Bronchoscopic biopsy of the central-chest lymph nodes is an important step for lung-cancer staging. Before bronchoscopy, the physician first visually assesses a patient's three-dimensional (3D) computed tomography (CT) chest scan to identify suspect lymph-node sites. Next, during bronchoscopy, the physician guides the bronchoscope to each desired lymph-node site. Unfortunately, the physician has no link between the 3D CT image data and the live video stream provided during bronchoscopy. Thus, the physician must essentially perform biopsy blindly, and the skill levels between different physicians differ greatly. We describe an approach that enables synergistic fusion between the 3D CT data and the bronchoscopic video. Both the integrated planning and guidance system and the internal CT-video registration and fusion methods are described. Phantom, animal, and human studies illustrate the efficacy of the methods.

Object The definition of regions of interest (ROIs) such as suspect cancer nodules or lymph nodes in 3D MDCT chest images is often difficult because of the complexity of the phenomena that give rise to them. Manual slice tracing has been used commonly for such problems, but it is extremely time consuming, subject to operator biases, and does not enable reproducible results. Proposed automated 3D imagesegmentation methods are generally application dependent, and even the most robust methods have difficulty in defining complex ROIs. Materials and methods The semi-automatic interactive paradigm known as live wire has been proposed by researchers, whereby the human operator interactively defines an ROI's boundary, guided by an active automated method. We propose 2D and 3D live-wire methods that improve upon previously proposed techniques. The 2D method gives improved robustness and incorporates a search region to improve computational efficiency. The 3D method requires the operator to only consider a few 2D slices, with an automated procedure performing the bulk of the analysis. Results For tests run with five human operators on both 2D and 3D ROIs in 3D MDCT chest images, the reproducibility was >97% and the ground-truth correspondence was at least 97%. The 2D live-wire approach was ≥14 times faster than manual slice tracing, while the 3D method was ≥28 times faster than manual slice tracing. Finally, we describe a computer-based tool and its application to 3D MDCT-based planning and follow-on live guidance of bronchoscopy. Conclusion The live-wire methods are efficient, reliable, easy to use, and applicable to a wide range of circumstances.

Exosomes derived from cancer cells are deemed important drivers of pre-metastatic niche formation at distant organs, but the underlying mechanisms of their effects remain largely unknow. Although the role of ADAM17 in cancer cells has been well studied, the secreted ADAM17 effects transported via exosomes are less understood. Herein, we show that the level of exosome-derived ADAM17 is elevated in the serum of patients with metastatic colorectal cancer as well as in metastatic colorectal cancer cells. Furthermore, exosomal ADAM17 was shown to promote the migratory ability of colorectal cancer cells by cleaving the E-cadherin junction. Moreover, exosomal ADAM17 overexpression as well as RNA interference results highlighted its function as a tumor metastasis-promoting factor in colorectal cancer in vitro and in vivo. Taken together, our current work suggests that exosomal ADAM17 is involved in pre-metastatic niche formation and may be utilized as a blood-based biomarker of colorectal cancer metastasis.

Purpose Lung cancer remains the leading cause of cancer death in the United States. Central to the lung-cancer diagnosis and staging process is the assessment of the central-chest lymph nodes. This assessment requires two steps: (1) examination of the lymph-node stations and identification of diagnostically important nodes in a three-dimensional (3D) multidetector computed tomography (MDCT) chest scan; (2) tissue sampling of the identified nodes. We describe a computer-based system for automatically defining the central-chest lymph-node stations in a 3D MDCT chest scan. Methods Automated methods first construct a 3D chest model, consisting of the airway tree, aorta, pulmonary artery, and other anatomical structures. Subsequent automated analysis then defines the 3D regional nodal stations, as specified by the internationally standardized TNM lung-cancer staging system. This analysis involves extracting over 140 pertinent anatomical landmarks from structures contained in the 3D chest model. Next, the physician uses data mining tools within the system to interactively select diagnostically important lymph nodes contained in the regional nodal stations. Results Results from a ground-truth database of unlabeled lymph nodes identified in 32 MDCT scans verify the system’s performance. The system automatically defined 3D regional nodal stations that correctly labeled 96% of the database’s lymph nodes, with 93% of the stations correctly labeling 100% of their constituent nodes. Conclusions The system accurately defines the regional nodal stations in a given high-resolution 3D MDCT chest scan and eases a physician’s burden for analyzing a given MDCT scan for lymph-node station assessment. It also shows potential as an aid for preplanning lung-cancer staging procedures.