Measuring glioma volumes: A comparison of linear measurement based formulae with the manual image segmentation technique

• Published in: Journal of cancer research and therapeutics 2016-01, Vol.12 (1), p.161-168
• Main Authors: Sreenivasan, Sanjeev, Madhugiri, Venkatesh, Sasidharan, Gopalakrishnan, Kumar, Roopesh
• Format: Article
• Language: English
• Subjects: Agreements; Automation; Computational biology; Cone-Beam Computed Tomography; Diagnosis; Diagnostic imaging; Glioma - diagnostic imaging; Glioma - pathology; Gliomas; Humans; Image Processing, Computer-Assisted; Image segmentation; Innovations; Medical imaging; Medical prognosis; Methods; NMR; Nuclear magnetic resonance; Retrospective Studies; Studies; Tumor Burden; Tumors
• ISSN: 0973-1482; 1998-4138
• DOI: 10.4103/0973-1482.153999

Context: Gliomas are irregular in shape unlike benign brain tumors like meningiomas or schwannomas. Simplifying assumptions about glioma geometry are therefore more likely to lead to wrong calculations of glioma volumes than for other tumors. Aims: We compared simple linear measurement.based techniques of measuring glioma volume with manual region of interest.based image segmentation and to assess concordance. Settings and Design: This study was a retrospective radiology archive-based study. Subjects and Methods: The volumes of gliomas were measured by two assessors using five different techniques - manual image segmentation and four linear measurement-based formulae, which included the formulae for the volume of a sphere, cylinder, ellipsoid and its simplification v = abc/2. Statistical Analysis Used: Intra-ssessor concordance was evaluated using mean vs. difference. (Bland-Altman) plots and raw agreement indices. Inter-rater agreement was assessed by calculating the intra-class correlation coefficient for each technique. Results: The best inter.rater concordance was for volume measured by manual segmentation. The tumor volumes measured using the formulae for volume of a sphere and cylinder had poor agreement with the planimetric volume and low inter.rater concordance. The formula for volume of an ellipsoid and its simplification had good agreement with the manual planimetric volume and had good inter.rater concordance. However, for larger tumors, the agreement with planimetric volume was poorer. Conclusions: Manual region of interest-based image segmentation is the standard technique for measuring glioma volumes. For routine clinical use, the simple formula v = abc/2 (or the formula for volume of an ellipsoid) could be used as alternatives.