Wei Zhao1, Han Liu2, Joseph K Leader2, David Wilson3, Xin Meng2, Lei Wang2, Liang-An Chen1, Jiantao Pu4. 1. Respiratory Department, Chinese PLA General Hospital, Beijing, China. 2. Departments of Radiology and Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA. 3. Department of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA. 4. Departments of Radiology and Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA. Electronic address: puj@upmc.edu.
Abstract
OBJECTIVES: The idea of inferring the prognosis of lung tumor via its surrounding vasculature is novel, but not supported by available technology. In this study, we described and validated a computerized method to identify the vasculature surrounding a pulmonary nodule depicted on low-dose computed tomography (LDCT). MATERIALS AND METHODS: The proposed computerized scheme identified the vessels surrounding a lung nodule by using novel computational geometric solutions and quantified them by decomposing the vessels into independent vessel branches. We validated this scheme by testing it on a dataset consisting of 100 chest CT examinations, with 50-paired benign and malignant nodules. Two experienced pulmonologists were asked to measure the vessel branches associated with a nodule under the aid of a visualization tool. We used the Bland-Altman plots and the concordance correlation coefficient (CCC) to assess the agreement between the results of the computer algorithm and two experienced pulmonologists. RESULTS: Bland-Altman different analysis demonstrated a mean bias of 0.61 ± 4.17 in terms of vessel branches between the computer results and the human experts, while the inter-rater mean bias was -0.61 ± 1.60. The CCC-based agreements between the computer and the two raters were 0.90 / 0.86, 0.79 / 0.83 for benign and malignant nodules, respectively. CONCLUSION: The small width of the limits of agreement between the computer algorithm and the human experts suggests that the results from the computer and the pulmonologist experts were relatively consistent, namely the computerized scheme is capable of reliably identifying the vasculature surrounding a nodule.
OBJECTIVES: The idea of inferring the prognosis of lung tumor via its surrounding vasculature is novel, but not supported by available technology. In this study, we described and validated a computerized method to identify the vasculature surrounding a pulmonary nodule depicted on low-dose computed tomography (LDCT). MATERIALS AND METHODS: The proposed computerized scheme identified the vessels surrounding a lung nodule by using novel computational geometric solutions and quantified them by decomposing the vessels into independent vessel branches. We validated this scheme by testing it on a dataset consisting of 100 chest CT examinations, with 50-paired benign and malignant nodules. Two experienced pulmonologists were asked to measure the vessel branches associated with a nodule under the aid of a visualization tool. We used the Bland-Altman plots and the concordance correlation coefficient (CCC) to assess the agreement between the results of the computer algorithm and two experienced pulmonologists. RESULTS: Bland-Altman different analysis demonstrated a mean bias of 0.61 ± 4.17 in terms of vessel branches between the computer results and the human experts, while the inter-rater mean bias was -0.61 ± 1.60. The CCC-based agreements between the computer and the two raters were 0.90 / 0.86, 0.79 / 0.83 for benign and malignant nodules, respectively. CONCLUSION: The small width of the limits of agreement between the computer algorithm and the human experts suggests that the results from the computer and the pulmonologist experts were relatively consistent, namely the computerized scheme is capable of reliably identifying the vasculature surrounding a nodule.