Automated detection of bone metastatic changes using serial CT scans

Abstract

Bone metastases resulting from a primary tumor invasion to the bone are common and cause significant morbidity in advanced cancer patients. Although the detection of bone metastases is often straightforward, it is difficult to identify their spread and track their changes, particularly in early stages. This paper presents a novel method that automatically finds the changes in appearance and the progress of bone metastases using longitudinal CT images. In contrast to previous methods based on nodule detection within a specific bone site in an individual CT scan, the approach in the present study is based on the subtraction between two registered CT volumes. The volumes registered using the proposed weighted-Demons registration and symmetric warping were subtracted with minimizing noise, and the Jacobian and false positive suppressions were performed to reduce false alarms. The proposed method detects the changes in bone metastases within 3min for entire chest bone structures covering the spine, ribs, and sternum. The method was validated based on 3-fold cross validation using the radiologists' markings of 459 lesions in 24 subjects and was performed with a sensitivity of 92.59%, a false positive volume of 2.58%, and 9.71 false positives per patient. Note that 113 lesions (24%) missed by the radiologists were identified by the present system and confirmed to be true metastases. Indeed, three patients diagnosed initially as normal, having no metastatic difference, by radiologists were found to be abnormal using the proposed system. Automatic detection method of bone metastatic changes in the entire chest bone was developed. Weighted Demons, symmetric warping, following false positive suppressions, and their parallel computing implementation enabled precise and fast computation of delicate changes in serial CT scans. The cross validation proved that this method can be quite useful for assisting radiologists in sensing minute metastatic changes from early stage.