Evidence map›Paper›PMID 39791584›Full record

ArticleInvestigative and clinical urology2025

Artificial intelligence can help individualize Wilms tumor treatment by predicting tumor response to preoperative chemotherapy.

Ahmed Nashat, Ahmed Alksas, Rasha T Aboulelkheir, Ahmed Elmahdy, Sherry M Khater, Hossam M Balaha, Israa Sharaby, Mohamed Shehata, Mohammed Ghazal, Salama Abd El-Wadoud and 3 more

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Article in Investigative and clinical urology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Ahmed NashatDepartment of Urology, Mansoura Urology and Nephrology Center, Mansoura University, Mansoura, Egypt.ORCID 0000-0002-7371-6144
Ahmed AlksasBioengineering Department, University of Louisville, Louisville, KY, USA.ORCID 0000-0001-9409-931X
Rasha T AboulelkheirDepartment of Radiology, Mansoura Urology and Nephrology Center, Mansoura University, Mansoura, Egypt.ORCID 0000-0002-4250-8772
Ahmed ElmahdyDepartment of Radiology, Mansoura Urology and Nephrology Center, Mansoura University, Mansoura, Egypt.
Sherry M KhaterDepartment of Pathology, Mansoura Urology and Nephrology Center, Mansoura University, Mansoura, Egypt.ORCID 0000-0001-5988-9711
Hossam M BalahaBioengineering Department, University of Louisville, Louisville, KY, USA.ORCID 0000-0002-0686-4411
Israa SharabyBioengineering Department, University of Louisville, Louisville, KY, USA.ORCID 0000-0003-2287-3315
Mohamed ShehataBioengineering Department, University of Louisville, Louisville, KY, USA.ORCID 0000-0001-6640-6183
Mohammed GhazalElectrical, Computer and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi, United Arab Emirates.ORCID 0000-0002-9045-6698
Salama Abd El-WadoudDepartment of Emergency Medicine, Royal Berkshire Hospital, Reading, UK.ORCID 0009-0000-6893-0018
Ayman El-BazBioengineering Department, University of Louisville, Louisville, KY, USA.ORCID 0000-0001-7264-1323
Ahmed MosbahDepartment of Urology, Mansoura Urology and Nephrology Center, Mansoura University, Mansoura, Egypt.
Ahmed AbdelhalimDepartment of Urology, Mansoura Urology and Nephrology Center, Mansoura University, Mansoura, Egypt.ORCID 0000-0002-4451-0032

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo create a computer-aided prediction (CAP) system to predict Wilms tumor (WT) responsiveness to preoperative chemotherapy (PC) using pre-therapy contrast-enhanced computed tomography (CECT). MATERIALS AND

methodsA single-center database was reviewed for children <18 years diagnosed with WT and received PC between 2001 and 2021. Patients were excluded if pre- and post-PC CECT were not retrievable. According to the Response Evaluation Criteria in Solid Tumors criteria, volumetric response was considered favorable if PC resulted in ≥30% tumor volume reduction. Histological response was considered favorable if post-nephrectomy specimens had ≥66% necrosis. Four steps were used to create the prediction model: tumor delineation; extraction of shape, texture and functionality-based features; integration of the extracted features and selection of the prediction model with the highest diagnostic performance. K-fold cross-validation allowed the presentation of all data in the training and testing phases.

resultsA total of 63 tumors in 54 patients were used to train and test the prediction model. Patients were treated with 4-8 weeks of vincristine/actinomycin-D combination. Favorable volumetric and histologic responses were achieved in 46 tumors (73.0%) and 38 tumors (60.3%), respectively. Among machine learning classifiers, support vector machine had the best diagnostic performance with an accuracy, sensitivity, and specificity of 95.24%, 95.65%, and 94.12% for volumetric and 84.13%, 89.47%, 88% for histologic response prediction.

conclusionsBased on pre-therapy CECT, CAP systems can help identify WT that are less likely to respond to PC with excellent accuracy. These tumors can be offered upfront surgery, avoiding the cons of PC.

Indexed as

Artificial IntelligenceKidney NeoplasmsTomography, X-Ray ComputedWilms TumorAdolescentAntineoplastic Combined Chemotherapy ProtocolsChildChild, PreschoolDactinomycinFemaleHumansInfantMaleNephrectomyPrecision MedicinePreoperative CareDactinomycinVincristineArtificial intelligenceNeoadjuvant therapySensitivity and specificityTomography, spiral computedWilms tumor

Identifiers

PMID39791584
PMCPMC11729221

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.