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Acute Kidney Injury Research / Renal Cell Carcinoma Treatment · Journal article
Cancers · September 10, 2026
A consensus or society position rather than new primary data.
This narrative review examines conventional and AI/ML-based approaches to AKI prediction in oncology, identifying that evidence is most developed for hospitalized cancer patients, cisplatin exposure, contrast-enhanced CT, and immune checkpoint inhibitors, but notes critical gaps in prospective validation, standardization, and outcome improvement demonstration. No oncology-specific AKI prediction model has yet shown improved clinical outcomes in a prospective interventional trial. The authors conclude that clinical translation will require standardized outcomes, treatment-aware data, rigorous external validation, and integration with evidence-based response pathways.
Narrative review. Published studies on AKI prediction in cancer care; emphasis on oncology-specific models and conventional risk scores. Intervention: AI/ML-based and conventional clinical AKI prediction models and risk scores.
Prediction evidence is most developed in hospitalized cancer populations, cisplatin exposure, contrast-enhanced computed tomography, immune checkpoint inhibitor therapy, and selected oncologic surgical procedures Hematopoietic stem cell transplantation includes an early conventional risk score; CAR T-cell therapy and targeted therapies remain predominantly observational Most studies are retrospective; independent external validation, calibration assessment, fairness evaluation, and prospective workflow implementation remain uncommon
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
Clinicians should recognize that while AI/ML tools for AKI prediction in oncology are emerging, no validated model has yet demonstrated improved patient outcomes in a prospective trial. Current evidence supports careful consideration of conventional risk factors in high-risk oncology populations, but implementation of prediction tools requires independent validation and linkage to actionable kidney protection strategies before widespread clinical adoption.
A narrative review synthesizing evidence on AKI prediction in oncology that identifies gaps, methodological limitations, and priorities for clinical translation rather than reporting a primary empirical finding.
As stated by the source record.
Clinicians should recognize that while AI/ML tools for AKI prediction in oncology are emerging, no validated model has yet demonstrated improved patient outcomes in a prospective trial. Current evidence supports careful consideration of conventional risk factors in high-risk oncology populations, but implementation of prediction tools requires independent validation and linkage to actionable kidney protection strategies before widespread clinical adoption.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
What is missing. This record has no reported figures. That is a gap in the analysis, not a judgement about the study.
Background/Objectives: Acute kidney injury (AKI) is a common complication across the cancer care continuum and can compromise kidney function, delay or interrupt anticancer therapy, and worsen renal and oncologic outcomes. This state-of-the-art narrative review examines conventional clinical risk scores and artificial intelligence (AI)- and machine learning (ML)-based approaches to AKI prediction in oncology and identifies priorities for clinical translation. Methods: We conducted iterative searches of PubMed and Google Scholar through July 2026 and screened reference lists of relevant primary studies and reviews. We prioritized oncology-specific model development and validation studies and selectively included conventional scores and observational evidence where dedicated prediction models were unavailable. Results: Prediction evidence is most developed in hospitalized cancer populations, cisplatin exposure, contrast-enhanced computed tomography, immune checkpoint inhibitor therapy, and selected oncologic surgical procedures. Hematopoietic stem cell transplantation includes an early conventional risk score, whereas evidence for CAR T-cell therapy and targeted therapies remains predominantly observational. Most studies are retrospective, and independent external validation, calibration assessment, fairness evaluation, and prospective workflow implementation remain uncommon. Reported performance cannot be compared directly across studies because outcome definitions, prediction windows, populations, and validation strategies differ. Conclusions: AI- and ML-based AKI prediction may support precision onco-nephrology, but no oncology-specific model has yet demonstrated improved outcomes in a prospective interventional study. Clinical progress will require standardized outcomes, treatment-aware longitudinal data, rigorous external validation, and prediction tools linked to evidence-based response pathways. These advances may ultimately enable precision onco-nephrology by supporting proactive kidney protection while preserving optimal cancer treatment.
Taken from the source record, never inferred. Follow any of these and new work involving them reaches your briefing.