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Proceedings Paper

Multi-destination procedure planning for comprehensive lymph node staging bronchoscopy
Author(s): Trevor K. Kuhlengel; William E. Higgins
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Paper Abstract

Lung cancer is the deadliest form of cancer. New lung cancer screening programs are currently being deployed worldwide. This increases the premium on accurate lung cancer staging as well as increasing the number of detected early-stage cancer patients. Accurate staging requires sampling lymph nodes in a sufficient number of nodal stations throughout the central chest. To this end, physicians use the world standard International Association for the Study of Lung Cancer’s (IASLC) TNM lung cancer staging model and lymph node station map. To determine the nodal stage, the physician performs a bronchoscopic lymph node staging procedure, a minimally-invasive procedure in which the physician samples lymph nodes from multiple diagnostic sites in the central chest using a bronchoscope. Image guided-bronchoscopy (IGB) systems, now a part of widely-accepted practice, greatly assist in this procedure by drawing upon information from the patient’s three-dimensional (3D) x-ray computed tomography (CT) scan. Unfortunately, even with modern IGB systems, most physicians still do not stage lung cancer in a comprehensive manner, sampling only a few nodes for each patient. Furthermore, current IGB systems do not integrate N stage information into their planning or guidance strategies, nor do they attempt to optimize procedure routes to efficiently visit multiple diagnostic sites. To bridge this gap, we propose new methods tailored to planning more comprehensive lymph node staging procedures. Specifically, our development features two interconnected contributions toward creating a computer-based planning and guidance system. First is a method for defining the nodal staging zones and automatically labeling the nodal stage value of each defined lymph node. Second, we develop a method for creating efficient multi-destination guidance plans visiting diagnostic sites throughout the central chest. We demonstrate our methods using CT image data collected from human lung cancer patients.

Paper Details

Date Published: 16 March 2020
PDF: 7 pages
Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 113151T (16 March 2020); doi: 10.1117/12.2542851
Show Author Affiliations
Trevor K. Kuhlengel, Penn. State Univ. (United States)
William E. Higgins, Penn. State Univ. (United States)


Published in SPIE Proceedings Vol. 11315:
Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling
Baowei Fei; Cristian A. Linte, Editor(s)

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