Correlation Morphometric Feature Analysis in Radiation Oncology
Received Date: Jun 16, 2022 / Published Date: Jul 21, 2022
Abstract
The information related to the shape and size of a tumour can be exploited from the morphometric feature analysis of medical images. The ability of extracting such features from a wide range of imaging modalities enables various clinical applications in radiation oncology. The morphometric features such as volume, surface area, and Surface to Volume Ratio (SVR), sphericity, asphercity, Spherical Disproportion (SD), compactness one and two were useful in detecting and distinguishing benign and malignant lesions, classifying histological subtypes of carcinomas, predicting prognosis and assessing response after therapy. The morphometric features have emerged as promising biomarkers with discriminative and predictive capabilities and their appropriate usage will allow for the development of clinically implementable radiomics models in radiation oncology.
Keywords: Shape; Morphology; Morphometry; Radiomics; Cancer; Tumour
Citation: Jayatilake ML, Sherminie LPG (2022) Correlation Morphometric Feature Analysis in Radiation Oncology. J Oncol Res Treat 7: 187. Doi: 10.4172/aot-1000187
Copyright: © 2022 Jayatilake ML, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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