背景:癌症患者经常经历与癌症及其治疗相关的严重和令人痛苦的症状。预测癌症患者的症状仍然是临床医生和研究人员面临的重大挑战。机器学习(ML)的快速发展凸显了对当前系统评价以改善癌症症状预测的必要性。
目的:本系统综述旨在综合使用ML算法预测癌症症状发展的文献,并确定这些症状的预测因子。这对于整合新的发展和确定现有文献中的差距至关重要。
方法:我们根据PRISMA(系统评价和荟萃分析的首选报告项目)核对表进行了系统评价。我们对CINAHL进行了系统的搜索,Embase,和PubMed,用于1984年至2023年8月11日发布的英语记录,使用以下搜索词:癌症,肿瘤,具体症状,神经网络,机器学习,特定的算法名称,和深度学习。所有符合资格标准的记录由2位共同作者单独审查,并提取和合成了关键发现。我们专注于使用ML算法预测癌症症状的研究,不包括非人类研究,技术报告,reviews,书籍章节,会议记录,和无法访问的全文。
结果:共纳入42项研究,其中大部分是在2017年之后发布的。大多数研究在北美(18/42,43%)和亚洲(16/42,38%)进行。大多数研究(27/42,64%)的样本量通常在100至1000名参与者之间。最流行的算法类别是监督ML,占42项研究中的39项(93%)。每一种方法——深度学习,集成分类器,在42项研究中,无监督的ML构成了3项(3%)。性能最好的ML算法是逻辑回归(9/42,17%),随机森林(7/42,13%),人工神经网络(5/42,9%),和决策树(5/42,9%)。最常见的原发癌部位是头颈部(9/42,22%)和乳腺(8/42,19%),42项研究中有17项(41%)未指定该地点。最常见的症状是口干症(9/42,14%),抑郁症(8/42,13%),疼痛(8/42,13%),和疲劳(6/42,10%)。重要的预测因素是年龄,性别,治疗类型,治疗编号,癌症部位,癌症阶段,化疗,放射治疗,慢性疾病,合并症,物理因素,和心理因素。
结论:这篇综述概述了用于预测癌症患者症状的算法。鉴于癌症患者症状的多样性,可以处理复杂和非线性关系的分析方法是至关重要的。这些知识可以为制定针对特定症状的算法铺平道路。此外,为了提高预测精度,未来的研究应该将深度学习和集成方法等前沿机器学习策略与传统的统计模型进行比较。
BACKGROUND: People with cancer frequently experience severe and distressing symptoms associated with cancer and its treatments. Predicting symptoms in patients with cancer continues to be a significant challenge for both clinicians and researchers. The rapid evolution of machine learning (ML) highlights the need for a current systematic review to improve cancer symptom prediction.
OBJECTIVE: This systematic review aims to synthesize the literature that has used ML algorithms to predict the development of cancer symptoms and to identify the predictors of these symptoms. This is essential for integrating new developments and identifying gaps in existing literature.
METHODS: We conducted this systematic review in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist. We conducted a systematic search of CINAHL, Embase, and PubMed for English records published from 1984 to August 11, 2023, using the following search terms: cancer, neoplasm, specific symptoms, neural networks, machine learning, specific algorithm names, and deep learning. All records that met the eligibility criteria were individually reviewed by 2 coauthors, and key findings were extracted and synthesized. We focused on studies using ML algorithms to predict cancer symptoms, excluding nonhuman research, technical reports, reviews, book chapters, conference proceedings, and inaccessible full texts.
RESULTS: A total of 42 studies were included, the majority of which were published after 2017. Most studies were conducted in North America (18/42, 43%) and Asia (16/42, 38%). The sample sizes in most studies (27/42, 64%) typically ranged from 100 to 1000 participants. The most prevalent category of algorithms was supervised ML, accounting for 39 (93%) of the 42 studies. Each of the methods-deep learning, ensemble classifiers, and unsupervised ML-constituted 3 (3%) of the 42 studies. The ML algorithms with the best performance were logistic regression (9/42, 17%), random forest (7/42, 13%), artificial neural networks (5/42, 9%), and decision trees (5/42, 9%). The most commonly included primary cancer sites were the head and neck (9/42, 22%) and breast (8/42, 19%), with 17 (41%) of the 42 studies not specifying the site. The most frequently studied symptoms were xerostomia (9/42, 14%), depression (8/42, 13%), pain (8/42, 13%), and fatigue (6/42, 10%). The significant predictors were age, gender, treatment type, treatment number, cancer site, cancer stage, chemotherapy, radiotherapy, chronic diseases, comorbidities, physical factors, and psychological factors.
CONCLUSIONS: This review outlines the algorithms used for predicting symptoms in individuals with cancer. Given the diversity of symptoms people with cancer experience, analytic approaches that can handle complex and nonlinear relationships are critical. This knowledge can pave the way for crafting algorithms tailored to a specific symptom. In addition, to improve prediction precision, future research should compare cutting-edge ML strategies such as deep learning and ensemble methods with traditional statistical models.