{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T12:13:13Z","timestamp":1789042393386,"version":"build-2803163510"},"reference-count":84,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T00:00:00Z","timestamp":1788998400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/cancers18182937","type":"journal-article","created":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T11:38:39Z","timestamp":1789040319000},"page":"2937","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Prediction of Acute Kidney Injury in Oncology: From Clinical Risk Scores to AI-Enabled Precision Onco-Nephrology"],"prefix":"10.3390","volume":"18","author":[{"given":"Brena F.","family":"Sena","sequence":"first","affiliation":[{"name":"Department of Nephrology, AP-HP Sorbonne Universit\u00e9, 75013 Paris, France"},{"name":"ESIEE Paris, Universit\u00e9 Gustave Eiffel, 93162 Noisy-le-Grand CEDEX, France"},{"name":"Laboratoire de Biom\u00e9canique Appliqu\u00e9e (LBA), UMR T24, Aix-Marseille Universit\u00e9, Universit\u00e9 Gustave Eiffel, 51 Boulevard Pierre Dramard, 13015 Marseille, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexandre","family":"Homo","sequence":"additional","affiliation":[{"name":"Department of Nephrology, AP-HP Sorbonne Universit\u00e9, 75013 Paris, France"},{"name":"European Renal Association Onconephrology Working Group"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Camille","family":"Fahy","sequence":"additional","affiliation":[{"name":"ESIEE Paris, Universit\u00e9 Gustave Eiffel, 93162 Noisy-le-Grand CEDEX, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anthony","family":"Van de Putte","sequence":"additional","affiliation":[{"name":"Department of Nephrology, AP-HP Sorbonne Universit\u00e9, 75013 Paris, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bertrand","family":"Roudier","sequence":"additional","affiliation":[{"name":"Laboratoire de Biom\u00e9canique Appliqu\u00e9e (LBA), UMR T24, Aix-Marseille Universit\u00e9, Universit\u00e9 Gustave Eiffel, 51 Boulevard Pierre Dramard, 13015 Marseille, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8258-5759","authenticated-orcid":false,"given":"Adrien","family":"Ugon","sequence":"additional","affiliation":[{"name":"ESIEE Paris, Universit\u00e9 Gustave Eiffel, 93162 Noisy-le-Grand CEDEX, France"},{"name":"Laboratoire de Biom\u00e9canique Appliqu\u00e9e (LBA), UMR T24, Aix-Marseille Universit\u00e9, Universit\u00e9 Gustave Eiffel, 51 Boulevard Pierre Dramard, 13015 Marseille, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1048-6636","authenticated-orcid":false,"given":"Corinne","family":"Isnard Bagnis","sequence":"additional","affiliation":[{"name":"Department of Nephrology, AP-HP Sorbonne Universit\u00e9, 75013 Paris, France"},{"name":"European Renal Association Onconephrology Working Group"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1097\/MNH.0000000000001001","article-title":"Cancer Drugs and Acute Kidney Injury: New Therapies and New Challenges","volume":"33","author":"Gork","year":"2024","journal-title":"Curr. 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