This proposal aims to develop, build, and assess a new medical system that will use a multi-lumen steerable needle to extend the reach of a bronchoscope to safely access locations in the peripheral lung for diagnosing and treating lung cancer at earlier stages. Lung cancer is responsible for more deaths per year than any other cancer, with more than 150,000 Americans dying from it each year. Survival depends on early diagnosis, which requires biopsy to be definitive. Unfortunately, current approaches for accessing suspicious nodules for biopsy either risk lung collapse (pneumothorax) or are unable to accurately reach nodules in the peripheral lung not adjacent to a bronchial tube, making it dangerous at best, or impossible at worst, to accurately access nodules in over half the lung. The new system will harness the capabilities of multi-lumen steerable needles to reliably and safely access any nodule in the lung, including in the peripheral zone. Multi-lumen steerable needles consist of pre-curved, nested tubes. Independently rotating and axially translating each tube allows a physician to control the needle's curvilinear shape. The proposed system will include the bronchoscope through which multi-lumen steerable needles can be deployed. To use the system, a physician will first guide a bronchoscope close to the nodule. Next, using image-guidance, the multi-lumen steerable needle will deploy from the bronchoscope's tip, pierce the bronchial tube, and maneuver through the parenchyma to the nodule. Unlike existing trans-airway devices that are restricted to moving along bronchial tubes, our device will be able to steer anywhere in the lung. Furthermore, using multi-lumen steerable needles deployed from a bronchoscope will decrease the serious risks of bleeding (since significant vessels in the parenchyma can be avoided by image-guided steering) and pneumothorax (since, in contrast to transthoracic needle biopsy, our device never punctures the pleura around the lung).
The specific aims are to (1) develop a bronchoscope-deployed multi-lumen steerable needle for lung procedures, (2) build a prototype system with device hardware and image-guidance planning software, and (3) assess the effectiveness of the system via CT-guided experiments in inflated, ex vivo porcine lungs. This project brings together a multidisciplinary team that spans the areas of expertise necessary to accomplish these aims, including expertise in cardiothoracic surgery, bronchoscopy, radiology, mechanical engineering, and computer science. We will conduct the evaluation experiments at the University of North Carolina at Chapel Hill using animated, ex vivo porcine lungs in a simulator that is used for training surgeons and residents. The proposed new system will lay the groundwork for future studies with live animals, cadavers, and, ultimately, humans. This proposed research has the potential to enhance public health by enabling low-risk, reliable access to nodules anywhere in the lung for early-stage, definitive diagnosis of lung cancer. Once the multi-lumen steerable needles safely reach the nodule, the new device will also be able to deliver local therapy via chemotherapy instillation, ablation, and brachytherapy.
Lung cancer is the most deadly form of cancer, and survival depends on early-stage diagnosis and treatment. This proposal focuses on developing a new device, which deploys steerable needles from the tip of a bronchoscope, enabling physicians too reliably and safely reach potential cancer sites anywhere in the lung for early diagnosis and treatment. By using a bronchoscope to deploy multi-lumen steerable needles inside the lung, rather than puncturing the pleura, and by steering around critical blood vessels, our new system could significantly decrease the risk of serious complications such as lung collapse and bleeding.
|Torres, Luis G; Baykal, Cenk; Alterovitz, Ron (2014) Interactive-rate Motion Planning for Concentric Tube Robots. IEEE Int Conf Robot Autom 2014:1915-1921|
|Ichnowski, Jeffrey; Prins, Jan F; Alterovitz, Ron (2014) Cache-Aware Asymptotically-Optimal Sampling-Based Motion Planning. IEEE Int Conf Robot Autom 2014:5804-5810|