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

• Published in: Diagnostics (Basel) 2023-01, Vol.13 (3), p.486
• Main Authors: Sharaby, Israa, Alksas, Ahmed, Nashat, Ahmed, Balaha, Hossam Magdy, Shehata, Mohamed, Gayhart, Mallorie, Mahmoud, Ali, Ghazal, Mohammed, Khalil, Ashraf, Abouelkheir, Rasha T, Elmahdy, Ahmed, Abdelhalim, Ahmed, Mosbah, Ahmed, El-Baz, Ayman
• Format: Article
• Language: English
• Subjects: Abdomen; Algorithms; Cancer; Care and treatment; Chemotherapy; Computer-aided medical diagnosis; Datasets; features engineering; Kidney cancer; Machine learning; Magnetic resonance imaging; Methods; Nephroblastoma; Patient outcomes; Patients; Preoperative care; preoperative chemotherapy; Radiomics; Surgery; Tumors; Wilms’ tumor
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics13030486

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.