A minimally invasive multimodality image-guided (MIMIG) system for peripheral lung cancer intervention and diagnosis

Tiancheng He, Zhong Xue, Kongkuo Lu, Miguel Valdivia y Alvarado, Kelvin K. Wong, Weixin Xie, Stephen T. Wong

Research output: Contribution to journalArticle

16 Scopus citations

Abstract

Background: Lung cancer is the leading cause of cancer-related death in the United States, with more than half of the cancers are located peripherally. Computed tomography (CT) has been utilized in the last decade to detect early peripheral lung cancer. However, due to the high false diagnosis rate of CT, further biopsy is often necessary to confirm cancerous cases. This renders intervention for peripheral lung nodules (especially for small peripheral lung cancer) difficult and time-consuming, and it is highly desirable to develop new, on-the-spot earlier lung cancer diagnosis and treatment strategies. Purpose: The objective of this study is to develop a minimally invasive multimodality image-guided (MIMIG) intervention system to detect lesions, confirm small peripheral lung cancer, and potentially guide on-the-spot treatment at an early stage. Accurate image guidance and real-time optical imaging of nodules are thus the key techniques to be explored in this work. Methods: The MIMIG system uses CT images and electromagnetic (EM) tracking to help interventional radiologists target the lesion efficiently. After targeting the lesion, a fiber-optic probe coupled with optical molecular imaging contrast agents is used to confirm the existence of cancerous tissues on-site at microscopic resolution. Using the software developed, pulmonary vessels, airways, and nodules can be segmented and visualized for surgical planning; the segmented results are then transformed onto the intra-procedural CT for interventional guidance using EM tracking. Endomicroscopy through a fiber-optic probe is then performed to visualize tumor tissues. Experiments using IntegriSense 680 fluorescent contrast agent labeling α vβ 3 integrin were carried out for rabbit lung cancer models. Confirmed cancers could then be treated on-the-spot using radio-frequency ablation (RFA). Results: The prototype system is evaluated using the rabbit VX2 lung cancer model to evaluate the targeting accuracy, guidance efficiency, and performance of molecular imaging. Using this system, we achieved an average targeting accuracy of 3.04. mm, and the IntegriSense signals within the VX2 tumors were found to be at least two-fold higher than those of normal tissues. The results demonstrate great potential for applying the system in human trials in the future if an optical molecular imaging agent is approved by the Food and Drug Administration (FDA). Conclusions: The MIMIG system was developed for on-the-spot interventional diagnosis of peripheral lung tumors by combining image-guidance and molecular imaging. The system can be potentially applied to human trials on diagnosing and treating earlier stage lung cancer. For current clinical applications, where a biopsy is unavoidable, the MIMIG system without contrast agents could be used for biopsy guidance to improve the accuracy and efficiency.

Original languageEnglish (US)
Pages (from-to)345-355
Number of pages11
JournalComputerized Medical Imaging and Graphics
Volume36
Issue number5
DOIs
StatePublished - Jul 1 2012

Keywords

  • Image computing
  • Image-guided intervention
  • Molecular imaging
  • Peripheral lung cancer

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Health Informatics
  • Radiological and Ultrasound Technology
  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition

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