Prediction of Wilms’ Tumor Susceptibility to Preoperative Chemotherapy Using a Novel Computer-Aided Prediction System
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...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/13/3/486 |
| _version_ | 1827760199561117696 |
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| author | Israa Sharaby Ahmed Alksas Ahmed Nashat Hossam Magdy Balaha Mohamed Shehata Mallorie Gayhart Ali Mahmoud Mohammed Ghazal Ashraf Khalil Rasha T. Abouelkheir Ahmed Elmahdy Ahmed Abdelhalim Ahmed Mosbah Ayman El-Baz |
| author_facet | Israa Sharaby Ahmed Alksas Ahmed Nashat Hossam Magdy Balaha Mohamed Shehata Mallorie Gayhart Ali Mahmoud Mohammed Ghazal Ashraf Khalil Rasha T. Abouelkheir Ahmed Elmahdy Ahmed Abdelhalim Ahmed Mosbah Ayman El-Baz |
| author_sort | Israa Sharaby |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T09:48:12Z |
| format | Article |
| id | doaj.art-7b6cdf1a202e4594979bcf235a085c7d |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-11T09:48:12Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-7b6cdf1a202e4594979bcf235a085c7d2023-11-16T16:25:24ZengMDPI AGDiagnostics2075-44182023-01-0113348610.3390/diagnostics13030486Prediction of Wilms’ Tumor Susceptibility to Preoperative Chemotherapy Using a Novel Computer-Aided Prediction SystemIsraa Sharaby0Ahmed Alksas1Ahmed Nashat2Hossam Magdy Balaha3Mohamed Shehata4Mallorie Gayhart5Ali Mahmoud6Mohammed Ghazal7Ashraf Khalil8Rasha T. Abouelkheir9Ahmed Elmahdy10Ahmed Abdelhalim11Ahmed Mosbah12Ayman El-Baz13Bioengineering Department, University of Louisville, Louisville, KY 40292, USABioengineering Department, University of Louisville, Louisville, KY 40292, USAUrology and Nephrology Center, Mansoura University, Mansoura 35516, EgyptBioengineering Department, University of Louisville, Louisville, KY 40292, USABioengineering Department, University of Louisville, Louisville, KY 40292, USADepartment of Biology, Berea College, Berea, KY 40292, USABioengineering Department, University of Louisville, Louisville, KY 40292, USAElectrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab EmiratesCollege of Technological Innovation, Zayed University, Abu Dhabi 144534, United Arab EmiratesUrology and Nephrology Center, Mansoura University, Mansoura 35516, EgyptUrology and Nephrology Center, Mansoura University, Mansoura 35516, EgyptUrology and Nephrology Center, Mansoura University, Mansoura 35516, EgyptUrology and Nephrology Center, Mansoura University, Mansoura 35516, EgyptBioengineering Department, University of Louisville, Louisville, KY 40292, USAWilms’ 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.https://www.mdpi.com/2075-4418/13/3/486features engineeringmachine learningpreoperative chemotherapyWilms’ tumor |
| spellingShingle | Israa Sharaby Ahmed Alksas Ahmed Nashat Hossam Magdy Balaha Mohamed Shehata Mallorie Gayhart Ali Mahmoud Mohammed Ghazal Ashraf Khalil Rasha T. Abouelkheir Ahmed Elmahdy Ahmed Abdelhalim Ahmed Mosbah Ayman El-Baz Prediction of Wilms’ Tumor Susceptibility to Preoperative Chemotherapy Using a Novel Computer-Aided Prediction System Diagnostics features engineering machine learning preoperative chemotherapy Wilms’ tumor |
| title | Prediction of Wilms’ Tumor Susceptibility to Preoperative Chemotherapy Using a Novel Computer-Aided Prediction System |
| title_full | Prediction of Wilms’ Tumor Susceptibility to Preoperative Chemotherapy Using a Novel Computer-Aided Prediction System |
| title_fullStr | Prediction of Wilms’ Tumor Susceptibility to Preoperative Chemotherapy Using a Novel Computer-Aided Prediction System |
| title_full_unstemmed | Prediction of Wilms’ Tumor Susceptibility to Preoperative Chemotherapy Using a Novel Computer-Aided Prediction System |
| title_short | Prediction of Wilms’ Tumor Susceptibility to Preoperative Chemotherapy Using a Novel Computer-Aided Prediction System |
| title_sort | prediction of wilms tumor susceptibility to preoperative chemotherapy using a novel computer aided prediction system |
| topic | features engineering machine learning preoperative chemotherapy Wilms’ tumor |
| url | https://www.mdpi.com/2075-4418/13/3/486 |
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