Systematic analysis of measurement variability in lung cancer with multidetector computed tomography
Binghu Jiang1, Dan Zhou2, Yujie Sun3, Jichen Wang2
1 Department of Radiology, Sir Run Run Hospital Affiliated with Nanjing Medical University, Nanjing, China
2 Department of Radiology, BenQ Medical Center, Nanjing Medical University, Nanjing, China
3 Department of Cell Biology, Collaborative Innovation Center for Cancer Personalized Medicine, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Cancer Center, Key Laboratory of Human Functional Genomics of Jiangsu Province, Nanjing Medical University, Nanjing, China
Department of Radiology, BenQ Medical Center, Nanjing Medical University, No.71, Hexi Street, Jianye District, 210019, Nanjing
Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, No.101, Longmian Avenue, Jiangning District, 211166, Nanjing
Source of Support: None, Conflict of Interest: None
Objective: To systematically analyze the nature of measurement variability in lung cancer with multidetector computed tomography (CT) scans.
Methods: Multidetector CT scans of 67 lung cancer patients were analyzed. Unidimensional (Response Evaluation Criteria in Solid Tumor criteria), bidimensional (World Health Organization criteria), and volumetric measurements were performed independently by ten radiologists and were repeated after at least 5 months. Repeatability and reproducibility measurement variations were estimated by analyzing reliability, agreement, variation coefficient, and misclassification statistically. The relationship of measurement variability with various sources was also analyzed.
Results: Analyses of 69 lung tumors with an average size of 1.1–12.1 cm (mean 4.3 cm) indicated that volumetric technique had the minimum measurement variability compared to the unidimensional or bidimensional technique. Tumor characteristics (object effect) could be the primary factor to influence measurement variability while the effect of raters (subjective effect) was faint. Segmentation and size in tumor characteristics were associated with measurement variability, and some mathematical function was established between the volumetric variability and tumor size.
Conclusion: Volumetric technique has the minimum variability in measuring lung cancer, and measurement variability is associated with tumor size by nonlinear mathematical function.