IMPORTANCE Gastric neuroendocrine carcinoma and mixed adenoneuroendocrine carcinoma are rare pathological types of gastric cancer, and there is a lack of multicenter studies comparing the prognosis and recurrence patterns of gastric neuroendocrine carcinoma, gastric mixed adenoneuroendocrine carcinoma, and gastric adenocarcinoma.OBJECTIVE To compare the differences in long-term survival and patterns of recurrence among gastric neuroendocrine carcinoma, gastric mixed adenoneuroendocrine carcinoma, and gastric adenocarcinoma. DESIGN, SETTING, AND PARTICIPANTSThis cohort study included patients with resectable gastric neuroendocrine carcinoma and gastric mixed adenoneuroendocrine carcinoma at 23 hospitals in China from January 2006 to December 2016. In addition, patients with gastric adenocarcinoma were selected as controls. Propensity score-matched analysis was used to match pathological stage among the different pathological types, and disease-free survival (DFS), postrecurrence survival (PRS), and patterns of recurrence were examined. Data analysis was conducted from July 15, 2020, to October 21, 2020.EXPOSURES Curative resection for gastric neuroendocrine carcinoma, gastric mixed adenoneuroendocrine carcinoma, and gastric adenocarcinoma. MAIN OUTCOMES AND MEASURESThe main outcomes were DFS and patterns of recurrence. RESULTS A total of 3689 patients were analyzed (median [interquartile range] age, 62 [55][56][57][58][59][60][61][62][63][64][65][66][67][68][69] years; 2748 [74.5%] men), including 503 patients (13.6%) with gastric neuroendocrine carcinoma, 401 patients (10.9%) with gastric mixed adenoneuroendocrine carcinoma, and 2785 patients (75.5%) with gastric adenocarcinoma. After propensity score matching, 5-year DFS was 47.6% (95% CI, 42.7%-52.5%) for patients with gastric neuroendocrine carcinoma, compared with 57.6% (95% CI, 55.1%-60.1%) with gastric adenocarcinoma (P < .001) and 51.1% (95% CI, 46.0%-56.2%) for patients with gastric mixed adenoneuroendocrine carcinoma, compared with 57.8% (95% CI, 55.1%-60.5%) patients with gastric adenocarcinoma (P = .02). Multivariable analyses found that, compared with gastric adenocarcinoma, gastric neuroendocrine carcinoma (hazard ratio [HR], 1.64; 95% CI, 1.40-1.93) and gastric mixed adenoneuroendocrine carcinoma (HR, 1.25; 95% CI, 1.05-1.49) were independent risk factors associated with worse DFS. Compared with matched patients with gastric adenocarcinoma, patients with gastric neuroendocrine carcinoma were more likely to have distant recurrence (268 patients [17.2%] vs 101 patients [23.7%]; P = .002), as were patients with gastric mixed adenoneuroendocrine carcinoma (232 patients [17.3%] vs 76 patients [22.8%]; P = .02). In (continued) Key Points Question Are there any differences in prognoses or recurrence patterns associated with gastric neuroendocrine carcinoma, mixed adenoneuroendocrine carcinoma, or adenocarcinoma? Findings This cohort study included 3689 patients with resectable gastric adenocarcinoma, gastric neuroendocrine carcinoma, or gastric mixed adeno...

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IMPORTANCE Apatinib is a novel treatment option for chemotherapy-refractory advanced gastric cancer (GC), but it has not been evaluated in patients with locally advanced GC.OBJECTIVE To investigate the effectiveness and safety of apatinib combined with S-1 plus oxaliplatin (SOX) as a neoadjuvant treatment for locally advanced GC. DESIGN, SETTING, AND PARTICIPANTSThis multicenter, prospective, single-group, open-label, phase 2 nonrandomized controlled trial was conducted in 10 centers in southern China. Patients with M0 and either clinical T2 to T4 or N+ disease were enrolled between

Background Protein biomarkers play important roles in cancer diagnosis. Many efforts have been made on measuring abnormal expression intensity in biological samples to identity cancer types and stages. However, the change of subcellular location of proteins, which is also critical for understanding and detecting diseases, has been rarely studied. Results In this work, we developed a machine learning model to classify protein subcellular locations based on immunohistochemistry images of human colon tissues, and validated the ability of the model to detect subcellular location changes of biomarker proteins related to colon cancer. The model uses representative image patches as inputs, and integrates feature engineering and deep learning methods. It achieves 92.69% accuracy in classification of new proteins. Two validation datasets of colon cancer biomarkers derived from published literatures and the human protein atlas database respectively are employed. It turns out that 81.82 and 65.66% of the biomarker proteins can be identified to change locations. Conclusions Our results demonstrate that using image patches and combining predefined and deep features can improve the performance of protein subcellular localization, and our model can effectively detect biomarkers based on protein subcellular translocations. This study is anticipated to be useful in annotating unknown subcellular localization for proteins and discovering new potential location biomarkers.

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