互联网是患者获取健康信息的主要来源，为医生的关怀提供补充。Google趋势(GT)是一种常用工具，可以探索人们对健康相关现象的兴趣。尽管GT研究的数量越来越多，但尚未有专门针对肿瘤学的研究，因此需要进行系统综述以填补这一空白。我们的目标是系统地描述使用GT进行肿瘤学研究的相关研究，以描述其效用和偏倚。我们包括所有使用GT分析与恶性肿瘤相关的Google搜索的研究。我们排除了除英语以外的其他语言编写的研究。搜索是在2022年8月1日使用PubMed引擎进行的。我们使用以下搜索输入: "Google trends" AND ("oncology" OR "cancer" or "malignancy" OR "tumor" OR "lymphoma" OR "multiple myeloma" OR "leukemia")。我们分析了使用搜索项而非主题、缺乏与实际数据对比以及缺乏敏感性分析等偏倚来源。我们进行了描述性统计学分析。总共包括了85篇文章。第一篇使用GT进行肿瘤学研究的研究于2013年发表，此后，每年发表的文章数量逐渐增加。这些研究可以归类如下：22% (19/85)与预防有关，20% (17/85)与意识活动有关，11% (9/85)与名人相关，13% (11/85)与COVID-19相关，而47% (40/85)属于其他类别。最常分析的癌症类型是乳腺癌 (n=28)，前列腺癌 (n=26)，肺癌 (n=18)和结直肠癌 (n=18)。我们发现在85个研究中，17个(20%)承认使用GT主题而非搜索项，79个(93%)披露了重现其结果所需的所有搜索输入细节，34个(40%)将GT统计数据与实际数据进行了比较。分析GT数据的最常用方法是相关性分析(55/85, 65%)和峰值分析(43/85, 51%)。只有11%(9/85)的研究的作者进行了敏感性分析。使用GT数据进行肿瘤学研究的研究数量逐年增加。本系统综述所包括的研究展示了各种令人关注的主题、搜索策略和统计方法。最常分析的癌症类型是乳腺癌、前列腺癌、肺癌、结直肠癌、皮肤癌和宫颈癌，这可能反映了它们在人群中的患病率或公众的兴趣。尽管大多数研究人员提供了可重现的搜索输入，但只有五分之一使用了GT主题而非搜索项，许多研究缺乏敏感性分析。使用GT进行医学研究的科学家应确保研究的质量，提供透明的搜索策略以重现结果，优先使用主题而非搜索项，并进行稳健的统计计算和敏感性分析。©Mikołaj Kamiński，Jakub Czarny，Piotr Skrzypczak，Krzysztof Sienicki，Magdalena Roszak。最初发表于医学互联网研究杂志 (https://www.jmir.org)，04.08.2023。

The internet is a primary source of health information for patients, supplementing physician care. Google Trends (GT), a popular tool, allows the exploration of public interest in health-related phenomena. Despite the growing volume of GT studies, none have focused explicitly on oncology, creating a need for a systematic review to bridge this gap.We aimed to systematically characterize studies related to oncology using GT to describe its utilities and biases.We included all studies that used GT to analyze Google searches related to malignancies. We excluded studies written in languages other than English. The search was performed using the PubMed engine on August 1, 2022. We used the following search input: "Google trends" AND ("oncology" OR "cancer" or "malignancy" OR "tumor" OR "lymphoma" OR "multiple myeloma" OR "leukemia"). We analyzed sources of bias that included using search terms instead of topics, lack of confrontation of GT statistics with real-world data, and absence of sensitivity analysis. We performed descriptive statistics.A total of 85 articles were included. The first study using GT for oncology research was published in 2013, and since then, the number of publications has increased annually. The studies were categorized as follows: 22% (19/85) were related to prophylaxis, 20% (17/85) pertained to awareness events, 11% (9/85) were celebrity-related, 13% (11/85) were related to COVID-19, and 47% (40/85) fell into other categories. The most frequently analyzed cancers were breast (n=28), prostate (n=26), lung (n=18), and colorectal cancers (n=18). We discovered that of the 85 studies, 17 (20%) acknowledged using GT topics instead of search terms, 79 (93%) disclosed all search input details necessary for replicating their results, and 34 (40%) compared GT statistics with real-world data. The most prevalent methods for analyzing the GT data were correlation analysis (55/85, 65%) and peak analysis (43/85, 51%). The authors of only 11% (9/85) of the studies performed a sensitivity analysis.The number of studies related to oncology using GT data has increased annually. The studies included in this systematic review demonstrate a variety of concerning topics, search strategies, and statistical methodologies. The most frequently analyzed cancers were breast, prostate, lung, colorectal, skin, and cervical cancers, potentially reflecting their prevalence in the population or public interest. Although most researchers provided reproducible search inputs, only one-fifth used GT topics instead of search terms, and many studies lacked a sensitivity analysis. Scientists using GT for medical research should ensure the quality of studies by providing a transparent search strategy to reproduce results, preferring to use topics over search terms, and performing robust statistical calculations coupled with sensitivity analysis.©Mikołaj Kamiński, Jakub Czarny, Piotr Skrzypczak, Krzysztof Sienicki, Magdalena Roszak. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 04.08.2023.