Professor of Surgery

Surgery, Urology
School of Medicine

Member of the Duke Cancer Institute

Duke Cancer Institute
School of Medicine

Education:

M.D. 1991

The University of Chicago

Intern in General Surgery, Surgery

Johns Hopkins University

Assistant Resident in General Surgery, Surgery

Johns Hopkins University

Resident, Urology

Johns Hopkins University

Chief Resident, Urology

Johns Hopkins University

Assistant Chief of Service, Urology

Johns Hopkins University

Fellowship, Urologic Oncology

Johns Hopkins University

Grants:

Image guided targeted biopsy of clinically significant prostate cancer with acoustic radiation force

Administered By
Biomedical Engineering
Awarded By
National Institutes of Health
Role
Co Investigator
Start Date
End Date

Prostate Cancer Assessment and Treatment Guidance Via Integrated 3D ARFI Elasticity Imaging and Multi-Parametric MRI

Administered By
Biomedical Engineering
Awarded By
Kitware Inc.
Role
Co Investigator
Start Date
End Date

Early Detection of Clinically Significant Prostate Cancer using Ultrasonic Acoustic Radiation Force Impulse (ARFI) Imaging

Administered By
Biomedical Engineering
Awarded By
United States Army Medical Research Acquisition Activity
Role
Co Investigator
Start Date
End Date

Prospective registry to optimize clinical care (DUCIMAS)

Awarded By
Myriad Genetics, Inc.
Role
Principal Investigator
Start Date
End Date

A Phase 3 Study to Evaluate the Safety and Efficacy of Tc-MIP-1404 SPECT/CT Imaging to Detect Clinically Significant Prostate Cancer in Men with Biopsy Proven Low-Grade Prostate Cancer who are Candidates for Active Surveillance(proSPECT-AS)

Awarded By
Molecular Insight Pharmaceuticals, Inc.
Role
Principal Investigator
Start Date
End Date

Publications:

Utilization of focal therapy for patients discontinuing active surveillance of prostate cancer: Recommendations of an international Delphi consensus.

BACKGROUND: With the advancement of imaging technology, focal therapy (FT) has been gaining acceptance for the treatment of select patients with localized prostate cancer (CaP). We aim to provide details of a formal physician consensus on the utilization of FT for patients with CaP who are discontinuing active surveillance (AS). METHODS: A 3-stage Delphi consensus on CaP and FT was conducted. Consensus was defined as agreement by ≥80% of physicians. An in-person meeting was attended by 17 panelists to formulate the consensus statement. RESULTS: Fifty-six respondents participated in this interdisciplinary consensus study (82% urologist, 16% radiologist, 2% radiation oncology). The participants confirmed that there is a role for FT in men discontinuing AS (48% strongly agree, 39% agree). The benefit of FT over radical therapy for men coming off AS is: less invasive (91%), has a greater likelihood to preserve erectile function (91%), has a greater likelihood to preserve urinary continence (91%), has fewer side effects (86%), and has early recovery post-treatment (80%). Patients will need to undergo mpMRI of the prostate and/or a saturation biopsy to determine if they are potential candidates for FT. Our limitations include respondent's biases and that the participants of this consensus may not represent the larger medical community. CONCLUSIONS: FT can be offered to men coming off AS between the age of 60 to 80 with grade group 2 localized cancer. This consensus from a multidisciplinary, multi-institutional, international expert panel provides a contemporary insight utilizing FT for CaP in select patients who are discontinuing AS.
Authors
Tan, WP; Rastinehad, AR; Klotz, L; Carroll, PR; Emberton, M; Feller, JF; George, AK; Gill, IS; Gupta, RT; Katz, AE; Lebastchi, AH; Marks, LS; Marra, G; Pinto, PA; Song, DY; Sidana, A; Ward, JF; Sanchez-Salas, R; Rosette, JDL; Polascik, TJ; Focal Therapy Consensus group.,
MLA Citation
URI
https://scholars.duke.edu/individual/pub1475713
PMID
33676851
Source
pubmed
Published In
Urol Oncol
Published Date
DOI
10.1016/j.urolonc.2021.01.027

Editorial Comment.

Authors
MLA Citation
Polascik, Thomas J. “Editorial Comment.J Urol, vol. 205, no. 3, Mar. 2021, pp. 798–99. Pubmed, doi:10.1097/JU.0000000000001382.02.
URI
https://scholars.duke.edu/individual/pub1477141
PMID
33393829
Source
pubmed
Published In
The Journal of Urology
Volume
205
Published Date
Start Page
798
End Page
799
DOI
10.1097/JU.0000000000001382.02

Imaging and technologies for prostate cancer. Where are we now-where do we go?

Authors
de la Rosette, JJMCH; Sanchez Salas, R; Rastinehad, A; Polascik, TJ
MLA Citation
de la Rosette, Jean J. M. C. H., et al. “Imaging and technologies for prostate cancer. Where are we now-where do we go?World J Urol, vol. 39, no. 3, Mar. 2021, pp. 635–36. Pubmed, doi:10.1007/s00345-021-03641-5.
URI
https://scholars.duke.edu/individual/pub1477142
PMID
33649870
Source
pubmed
Published In
World J Urol
Volume
39
Published Date
Start Page
635
End Page
636
DOI
10.1007/s00345-021-03641-5

Data-driven Focal Therapy for Localized Prostate Cancer: A Wake-up Call.

Authors
Shah, A; Polascik, TJ
MLA Citation
Shah, Ankeet, and Thomas J. Polascik. “Data-driven Focal Therapy for Localized Prostate Cancer: A Wake-up Call.Eur Urol Oncol, Feb. 2021. Pubmed, doi:10.1016/j.euo.2021.01.008.
URI
https://scholars.duke.edu/individual/pub1475017
PMID
33602653
Source
pubmed
Published In
Eur Urol Oncol
Published Date
DOI
10.1016/j.euo.2021.01.008

Deep Convolutional Neural Networks for Displacement Estimation in ARFI Imaging

Ultrasound elasticity imaging in soft tissue with acoustic radiation force requires the estimation of displacements, typically on the order of several microns, from serially-acquired raw data A-lines. In this work, we implement a fully convolutional neural network (CNN) for ultrasound displacement estimation. We present a novel method for generating ultrasound training data, in which synthetic 3-D displacement volumes with a combination of randomly-seeded ellipsoids are created and used to displace scatterers, from which simulated ultrasonic imaging is performed using Field II. Network performance was tested on these virtual displacement volumes as well as an experimental ARFI phantom dataset and a human in vivo prostate ARFI dataset. In simulated data, the proposed neural network performed comparably to Loupas’s algorithm, a conventional phase-based displacement estimation algorithm; the RMS error was 0.62 μm for the CNN and 0.73 μm for Loupas. Similarly, in phantom data, the contrast-to-noise ratio of a stiff inclusion was 2.27 for the CNN-estimated image and 2.21 for the Loupas-estimated image. Applying the trained network to in vivo data enabled the visualization of prostate cancer and prostate anatomy. The proposed training method provided 26,000 training cases, which allowed for robust network training. The CNN had a computation time that was comparable to Loupas’s algorithm; further refinements to the network architecture may provide an improvement in the computation time. We conclude that deep neural network-based displacement estimation from ultrasonic data is feasible, providing comparable performance with respect to both accuracy and speed compared to current standard time delay estimation approaches.
Authors
Chan, DY; Cody Morris, D; Polascik, TJ; Palmeri, ML; Nightingale, KR
MLA Citation
Chan, D. Y., et al. “Deep Convolutional Neural Networks for Displacement Estimation in ARFI Imaging.” Ieee Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, Jan. 2021. Scopus, doi:10.1109/TUFFC.2021.3068377.
URI
https://scholars.duke.edu/individual/pub1478591
Source
scopus
Published In
Ieee Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
Published Date
DOI
10.1109/TUFFC.2021.3068377