未经证实：舌头图像（颜色，舌头的大小和形状以及颜色，舌苔的厚度和水分含量)，根据中医理论反映全身的健康状况，已经在中国广泛使用了数千年。在这里，我们调查了舌象和舌苔微生物组在胃癌（GC）诊断中的价值。
UNASSIGNED：从2020年5月到2021年1月，我们同时收集了中国328名GC患者（所有新诊断为GC）和304名非胃癌（NGC）参与者的舌象和舌苔样本，和16SrDNA用于表征舌苔样品的微生物组。然后，建立人工智能(AI)深度学习模型,评估舌象和舌苔微生物组在GC诊断中的价值。考虑到舌成像作为诊断工具更方便、更经济,我们于2020年5月至2022年3月在中国进一步开展了一项前瞻性多中心临床研究,招募了来自中国10个中心的937例GC患者和1911例NGC患者,以进一步评估舌象在GC诊断中的作用.此外，我们在另一个独立的外部验证队列中验证了该方法,该队列包括来自7个中心的294例GC患者和521例NGC患者.这项研究在ClinicalTrials.gov注册，NCT01090362。
未经评估：第一次，我们发现舌象和舌苔微生物组可以作为GC诊断的工具,基于舌象的诊断模型的曲线下面积(AUC)值为0.89。基于舌苔微生物组的模型的AUC值使用属数据达到0.94,使用物种数据达到0.95。前瞻性多中心临床研究结果表明,三种基于舌象的GCs模型的AUC值在内部验证中达到0.88-0.92,在独立外部验证中达到0.83-0.88,显着优于八种血液生物标志物的组合。
UNASSIGNED：我们的结果表明，舌头图像可作为GC诊断的稳定方法，并且显着优于常规血液生物标志物。我们开发的三种基于舌图像的AI深度学习诊断模型可用于充分区分GC患者和NGC参与者，甚至早期GC和癌前病变，如萎缩性胃炎（AG）。
未经批准：国家重点研发计划（2021YFA0910100），浙江省中医药科技计划方案(2018ZY006),浙江省医学科技项目(2022KY114,WKJ-ZJ-2104),浙江省上消化道肿瘤研究中心(JBZX-202006),浙江省自然科学基金(HDMY22H160008),浙江省科技项目(2019C03049),国家自然科学基金(82074245,81973634,82204828),中国博士后科学基金（2022M713203）。
UNASSIGNED: Tongue images (the colour, size and shape of the tongue and the colour, thickness and moisture content of the tongue coating), reflecting the health state of the whole body according to the theory of traditional Chinese medicine (TCM), have been widely used in China for thousands of years. Herein, we investigated the value of tongue images and the tongue coating microbiome in the diagnosis of gastric cancer (GC).
UNASSIGNED: From May 2020 to January 2021, we simultaneously collected tongue images and tongue coating samples from 328 patients with GC (all newly diagnosed with GC) and 304 non-gastric cancer (NGC) participants in China, and 16 S rDNA was used to characterize the microbiome of the tongue coating samples. Then, artificial intelligence (AI) deep learning models were established to evaluate the value of tongue images and the tongue coating microbiome in the diagnosis of GC. Considering that tongue imaging is more convenient and economical as a diagnostic tool, we further conducted a prospective multicentre clinical
study from May 2020 to March 2022 in China and recruited 937 patients with GC and 1911 participants with NGC from 10 centres across China to further evaluate the role of tongue images in the diagnosis of GC. Moreover, we verified this approach in another independent external validation cohort that included 294 patients with GC and 521 participants with NGC from 7 centres. This
study is registered at ClinicalTrials.gov, NCT01090362.
UNASSIGNED: For the first time, we found that both tongue images and the tongue coating microbiome can be used as tools for the diagnosis of GC, and the area under the curve (AUC) value of the tongue image-based diagnostic model was 0.89. The AUC values of the tongue coating microbiome-based model reached 0.94 using genus data and 0.95 using species data. The results of the prospective multicentre clinical
study showed that the AUC values of the three tongue image-based models for GCs reached 0.88-0.92 in the internal verification and 0.83-0.88 in the independent external verification, which were significantly superior to the combination of eight blood biomarkers.
UNASSIGNED: Our results suggest that tongue images can be used as a stable method for GC diagnosis and are significantly superior to conventional blood biomarkers. The three kinds of tongue image-based AI deep learning diagnostic models that we developed can be used to adequately distinguish patients with GC from participants with NGC, even early GC and precancerous lesions, such as atrophic gastritis (AG).
UNASSIGNED: The National Key R&D Program of China (2021YFA0910100), Program of Zhejiang Provincial TCM Sci-tech Plan (2018ZY006), Medical Science and Technology Project of Zhejiang Province (2022KY114, WKJ-ZJ-2104), Zhejiang Provincial Research Center for Upper Gastrointestinal Tract Cancer (JBZX-202006), Natural Science Foundation of Zhejiang Province (HDMY22H160008), Science and Technology Projects of Zhejiang Province (2019C03049), National Natural Science Foundation of China (82074245, 81973634, 82204828), and Chinese Postdoctoral Science Foundation (2022M713203).
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