Targeted Localization and Optimized Surgical Resection for Lung Cancer

ABSTRACT In the US, nearly 230,000 new lung cancer cases are diagnosed annually, many at an early stage when they are smaller than 1 cm in diameter. For small tumors, sublobar resection such as segmentectomy is performed to resect the entire tumor with sufficient margins (distance between the edge of the tumor and the cut line). Although segmentectomy preserves lung function better than lobectomy (removal of entire lung lobe), it is associated with a higher locoregional recurrence rate (10.5% vs. 5.4%). This could be attributed to the critical challenges of 1) reliably localizing small, non-palpable nodules, and 2) resecting them with sufficient margins to ensure removal of all malignant tissue. Navigation Sciences, Inc. is revolutionizing lung cancer surgery with the cutting-edge NaviSci™ system, a real-time navigation technology designed to accurately localize and resect lung nodules in the presence of significant tissue deformation. The key innovation is the J-bar™, an active nodule marker with an electromagnetic (EM) sensor, placed percutaneously next to the nodule and paired with an EM sensor on the surgical stapler to provide real-time feedback on resection margins. A clinical trial involving 25 patients validated the NaviSci System wherein the J-bar was placed in the deflated lung after nodule palpation, and the J-bar-to-cutline (Jbar-cutline) distance was compared with pathology measurements. This project will develop novel navigation software, EndoNaviSci, to address two critical challenges in translating the NaviSci system to the clinic: 1) ensuring accurate nodule localization and J-bar placement without intraoperative imaging or palpation, and 2) measuring the true margin (nodule-cutline) distance in the deflated lung despite tissue deformation, a key prognostic factor for long-term survival. Aim 1 is to design and develop a software navigation system, EndoNaviSci, to accurately deploy the J-bar close to the nodule in the inflated lung. Aim 2 is to design and integrate the margin assessment algorithm with the EndoNaviSci software navigation system to accurately estimate the nodule and margin position in the deflated lung. Aim 3 is to validate the EndoNaviSci navigation system integrated with the J-bar in ex vivo lung and live pigs. The expected outcome of this Phase I project is the development of the EndoNaviSci system with the J-bar to localize lung nodules using diagnostic CT imaging and ensure complete nodule resection with sufficient margins. This project is significant since it addresses an important problem of accurately localizing and resecting lung nodules while preserving healthy lung function. The final milestone will be a fully integrated EndoNaviSci navigation system with the J-bar that is ready for future human clinical trials.