Primary small cell esophageal carcinoma is a rare and aggressive type of gastrointestinal cancer with poor prognosis. In the present study, the impact of tumour infiltrating inflammatory cells on clinico-pathological characteristics and the patients’ prognosis were analysed. A total of 36 small cell esophageal carcinomas, 19 adjacent normal tissues and 16 esophageal squamous cell carcinoma samples were collected. Qualified pathologists examined eosinophils, neutrophils, lymphocytes and macrophages on histochemical slides. The infiltration of eosinophils and macrophages in small cell esophageal carcinoma was significantly increased as compared with tumor adjacent normal tissues, and was significantly less in esophageal squamous cell carcinoma. Macrophage count was significantly associated with (p = 0.015) lymph node—stage in small cell esophageal carcinoma. When we grouped patients into two groups by counts of infiltrated inflammatory cells, Kaplan-Meier analysis revealed that high macrophage infiltration group (p = 0.004) and high eosinophil infiltration group (p = 0.027) had significantly enhanced survival. In addition, multivariate analysis unveiled that eosinophil count (p = 0.002) and chemotherapy (Yes vs. No, p = 0.001) were independent prognostic indicators. Taken together, infiltration of macrophages and eosinophils into the solid tumor appear to be important in the progression of small cell esophageal carcinoma and patients’ prognosis.

Background: Accurate and robust pathological image analysis for colorectal cancer (CRC) diagnosis is time-consuming and knowledge-intensive, but is essential for CRC patients’ treatment. The current heavy workload of pathologists in clinics/hospitals may easily lead to unconscious misdiagnosis of CRC based on their daily image analyses. Methods: Based on a state-of-the-art transfer-learned deep convolutional neural network in artificial intelligence (AI), we proposed a novel patch aggregation strategy for clinic CRC prediction/diagnosis using weakly labeled pathological whole slide image (WSI) patches. This approach was trained and validated using an unprecedented and enormously large number of 170,099 patches, >14,680 WSIs, from >9,631 subjects that covered diverse and representative clinical cases from multi-independent-sources across China, U.S., and Germany. Results: Our innovative AI tool was consistently nearly perfectly agreed with (average Kappa-statistic 0.896) and even often better than most of the experienced expert pathologists when tested in diagnosing CRC WSIs from multi-centers. The average area under the receiver operating characteristics curve (AUC) of AI was greater than that of the pathologists (0.981 vs 0.970) and achieved the best performance among the application of other AI methods to CRC diagnosis. Our AI-generated heatmap highlights the image regions of cancer tissue/cells. Conclusions: This first-ever generalizable AI system can handle large amounts of WSIs consistently and robustly without potential bias due to fatigue commonly experienced by clinical pathologists. Hence, it will drastically alleviate the heavy clinical burden of daily pathology diagnosis, and improve the treatment for CRC patients. This tool is generalizable to other cancer diagnosis based on image recognition.