Imaging of Pulmonary Cancer: Overcoming Limitations

New York University School Of Medicine, New York NY

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Abstract

DESCRIPTION (provided by applicant): Broad, long-term objectives and career goals: To acquire the knowledge and skills needed for independent patient-oriented research concentrated on improving the diagnosis and management of pulmonary malignancy through imaging and image analysis techniques. Health relatedness: About 1.2 million cases of invasive cancer are diagnosed annually in the United States. About 500,000 Americans die annually from cancer, and bronchogenic cancer is the leading cause of cancer deaths. Early diagnosis of pulmonary malignancy, comprised primarily of bronchogenic cancer and metastatic disease, is important for early treatment and patient survival. Early primary lung cancer and metastatic disease to the lung parenchyma commonly manifest as indeterminate nodules, meaning nodules that are too small to be characterized as benign or malignant by other methods, on diagnostic and, more recently, screening computer tomography (CT). Other than calcification, morphologic characteristics have not been shown to differentiate benign from malignant nodules. Change in volume over time has been used as a marker of malignancy; however, for small nodules in particular, subtle volume changes are difficult to detect and quantify consistently using current techniques. Specific aims: The immediate career goal is to obtain knowledge and research skills through a structured career development program in a mentored research environment. This entails multidisciplinary didactic training in digital imaging, image segmentation, image analysis, epidemiology and mechanisms of tumorigenesis, and clinical study design, and conducting the proposed research in a mentored environment. The overall aim of the proposed research is to facilitate the CT diagnosis of malignant nodules by the development of precise quantitative techniques for the measurement of pulmonary nodule volumes. Our central hypothesis is that early nodule growth can be detected on CT using computer-assisted techniques and be correlated with biologic indicators of preneoplasia and neoplasia. The proposed method will enable early recognition of nodule growth and, therefore, the early diagnosis of malignancy.