Non-invasive 3D time-of-flight imaging technique for tumour volume assessment in subcutaneous models

• Published in: Laboratory animals (London) Vol. 49; no. 2; pp. 168 - 171
• Main Authors: Delgado San Martin, JA, Worthington, P, Yates, JWT
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01-04-2015
• Subjects: Algorithms; Animals; Disease Models, Animal; Image Processing, Computer-Assisted - instrumentation; Image Processing, Computer-Assisted - methods; Imaging, Three-Dimensional - instrumentation; Imaging, Three-Dimensional - methods; Mice; Photography; Tumor Burden; infrared; non-invasive; xenograft; reduction
• DOI: 10.1177/0023677214562653
• EISSN: 1758-1117
• ISSN: 0023-6772

Subcutaneous tumour xenograft volumes are generally measured using callipers. This method is susceptible to inter- and intra-observer variability and systematic inaccuracies. Non-invasive 3D measurement using ultrasound and magnetic resonance imaging (MRI) have been considered, but require immobilization of the animal. An infrared-based 3D time-of-flight (3DToF) camera was used to acquire a depth map of tumour-bearing mice. A semi-automatic algorithm based on parametric surfaces was applied to estimate tumour volume. Four clay mouse models and 18 tumour-bearing mice were assessed using callipers (applying both prolate spheroid and ellipsoid models) and 3DToF methods, and validated using tumour weight. Inter-experimentalist variability could be up to 25% in the calliper method. Experimental results demonstrated good consistency and relatively low error rates for the 3DToF method, in contrast to biased overestimation using callipers. Accuracy is currently limited by camera performance; however, we anticipate the next generation 3DToF cameras will be able to support the development of a practical system. Here, we describe an initial proof of concept for a non-invasive, non-immobilized, morphology-independent, economical and potentially more precise tumour volume assessment technique. This affordable technique should maximize the datapoints per animal, by reducing the numbers required in experiments and reduce their distress.