Prediction of Wilms’ Tumor Susceptibility to Preoperative Chemotherapy Using a Novel Computer-Aided Prediction System

dc.contributor.authorSharaby, I.,
dc.contributor.authorAlksas, A.,
dc.contributor.authorNashat, A.,
dc.contributor.authorBalaha, H.M.,
dc.contributor.authorShehata, M.
dc.contributor.authorGayhart, M.
dc.contributor.authorMahmoud, A.
dc.contributor.authorGhazal, M
dc.contributor.authorKhalil, A.
dc.contributor.authorAbouelkheir, R.T
dc.contributor.authorETAL..
dc.date.accessioned2024-01-31T07:43:35Z
dc.date.available2024-01-31T07:43:35Z
dc.date.issued2023-01
dc.descriptionWilms’ tumor (WT) is the most common primary renal tumor in children, and it is the second most prevalent intra-abdominal malignancy in children. It accounts for over 90% of all kidney tumors in children under the age of 18 years and represents 4% of all childhood malignancies. The annual incidence of WT in the United States is approximately 500 to 600 children under the age of 15, with a peak incidence between 3 and 4 years of age [1,2].
dc.description.abstractAbstract: Wilms’ tumor, the most prevalent renal tumor in children, is known for its aggressive prognosis and recurrence. Treatment of Wilms’ tumor is multimodal, including surgery, chemotherapy, and occasionally, radiation therapy. Preoperative chemotherapy is used routinely in European studies and in select indications in North American trials. The objective of this study was to build a novel computer-aided prediction system for preoperative chemotherapy response in Wilms’ tumors. A total of 63 patients (age range: 6 months–14 years) were included in this study, after receiving their guardians’ informed consent. We incorporated contrast-enhanced computed tomography imaging to extract the texture, shape, and functionality-based features from Wilms’ tumors before chemotherapy. The proposed system consists of six steps: (i) delineate the tumors’ images across the three contrast phases; (ii) characterize the texture of the tumors using first- and second-order textural features; (iii) extract the shape features by applying a parametric spherical harmonics model, sphericity, and elongation; (iv) capture the intensity changes across the contrast phases to describe the tumors’ functionality; (v) apply features fusion based on the extracted features; and (vi) determine the final prediction as responsive or non-responsive via a tuned support vector machine classifier. The system achieved an overall accuracy of 95.24%, with 95.65% sensitivity and 94.12% specificity. Using the support vector machine along with the integrated features led to superior results compared with other classification models. This study integrates novel imaging markers with a machine learning classification model to make early predictions about how a Wilms’ tumor will respond to preoperative chemotherapy. This can lead to personalized management plans for Wilms’ tumors. Keywords: features engineering; machine learning; preoperative chemotherapy; Wilms’ tumor
dc.identifier.citationSharaby, I., Alksas, A., Nashat, A., Balaha, H. M., Shehata, M., Gayhart, M., ... & El-Baz, A. (2023). Prediction of wilms’ tumor susceptibility to preoperative chemotherapy using a novel computer-aided prediction system. Diagnostics, 13(3), 486.
dc.identifier.doihttps://doi.org/10.3390/diagnostics13030486
dc.identifier.urihttps://dspace.adu.ac.ae/handle/1/558
dc.language.isoen
dc.publisherMDPI
dc.titlePrediction of Wilms’ Tumor Susceptibility to Preoperative Chemotherapy Using a Novel Computer-Aided Prediction System
dc.typeArticle

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
diagnostics-13-00486.pdf
Size:
957.94 KB
Format:
Adobe Portable Document Format

License bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed to upon submission
Description: