OBJECTIVE: The development and evaluation of a computer-aided bone scan analysis technique to quantify changes in tumor burden and assess treatment effects in prostate cancer clinical trials.
METHODS: We have developed and report on a commercial fully automated computer-aided detection (CAD) system. Using this system, scan images were intensity normalized, and then lesions were identified and segmented by anatomic region-specific intensity thresholding. Detected lesions were compared against expert markings to assess the accuracy of the CAD system. The metrics Bone Scan Lesion Area, Bone Scan Lesion Intensity, and Bone Scan Lesion Count were calculated from identified lesions, and their utility in assessing treatment effects was evaluated by analyzing before and after scans from metastatic castration-resistant prostate cancer patients: 10 treated and 10 untreated. In this study, patients were treated with cabozantinib, a MET/vascular endothelial growth factor inhibitor resulting in high rates of resolution of bone scan abnormalities.
RESULTS: Our automated CAD system identified bone lesion pixels with 94% sensitivity, 89% specificity, and 89% accuracy. Significant differences in changes from baseline were found between treated and untreated groups in all assessed measurements derived by our system. The most significant measure, Bone Scan Lesion Area, showed a median (interquartile range) change from baseline at week 6 of 7.13% (27.61) in the untreated group compared with -73.76% (45.38) in the cabozantinib-treated group (P=0.0003).
CONCLUSION: Our system accurately and objectively identified and quantified metastases in bone scans, allowing for interpatient and intrapatient comparison. It demonstrates potential as an objective measurement of treatment effects, laying the foundation for validation against other clinically relevant outcome measures.
Written by:
Brown MS, Chu GH, Kim HJ, Allen-Auerbach M, Poon C, Bridges J, Vidovic A, Ramakrishna B, Ho J, Morris MJ, Larson SM, Scher HI, Goldin JG. Are you the author?
Department of Radiological Sciences, Center for Computer Vision and Imaging Biomarkers, David Geffen School of Medicine at UCLA, University of California, Los Angeles, California 90024, USA.
Reference: Nucl Med Commun. 2012 Apr;33(4):384-94.
doi: 10.1097/MNM.0b013e3283503ebf
PubMed Abstract
PMID: 22367858
