Taha Haneen A, Zeilani Ruqayya S, Haddad Rabia H, Abdalrahim Maysoon S
School of Nursing, Clinical Nursing Department, The University of Jordan, Amman, Jordan.
Faculty of Nursing, Nursing Department, Philadelphia University. Amman, Jordan.
Digit Health. 2025 Jul 7;11:20552076251358315. doi: 10.1177/20552076251358315. eCollection 2025 Jan-Dec.
Neuropathic pain (NP) remains a complex, under-recognized complication among cancer patients, frequently arising from surgery, chemotherapy, or radiotherapy. Early prediction is crucial for timely intervention, yet conventional tools often fall short due to their reactive and subjective nature.
This systematic review aims to evaluate the application of artificial intelligence (AI) and machine learning (ML) techniques in predicting NP and related outcomes among oncology patients, highlighting model performance, predictors, and limitations.
Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, a comprehensive search was conducted across PubMed, EMBASE, Web of Science, IEEE Xplore, and Google Scholar for English-language studies published between January 2020 and February 2025. Fourteen eligible studies were included based on predefined Population, Intervention, Comparator, Outcome, Study Design (PICOS) criteria. The risk of bias was assessed using QUADAS-2 and PROBAST tools.
Most studies in high-income countries focused on breast cancer. Supervised models such as random forest (area under the receiver operating characteristic curve (AUC) up to 0.94), support vector machine (AUC 0.808-0.87), and deep learning architectures were dominant. Key predictive features included acute postoperative pain, anxiety, type of surgery, and biomarkers like sphinganine-1-phosphate. Only 14% of studies used external validation, and 5% assessed calibration. Multimodal frameworks integrating clinical, emotional, imaging, and molecular data outperformed single-modality models.
AI and ML hold significant promise for enhancing NP prediction in cancer care. However, methodological limitations-particularly poor calibration, low external validation, and limited interpretability-currently hinder clinical adoption. Standardization, explainable AI, and diverse datasets are essential for future progress.
神经病理性疼痛（NP）仍是癌症患者中一种复杂且未得到充分认识的并发症，常由手术、化疗或放疗引起。早期预测对于及时干预至关重要，但传统工具因其反应性和主观性往往效果不佳。
本系统评价旨在评估人工智能（AI）和机器学习（ML）技术在预测肿瘤患者NP及相关结局中的应用，突出模型性能、预测因素和局限性。
按照系统评价和Meta分析的首选报告项目（PRISMA）2020指南，在PubMed、EMBASE、科学网、IEEE Xplore和谷歌学术上全面检索2020年1月至2025年2月发表的英文研究。根据预先定义的人群、干预措施、对照、结局、研究设计（PICOS）标准纳入了14项符合条件的研究。使用QUADAS-2和PROBAST工具评估偏倚风险。
高收入国家的大多数研究集中在乳腺癌。随机森林（受试者工作特征曲线下面积（AUC）高达0.94）、支持向量机（AUC 0.808 - 0.87）等监督模型以及深度学习架构占主导地位。关键预测特征包括术后急性疼痛、焦虑、手术类型以及像鞘氨醇-1-磷酸这样的生物标志物。只有14%的研究使用了外部验证，5%评估了校准。整合临床、情绪、影像和分子数据的多模态框架优于单模态模型。
AI和ML在改善癌症护理中NP预测方面具有巨大潜力。然而，方法学上的局限性，特别是校准不佳、外部验证不足和可解释性有限，目前阻碍了临床应用。标准化、可解释的AI和多样化的数据集对未来进展至关重要。
