Growth-pattern classification of pulmonary nodules based on variation of CT number histogram and its potential usefulness in nodule differentiation

In recent years, high resolution CT has been developed. CAD system is indispensable for pulmonary cancer screening. In research and development of computer-aided differential diagnosis, there is now widespread interest in the use of nodule doubling time for measuring the volumetric changes of pulmonary nodule. The evolution pattern of each nodule might depend on the CT number distribution pattern inside nodule such as pure GGO, mixed GGO, or solid nodules. This paper presents a computerized approach to measure nodule CT number variation inside pulmonary nodule. The approach consists of four steps: (1) nodule segmentation, (2) computation of CT number histogram, (3) nodule categorization (alpha, beta, gamma, delta, and epsilon) based on CT number histogram, (4) computation of doubling time based on CT number histogram, and growth-pattern classification which consists of six categories such as decrease, gradual decrease, no change, slow increase, gradual increase, and increase, and (5) classification between benign and malignant cases. Using our dataset of follow-up scans for whom the final diagnosis was known (62 benign and 42 malignant cases), we evaluated growth-pattern of nodules and designed the classification strategy between benign and malignant cases. In order to compare the performance between the proposed features and volumetric doubling time, the classification result was analyzed by an area under the receiver operating characteristic curve. The preliminary experimental result demonstrated that our approach has a highly potential usefulness to assess the nodule evolution using 3-D thoracic CT images.