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New Features Extracted From Renal Stone NCCT Images to Predict Retreatment After Shock Wave Lithotripsy (SWL)
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Author(s): Toktam Khatibi (Tarbiat Modares University (TMU), Iran), Mohammad Mehdi Sepehri (Tarbiat Modares University (TMU), Iran), Mohammad Javad Soleimani (Iran University of Medical Sciences (IUMS), Iran)and Pejman Shadpour (Iran University of Medical Sciences (IUMS), Iran)
Copyright: 2017
Pages: 21
Source title:
Handbook of Research on Data Science for Effective Healthcare Practice and Administration
Source Author(s)/Editor(s): Elham Akhond Zadeh Noughabi (University of Calgary, Canada), Bijan Raahemi (University of Ottawa, Canada), Amir Albadvi (Tarbiat Modares University, Iran)and Behrouz H. Far (University of Calgary, Canada)
DOI: 10.4018/978-1-5225-2515-8.ch013
PurchaseView on the publisher's website for pricing and purchasing information.
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Abstract
Shock wave lithotripsy (SWL) is a noninvasive and safe treatment for small renal stones. In unsuccessful cases, retreatment procedures are needed after SWL. According to the previous studies, patient and stone descriptors are good predictors of SWL success. Some stone and kidney descriptors are measured from renal Non-Contrast Computed Tomography (NCCT) images. It is a tedious, time-consuming and error-prone process with large inter-user variability when performed manually. In this study, novel features are proposed automatically extracted from NCCT images to describe morphology and location of renal stones and kidneys to predict retreatments after SWL. The proposed features can distinguish between different kidney and stone morphologies and locations while being less sensitive to image segmentation errors. These features are added to other stone and patient features to predict retreatment within 3 months after SWL. The experimental results show that using the proposed stone features extracted from NCCT images can improve the accuracy of predicting retreatment.
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