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Background: 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). Methods: 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. Findings: 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. Interpretation: 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). Funding: 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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BACKGROUND: Surgical resection combined with adjuvant chemotherapy is considered as the gold-standard treatment for advanced colorectal cancer patients. These patients have a poor 5-year survival rate of 5% or less. Furthermore, a large dose of chemotherapy can produce adverse side effects and severe toxicity. Therefore, this retrospective study aimed to evaluate the efficacy of dendritic cell-cytokine-induced killer (DC-CIK) cell infusion as an adjuvant therapy in patients with advanced colorectal cancer combined with first-line treatment. METHODS: A total of 142 patients with stage III/IV colorectal carcinoma who had been treated with first-line therapy were included in this study. Among these patients, 71 patients received first-line treatment only (non-DC-CIK group), while the other 71 patients who had similar demographic and clinical characteristics received a DC-CIK cell infusion combined with first-line treatment (DC-CIK group). These patients were followed up until August 2014. Data were analyzed by Kaplan-Meier and Cox regression. RESULTS: Our results showed that the 5-year overall survival (OS) rate for the DC-CIK group versus the non-DC-CIK group was 41.3 versus 19.4% (p = 0.001) and the 5-year progression-free survival (PFS) rate for the DC-CIK group versus the non-DC-CIK group was 57.4 versus 33.6% (p = 0.022). CONCLUSIONS: Our results showed that patients with advanced colorectal cancer might benefit from DC-CIK immunotherapy combined with first-line therapy by significantly prolonging 5-year OS and PFS.