Rapid Segmentation of Renal Tumours to Calculate Volume Using 3D Interpolation.
Michael Y ChenMaria A WoodruffBoon KuaNicholas J RukinPublished in: Journal of digital imaging (2021)
Small renal masses are commonly diagnosed with modern medical imaging. Renal tumour volume has been explored as a prognostic tool to help decide when intervention is needed and appears to provide additional prognostic information for smaller tumours compared with tumour diameter. However, the current method of calculating tumour volume in clinical practice uses the ellipsoid equation (π/6 × length × width × height) which is an oversimplified approach. Some research groups trace the contour of the tumour in every image slice which is impractical for clinical use. In this study, we demonstrate a method of using 3D segmentation software and the 3D interpolation method to rapidly calculate renal tumour volume in under a minute. Using this method in 27 patients that underwent radical or partial nephrectomy, we found a 10.07% mean absolute difference compared with the traditional ellipsoid method. Our segmentation volume was closer to the calculated histopathological tumour volume than the traditional method (p = 0.03) with higher Lin's concordance correlation coefficient (0.79 vs 0.72). 3D segmentation has many uses related to 3D printing and modelling and is becoming increasingly common. Calculation of tumour volume is one additional benefit it provides. Further studies on the association between segmented tumour volume and prognosis are needed.
Keyphrases
- deep learning
- clinical practice
- convolutional neural network
- healthcare
- body mass index
- randomized controlled trial
- high resolution
- ejection fraction
- newly diagnosed
- magnetic resonance imaging
- social media
- machine learning
- risk assessment
- prognostic factors
- computed tomography
- magnetic resonance
- chronic kidney disease
- health information