BackgroundIncreasing evidence has indicated an association between immune infiltration in gastric cancer and clinical outcome. However, reliable prognostic signatures, based on systematic assessments of the immune landscape inferred from bulk tumour transcriptomes, have not been established. The aim was to develop an immune signature, based on the cellular composition of the immune infiltrate inferred from bulk tumour transcriptomes, to improve the prognostic predictions of gastric cancer.MethodsTwenty‐two types of immune cell fraction were estimated based on large public gastric cancer cohorts from the Gene Expression Omnibus using CIBERSORT. An immunoscore based on the fraction of immune cell types was then constructed using a least absolute shrinkage and selection operator (LASSO) Cox regression model.ResultsUsing the LASSO model, an immunoscore was established consisting of 11 types of immune cell fraction. In the training cohort (490 patients), significant differences were found between high‐ and low‐immunoscore groups in overall survival across and within subpopulations with an identical TNM stage. Multivariable analysis revealed that the immunoscore was an independent prognostic factor (hazard ratio 1·92, 95 per cent c.i. 1·54 to 2·40). The prognostic value of the immunoscore was also confirmed in the validation (210) and entire (700) cohorts.ConclusionThe proposed immunoscore represents a promising signature for estimating overall survival in patients with gastric cancer.

BackgroundDue to the occult anatomic location of the nasopharynx and frequent presence of adenoid hyperplasia, the positive rate for malignancy identification during biopsy is low, thus leading to delayed or missed diagnosis for nasopharyngeal malignancies upon initial attempt. Here, we aimed to develop an artificial intelligence tool to detect nasopharyngeal malignancies under endoscopic examination based on deep learning.MethodsAn endoscopic images-based nasopharyngeal malignancy detection model (eNPM-DM) consisting of a fully convolutional network based on the inception architecture was developed and fine-tuned using separate training and validation sets for both classification and segmentation. Briefly, a total of 28,966 qualified images were collected. Among these images, 27,536 biopsy-proven images from 7951 individuals obtained from January 1st, 2008, to December 31st, 2016, were split into the training, validation and test sets at a ratio of 7:1:2 using simple randomization. Additionally, 1430 images obtained from January 1st, 2017, to March 31st, 2017, were used as a prospective test set to compare the performance of the established model against oncologist evaluation. The dice similarity coefficient (DSC) was used to evaluate the efficiency of eNPM-DM in automatic segmentation of malignant area from the background of nasopharyngeal endoscopic images, by comparing automatic segmentation with manual segmentation performed by the experts.ResultsAll images were histopathologically confirmed, and included 5713 (19.7%) normal control, 19,107 (66.0%) nasopharyngeal carcinoma (NPC), 335 (1.2%) NPC and 3811 (13.2%) benign diseases. The eNPM-DM attained an overall accuracy of 88.7% (95% confidence interval (CI) 87.8%–89.5%) in detecting malignancies in the test set. In the prospective comparison phase, eNPM-DM outperformed the experts: the overall accuracy was 88.0% (95% CI 86.1%–89.6%) vs. 80.5% (95% CI 77.0%–84.0%). The eNPM-DM required less time (40 s vs. 110.0 ± 5.8 min) and exhibited encouraging performance in automatic segmentation of nasopharyngeal malignant area from the background, with an average DSC of 0.78 ± 0.24 and 0.75 ± 0.26 in the test and prospective test sets, respectively.ConclusionsThe eNPM-DM outperformed oncologist evaluation in diagnostic classification of nasopharyngeal mass into benign versus malignant, and realized automatic segmentation of malignant area from the background of nasopharyngeal endoscopic images.

PurposeTumor stroma cells play an important role in the carcinogenesis and progression of cancer. The aim of the present investigation was to explore the predictive role of carcinoma-associated fibroblasts (CAFs) and tumor-associated macrophages (TAMs) in nasopharyngeal carcinoma (NPC).Patients and methodsThe densities of CAFs and TAMs were measured by immunohistochemistry staining for α-smooth muscle actin (α-SMA), CD68, and CD163 in two sets of tissue microarrays including 260 pretreatment NPC tissues, that is, a training test comprising of 152 patients and a validation set comprising of 108 patients. Chi-square tests were performed for comparisons among the groups. Survival rates were estimated by using the Kaplan–Meier method and compared with log-rank tests. Cox proportional hazards models were used to identify significant independent variables.ResultsPatients older than 50 years showed a lower expression of CD68, and there was a positive relationship between the densities of CAFs and CD163+ TAMs (p=0.001). In the multivariate analysis of the training test, both α-SMA and CD163 were independent prognostic factors for overall survival and progression-free survival (all p<0.05). Based on the expression levels of α-SMA and CD163, patients were categorized into three groups: high-risk, intermediate-risk, and low-risk groups according to both high, either high, and both low, respectively. Survival analysis and Cox multivariate analysis showed that the risk groups based on α-SMA and CD163 expression were independent predictors for the survival of patients with NPC in the training test, which was also confirmed by the validation test.ConclusionA patient’s risk group based on the level of CD163+ TAMs and CAFs was an independent predictor of survival, which may facilitate patient counseling and individualized treatment.

This study investigated whether common comorbidities or biochemical factors, such as allergic disease, anemia, inflammation, and neurotransmitters, are singly or additively associated with an increased risk of attention deficit–hyperactivity disorder (ADHD). We recruited 216 children diagnosed with ADHD and 216 age-, sex-, height-, weight-, and class-matched controls from 31 elementary schools in Taipei, Taiwan. The International Study of Asthma and Allergies in Childhood questionnaire was used to measure allergic symptoms. Fasting venous blood was collected and analyzed for complete blood count, white blood cell differential count, immunoglobulin (Ig) E level, and serotonin (5-HT) level. The results showed that symptoms of both rhinitis (OR = 2.08, 95% CI = 1.42–3.05) and eczema (OR = 1.72, 95% CI = 1.02–2.88) were significantly associated with increased risk of ADHD. Children with ADHD showed considerably lower levels of hemoglobin (p = 0.001) and 5-HT (p < 0.001) and higher IgE level (p < 0.001) and eosinophil count (p = 0.001) than did control children. ADHD risk increased with the number of aforementioned biochemical risk factors present (one factor: OR = 1.87, 95% CI = 0.87–4.18; two factors: OR = 2.90, 95% CI = 1.29–6.48; three factors: OR = 4.47, 95% CI = 1.97–10.13; four factors: OR = 6.53, 95% CI = 2.43–17.57). Findings suggest that either ADHD’s etiology is multidimensional or the aforementioned conditions have shared etiology with ADHD.