Evidence map›Paper›PMID 40824376›Full record

ArticleInternational urology and nephrology2026

Personalized survival predictions in chromophobe renal cell carcinoma: development of a machine learning-based web tool.

Sakhr Alshwayyat, Noor Almasri, Yamen Alshwaiyat, Tala Abdulsalam Alshwayyat, Rewa AlAwwa, Mustafa Alshwayyat, Madhawi Hadi Jamaan, Muneera Ahmad Alhamadi, Ratib Mahfouz, Anas Alshwayat and 1 more

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Article in International urology and nephrology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Sakhr AlshwayyatKern Medical (UCLA David Geffen School of Medicine Affiliate), Bakersfield, CA, USA.
Noor AlmasriUniversity of Jordan, Amman, Jordan.
Yamen AlshwaiyatJordan University of Science and Technology, Irbid, Jordan.
Tala Abdulsalam AlshwayyatPrincess Basma Teaching Hospital, Irbid, Jordan.
Rewa AlAwwaBiopharmaceuticals Department, Uppsala University, Uppsala, Sweden.
Mustafa AlshwayyatFaculty of Medicine, Jordan University of Science & Technology, P.O Box 3030, Irbid, 2110, Jordan.
Madhawi Hadi JamaanMinistry of Health Kuwait, Kuwait City, Kuwait.
Muneera Ahmad AlhamadiMinistry of Health Kuwait, Kuwait City, Kuwait.
Ratib MahfouzNephrology Department, Henry Ford Hospital, Detroit, USA.
Anas AlshwayatNephrology Department, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
Mohammed Al-Mahdi Al-KurdiUniversity of Aleppo, Faculty of Medicine, Aleppo, Syrian Arab Republic. mohammedmahdikurdi@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeChromophobe renal cell carcinoma (ChRCC) is a rare subtype of renal cancer, characterized by distinct clinical and genetic features. Existing studies on ChRCC are limited, and there is a critical need to explore the prognostic factors and treatment outcomes in this patient population. We used machine learning (ML) to build prognostic models and developed the first predictive web-based tool for survival.

methodsThe SEER database (2000-2020) was used for this study's analysis. To identify the prognostic variables, we conducted Cox regression analysis and constructed prognostic models using five ML algorithms to predict the 5-year survival. A validation method incorporating the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to validate the accuracy and reliability of ML models. We also performed Kaplan-Meier survival analysis.

resultsOur study analyzed 10,700 patients with ChRCC and identified metastasis and tumor size as significant predictors of survival. Subtotal nephrectomy was associated with the highest survival rates. Chemotherapy and radiotherapy were infrequently used but were associated with worse survival outcomes, particularly in patients with metastasis. The developed ML models demonstrated high accuracy in predicting survival, and a web-based tool offered real-time survival predictions based on patient-specific data.

conclusionOur study identified key prognostic factors and developed a machine learning-based web tool for personalized survival predictions. Metastasis and tumor size are critical in determining patient outcomes, with subtotal nephrectomy showing the highest survival rate.

Indexed as

Carcinoma, Renal CellKidney NeoplasmsMachine LearningAgedFemaleHumansInternetMaleMiddle AgedPrognosisSEER ProgramSurvival RateKidney neoplasmsMachine learningNephrectomyPrognosisRenal cell carcinomaSurvival analysis

Identifiers

PMID40824376

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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.