Image-guided EBUS bronchoscopy system for lung-cancer staging.

Bronchoscopy Endobronchial ultrasound Image-guided surgery systems Lung cancer staging Lymph nodes Multimodal imaging

Journal

Informatics in medicine unlocked
ISSN: 2352-9148
Titre abrégé: Inform Med Unlocked
Pays: England
ID NLM: 101718051

Informations de publication

Date de publication:
2021
Historique:
entrez: 17 9 2021
pubmed: 18 9 2021
medline: 18 9 2021
Statut: ppublish

Résumé

The staging of the central-chest lymph nodes is a major step in the management of lung-cancer patients. For this purpose, the physician uses a device that integrates videobronchoscopy and an endobronchial ultrasound (EBUS) probe. To biopsy a lymph node, the physician first uses videobronchoscopy to navigate through the airways and then invokes EBUS to localize and biopsy the node. Unfortunately, this process proves difficult for many physicians, with the choice of biopsy site found by trial and error. We present a complete image-guided EBUS bronchoscopy system tailored to lymph-node staging. The system accepts a patient's 3D chest CT scan, an optional PET scan, and the EBUS bronchoscope's video sources as inputs. System workflow follows two phases: (1) procedure planning and (2) image-guided EBUS bronchoscopy. Procedure planning derives airway guidance routes that facilitate optimal EBUS scanning and nodal biopsy. During the live procedure, the system's graphical display suggests a series of device maneuvers to perform and provides multimodal visual cues for locating suitable biopsy sites. To this end, the system exploits data fusion to drive a multimodal virtual bronchoscope and other visualization tools that lead the physician through the process of device navigation and localization. A retrospective lung-cancer patient study and follow-on prospective patient study, performed within the standard clinical workflow, demonstrate the system's feasibility and functionality. For the prospective study, 60/60 selected lymph nodes (100%) were correctly localized using the system, and 30/33 biopsied nodes (91%) gave adequate tissue samples. Also, the mean procedure time including all user interactions was 6 min 43 s All of these measures improve upon benchmarks reported for other state-of-the-art systems and current practice. Overall, the system enabled safe, efficient EBUS-based localization and biopsy of lymph nodes.

Identifiants

pubmed: 34532565
doi: 10.1016/j.imu.2021.100665
pmc: PMC8443139
mid: NIHMS1737913
pii:
doi:

Types de publication

Journal Article

Langues

eng

Subventions

Organisme : NCI NIH HHS
ID : R01 CA151433
Pays : United States

Déclaration de conflit d'intérêts

Declaration of competing interest William E. Higgins and Penn State have an identified conflict of interest and financial interest related to this research. These interests have been reviewed by the University’s Institutional and Individual Conflict of Interest Committees and are currently being managed by the University and reported to the NIH.

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Auteurs

Xiaonan Zang (X)

School of Electrical Engineering and Computer Science, Penn State University, University Park, PA, United States.
EDDA Technologies, Princeton, NJ 08540, United States.

Ronnarit Cheirsilp (R)

School of Electrical Engineering and Computer Science, Penn State University, University Park, PA, United States.
Broncus Medical, San Jose, CA, United States.

Patrick D Byrnes (PD)

School of Electrical Engineering and Computer Science, Penn State University, University Park, PA, United States.
University of Wisconsin, Platteville, WI 53818, United States.

Trevor K Kuhlengel (TK)

School of Electrical Engineering and Computer Science, Penn State University, University Park, PA, United States.

Catherine Abendroth (C)

Penn State Milton S. Hershey Medical Center, Penn State University, Hershey, PA, United States.

Thomas Allen (T)

Penn State Milton S. Hershey Medical Center, Penn State University, Hershey, PA, United States.

Rickhesvar Mahraj (R)

Penn State Milton S. Hershey Medical Center, Penn State University, Hershey, PA, United States.

Jennifer Toth (J)

Penn State Milton S. Hershey Medical Center, Penn State University, Hershey, PA, United States.

Rebecca Bascom (R)

Penn State Milton S. Hershey Medical Center, Penn State University, Hershey, PA, United States.

William E Higgins (WE)

School of Electrical Engineering and Computer Science, Penn State University, University Park, PA, United States.

Classifications MeSH