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  1. Book: Interventional Urology

    Rastinehad, Ardeshir R. / McClure, Timothy / Wood, Bradford J. / Siegel, David N.

    2021  

    Author's details Ardeshir R. Rastinehad Associate Professor of Urology and Radiology Barbara and Donald Zucker School of Medicine at Hofstra-Northwell Vice Chair, Smith Institute for Urology at Lenox Hill Northwell Health System Director for Prostate Cancer New York, NY USA § David N. Siegel Executive Vice Chairman, Department of Radiology Northwell Health Associate Professor of Radiology Barbara and Donald Zucker School of Medicine at Hofstra-Northwell New Hyde Park, NY USA § Bradford J. Wood Chief of Interventional Radiology, National Institutes of Health Clinical Center Professor Bioengineering (Adjunct), University of Maryland School of Engineering Founding Director, NIH Center for Interventional Oncology Bethesda, MD USA § Timothy McClure Director of Focal Therapy and Interventional Urology Assistant Professor of Urology Assistant Professor of Radiology Weill Cornell Medicine New York, NY USA
    Keywords venography ; ImageFusion ; imageguidedtreatments ; imageguidedurology ; Retroperitoneum ; CBCT ; PIRADS ; GyroscopicMEMS ; Venography ; image fusion ; image guided treatments ; image guided urology ; retroperitoneum ; Gyroscopic MEMS
    Language English
    Size 588 p.
    Edition 2
    Publisher Springer International Publishing
    Document type Book
    Note PDA Manuell_12
    Format 215 x 285 x 29
    ISBN 9783030735647 ; 3030735648
    Database PDA

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  2. Article ; Online: The need for standardization of reporting in prostate MRI.

    Turkbey, Baris / Wood, Bradford J

    Nature reviews. Urology

    2021  Volume 18, Issue 4, Page(s) 195–196

    MeSH term(s) Humans ; Magnetic Resonance Imaging ; Male ; Prostatic Neoplasms/diagnostic imaging ; Reference Standards
    Language English
    Publishing date 2021-01-06
    Publishing country England
    Document type Journal Article ; Comment
    ZDB-ID 2493737-X
    ISSN 1759-4820 ; 1759-4812
    ISSN (online) 1759-4820
    ISSN 1759-4812
    DOI 10.1038/s41585-021-00425-9
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 6030: Show issues Location:
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  3. Article ; Online: Locoregional Thermal and Chemical Tumor Ablation: Review of Clinical Applications and Potential Opportunities for Use in Low- and Middle-Income Countries.

    Quang, Tri T / Yang, Jeffrey / Mikhail, Andrew S / Wood, Bradford J / Ramanujam, Nimmi / Mueller, Jenna L

    JCO global oncology

    2023  Volume 9, Page(s) e2300155

    MeSH term(s) Humans ; Developing Countries ; Neoplasms/surgery
    Language English
    Publishing date 2023-08-25
    Publishing country United States
    Document type Journal Article ; Review ; Research Support, Non-U.S. Gov't
    ISSN 2687-8941
    ISSN (online) 2687-8941
    DOI 10.1200/GO.23.00155
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Ultrasound Volume Reconstruction From Freehand Scans Without Tracking.

    Guo, Hengtao / Chao, Hanqing / Xu, Sheng / Wood, Bradford J / Wang, Jing / Yan, Pingkun

    IEEE transactions on bio-medical engineering

    2023  Volume 70, Issue 3, Page(s) 970–979

    Abstract: Transrectal ultrasound is commonly used for guiding prostate cancer biopsy, where 3D ultrasound volume reconstruction is often desired. Current methods for 3D reconstruction from freehand ultrasound scans require external tracking devices to provide ... ...

    Abstract Transrectal ultrasound is commonly used for guiding prostate cancer biopsy, where 3D ultrasound volume reconstruction is often desired. Current methods for 3D reconstruction from freehand ultrasound scans require external tracking devices to provide spatial information of an ultrasound transducer. This paper presents a novel deep learning approach for sensorless ultrasound volume reconstruction, which efficiently exploits content correspondence between ultrasound frames to reconstruct 3D volumes without external tracking. The underlying deep learning model, deep contextual-contrastive network (DC
    MeSH term(s) Male ; Humans ; Imaging, Three-Dimensional/methods ; Ultrasonography/methods ; Neural Networks, Computer ; Prostate/diagnostic imaging ; Movement
    Language English
    Publishing date 2023-02-17
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 160429-6
    ISSN 1558-2531 ; 0018-9294
    ISSN (online) 1558-2531
    ISSN 0018-9294
    DOI 10.1109/TBME.2022.3206596
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Advantages of a Photodiode Detector Endoscopy System in Fluorescence-Guided Percutaneous Liver Biopsies.

    Marcos-Vidal, Asier / Heidari, Pedram / Xu, Sheng / Wood, Bradford J / Mahmood, Umar

    Optics

    2023  Volume 4, Issue 2, Page(s) 340–350

    Abstract: Image-guided liver biopsies can improve their success rate when combined with the optical detection of Indocyanine Green (ICG) fluorescence accumulated in tumors. Previous works used a camera coupled to a thin borescope to capture and quantify images ... ...

    Abstract Image-guided liver biopsies can improve their success rate when combined with the optical detection of Indocyanine Green (ICG) fluorescence accumulated in tumors. Previous works used a camera coupled to a thin borescope to capture and quantify images from fluorescence emission during procedures; however, light-scattering prevented the formation of sharp images, and the time response for weakly fluorescent tumors was very low. Instead, replacing the camera with a photodiode detector shows an improved temporal resolution in a more compact and lighter device. This work presents the new design in a comparative study between both detection technologies, including an assessment of the temporal response and sensitivity to the presence of background fluorescence.
    Language English
    Publishing date 2023-05-15
    Publishing country Switzerland
    Document type Journal Article
    ISSN 2673-3269
    ISSN (online) 2673-3269
    DOI 10.3390/opt4020025
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: C-DARL: Contrastive diffusion adversarial representation learning for label-free blood vessel segmentation.

    Kim, Boah / Oh, Yujin / Wood, Bradford J / Summers, Ronald M / Ye, Jong Chul

    ArXiv

    2023  

    Abstract: Blood vessel segmentation in medical imaging is one of the essential steps for vascular disease diagnosis and interventional planning in a broad spectrum of clinical scenarios in image-based medicine and interventional medicine. Unfortunately, manual ... ...

    Abstract Blood vessel segmentation in medical imaging is one of the essential steps for vascular disease diagnosis and interventional planning in a broad spectrum of clinical scenarios in image-based medicine and interventional medicine. Unfortunately, manual annotation of the vessel masks is challenging and resource-intensive due to subtle branches and complex structures. To overcome this issue, this paper presents a self-supervised vessel segmentation method, dubbed the contrastive diffusion adversarial representation learning (C-DARL) model. Our model is composed of a diffusion module and a generation module that learns the distribution of multi-domain blood vessel data by generating synthetic vessel images from diffusion latent. Moreover, we employ contrastive learning through a mask-based contrastive loss so that the model can learn more realistic vessel representations. To validate the efficacy, C-DARL is trained using various vessel datasets, including coronary angiograms, abdominal digital subtraction angiograms, and retinal imaging. Experimental results confirm that our model achieves performance improvement over baseline methods with noise robustness, suggesting the effectiveness of C-DARL for vessel segmentation.
    Language English
    Publishing date 2023-07-31
    Publishing country United States
    Document type Preprint
    ISSN 2331-8422
    ISSN (online) 2331-8422
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Chest imaging in patients with acute respiratory failure because of coronavirus disease 2019.

    Di Meglio, Letizia / Carriero, Serena / Biondetti, Pierpaolo / Wood, Bradford J / Carrafiello, Gianpaolo

    Current opinion in critical care

    2023  Volume 28, Issue 1, Page(s) 17–24

    Abstract: Purpose of review: This review aims to explore the different imaging modalities, such as chest radiography (CXR), computed tomography (CT), ultrasound, PET/CT scan, and MRI to describe the main features for the evaluation of the chest in COVID-19 ... ...

    Abstract Purpose of review: This review aims to explore the different imaging modalities, such as chest radiography (CXR), computed tomography (CT), ultrasound, PET/CT scan, and MRI to describe the main features for the evaluation of the chest in COVID-19 patients with ARDS.
    Recent findings: This article includes a systematic literature search, evidencing the different chest imaging modalities used in patients with ARDS from COVID-19. Literature evidences different possible approaches going from the conventional CXR and CT to the LUS, MRI, and PET/CT.
    Summary: CT is the technique with higher sensitivity and definition for studying chest in COVID-19 patients. LUS or bedside CXR are critical in patients requiring close and repeated monitoring. Moreover, LUS and CXR reduce the radiation burden and the risk of infection compared with CT. PET/CT and MRI, especially in ARDS patients, are not usually used for diagnostic or follow-up purposes.
    MeSH term(s) COVID-19 ; Humans ; Lung/diagnostic imaging ; Positron Emission Tomography Computed Tomography ; Respiratory Distress Syndrome/diagnostic imaging ; Respiratory Insufficiency ; SARS-CoV-2 ; Ultrasonography
    Language English
    Publishing date 2023-03-01
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Intramural ; Review
    ZDB-ID 1235629-3
    ISSN 1531-7072 ; 1070-5295
    ISSN (online) 1531-7072
    ISSN 1070-5295
    DOI 10.1097/MCC.0000000000000906
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 4999: Show issues Location:
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  8. Article ; Online: C-DARL: Contrastive diffusion adversarial representation learning for label-free blood vessel segmentation.

    Kim, Boah / Oh, Yujin / Wood, Bradford J / Summers, Ronald M / Ye, Jong Chul

    Medical image analysis

    2023  Volume 91, Page(s) 103022

    Abstract: Blood vessel segmentation in medical imaging is one of the essential steps for vascular disease diagnosis and interventional planning in a broad spectrum of clinical scenarios in image-based medicine and interventional medicine. Unfortunately, manual ... ...

    Abstract Blood vessel segmentation in medical imaging is one of the essential steps for vascular disease diagnosis and interventional planning in a broad spectrum of clinical scenarios in image-based medicine and interventional medicine. Unfortunately, manual annotation of the vessel masks is challenging and resource-intensive due to subtle branches and complex structures. To overcome this issue, this paper presents a self-supervised vessel segmentation method, dubbed the contrastive diffusion adversarial representation learning (C-DARL) model. Our model is composed of a diffusion module and a generation module that learns the distribution of multi-domain blood vessel data by generating synthetic vessel images from diffusion latent. Moreover, we employ contrastive learning through a mask-based contrastive loss so that the model can learn more realistic vessel representations. To validate the efficacy, C-DARL is trained using various vessel datasets, including coronary angiograms, abdominal digital subtraction angiograms, and retinal imaging. Experimental results confirm that our model achieves performance improvement over baseline methods with noise robustness, suggesting the effectiveness of C-DARL for vessel segmentation.Our source code is available at https://github.com/boahK/MEDIA_CDARL.
    MeSH term(s) Humans ; Learning ; Coronary Angiography ; Diffusion ; Retina ; Software ; Image Processing, Computer-Assisted
    Language English
    Publishing date 2023-11-11
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 1356436-5
    ISSN 1361-8423 ; 1361-8431 ; 1361-8415
    ISSN (online) 1361-8423 ; 1361-8431
    ISSN 1361-8415
    DOI 10.1016/j.media.2023.103022
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 5206: Show issues Location:
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  9. Article ; Online: Hydrogel drug delivery systems for minimally invasive local immunotherapy of cancer.

    Mikhail, Andrew S / Morhard, Robert / Mauda-Havakuk, Michal / Kassin, Michael / Arrichiello, Antonio / Wood, Bradford J

    Advanced drug delivery reviews

    2023  Volume 202, Page(s) 115083

    Abstract: Although systemic immunotherapy has achieved durable responses and improved survival for certain patients and cancer types, low response rates and immune system-related systemic toxicities limit its overall impact. Intratumoral (intralesional) delivery ... ...

    Abstract Although systemic immunotherapy has achieved durable responses and improved survival for certain patients and cancer types, low response rates and immune system-related systemic toxicities limit its overall impact. Intratumoral (intralesional) delivery of immunotherapy is a promising technique to combat mechanisms of tumor immune suppression within the tumor microenvironment and reduce systemic drug exposure and associated side effects. However, intratumoral injections are prone to variable tumor drug distribution and leakage into surrounding tissues, which can compromise efficacy and contribute to toxicity. Controlled release drug delivery systems such as in situ-forming hydrogels are promising vehicles for addressing these challenges by providing improved spatio-temporal control of locally administered immunotherapies with the goal of promoting systemic tumor-specific immune responses and abscopal effects. In this review we will discuss concepts, applications, and challenges in local delivery of immunotherapy using controlled release drug delivery systems with a focus on intratumorally injected hydrogel-based drug carriers.
    MeSH term(s) Humans ; Delayed-Action Preparations ; Hydrogels ; Drug Delivery Systems ; Neoplasms/drug therapy ; Immunotherapy/methods ; Tumor Microenvironment
    Chemical Substances Delayed-Action Preparations ; Hydrogels
    Language English
    Publishing date 2023-09-09
    Publishing country Netherlands
    Document type Journal Article ; Review ; Research Support, N.I.H., Intramural
    ZDB-ID 639113-8
    ISSN 1872-8294 ; 0169-409X
    ISSN (online) 1872-8294
    ISSN 0169-409X
    DOI 10.1016/j.addr.2023.115083
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  10. Article: Simulated Misuse of Large Language Models and Clinical Credit Systems.

    Anibal, James / Huth, Hannah / Gunkel, Jasmine / Wood, Bradford

    medRxiv : the preprint server for health sciences

    2024  

    Abstract: Large language models (LLMs) have been proposed to support many healthcare tasks, including disease diagnostics and treatment personalization. While AI models may be applied to assist or enhance the delivery of healthcare, there is also a risk of misuse. ...

    Abstract Large language models (LLMs) have been proposed to support many healthcare tasks, including disease diagnostics and treatment personalization. While AI models may be applied to assist or enhance the delivery of healthcare, there is also a risk of misuse. LLMs could be used to allocate resources based on unfair, inaccurate, or unjust criteria. For example, a social credit system uses big data to assess "trustworthiness" in society, punishing those who score poorly based on evaluation metrics defined only by a power structure (corporate entity, governing body). Such a system may be amplified by powerful LLMs which can rate individuals based on high-dimensional multimodal data - financial transactions, internet activity, and other behavioural inputs. Healthcare data is perhaps the most sensitive information which can be collected and could potentially be used to violate civil liberty via a "clinical credit system", which may include limiting or rationing access to standard care. This report simulates how clinical datasets might be exploited and proposes strategies to mitigate the risks inherent to the development of AI models for healthcare.
    Language English
    Publishing date 2024-04-12
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2024.04.10.24305470
    Database MEDical Literature Analysis and Retrieval System OnLINE

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