TY - GEN
T1 - Multi-destination procedure planning for comprehensive lymph node staging bronchoscopy
AU - Kuhlengel, Trevor K.
AU - Higgins, William E.
N1 - Publisher Copyright:
© 2020 SPIE.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85085261420&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85085261420&partnerID=8YFLogxK
U2 - 10.1117/12.2542851
DO - 10.1117/12.2542851
M3 - Conference contribution
AN - SCOPUS:85085261420
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Medical Imaging 2020
A2 - Fei, Baowei
A2 - Linte, Cristian A.
PB - SPIE
T2 - Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling
Y2 - 16 February 2020 through 19 February 2020
ER -
