Gockel I, et al. J Gastric Cancer. 2018 Aug;18:e37. https://doi.org/10.5230/jgc.2018.18.e37

Esophageal cancer (EC) is an aggressive form of cancer, including squamous cell carcinoma (ESCC) and adenocarcinoma (EAC) as two predominant histological subtypes. Accumulating evidence supports the existence of cancer stem cells (CSCs) able to initiate and maintain EAC or ESCC. In this review, we aim to collect the current evidence on CSCs in esophageal cancer, including the biomarkers/characterization strategies of CSCs, heterogeneity of CSCs, and the key signaling pathways (Wnt/β-catenin, Notch, Hedgehog, YAP, JAK/STAT3) in modulating CSCs during esophageal cancer progression. Exploring the molecular mechanisms of therapy resistance in EC highlights DNA damage response (DDR), metabolic reprogramming, epithelial mesenchymal transition (EMT), and the role of the crosstalk of CSCs and their niche in the tumor progression. According to these molecular findings, potential therapeutic implications of targeting esophageal CSCs may provide novel strategies for the clinical management of esophageal cancer.

Esophageal cancer is the sixth leading cause of cancer-related death worldwide. Histopathological confirmation is a key step in tumor diagnosis. Therefore, simplification in decision-making by discrimination between malignant and non-malignant cells of histological specimens can be provided by combination of new imaging technology and artificial intelligence (AI). In this work, hyperspectral imaging (HSI) data from 95 patients were used to classify three different histopathological features (squamous epithelium cells, esophageal adenocarcinoma (EAC) cells, and tumor stroma cells), based on a multi-layer perceptron with two hidden layers. We achieved an accuracy of 78% for EAC and stroma cells, and 80% for squamous epithelium. HSI combined with machine learning algorithms is a promising and innovative technique, which allows image acquisition beyond Red–Green–Blue (RGB) images. Further method validation and standardization will be necessary, before automated tumor cell identification algorithms can be used in daily clinical practice.

Purpose: This study explored the association between triglyceride-glucose (TyG), TyG index with body mass index (TyG-BMI), triglyceride/high-density lipoprotein cholesterol ratio (TG/HDL-C), metabolic score for insulin resistance (IR) (METS-IR) and the risk of esophageal cancer. Methods: A total of 388,900 participants from the United Kingdom Biobank from 2006 to 2010 were included. Fine-Gray models, restricted cubic spline (RCS), and receiver operating characteristic (ROC) curves were used to assess the association between the four IR surrogates and the risk of esophageal cancer, specifically, esophageal adenocarcinoma (EAC) and esophageal squamous cell carcinoma (ESCC). Results: Ten years after recruitment, 0.16% (95%CI 0.11–0.26%) had esophageal cancer and 4.17% (95%CI 3.86–4.46%) are deceased. For each standard deviation increase in the TyG index, TyG-BMI, TG/HDL-C, and METS-IR, the risk of EAC increased by Hazard ratios (HR)1.16, 1.37, 1.08, and 1.36, respectively (all P < 0.05), while the risk of ESCC decreased by HRs 0.80, 0.67, 0.77, and 0.65, respectively. RCS analysis indicated that most relationships were nonlinear (P < 0.05). ROC curves showed that METS-IR had a more robust diagnostic efficacy than TyG, TyG-BMI, and TG/HDL-C. Conclusion: TyG index, TyG-BMI, TG/HDL-C, and METS-IR were closely associated with the risk of EAC and ESCC. Additionally, METS-IR surpassed the other three IR indices in predicting and diagnosing the risks of EAC and ESCC. The METS-IR is expected to become a more effective metric for identifying populations at early risk of esophageal cancer and for improving risk stratification.

Background: Patients with intestinal cancer (colorectal, appendiceal, and small bowel) with peritoneal metastases (PM) have a poor prognosis. We assessed whether pressurized intraperitoneal aerosol chemotherapy (PIPAC) together with systemic chemotherapy is an effective treatment option for these entities in palliative intent. Methods: Between November 2015 and February 2018, prospective data registry was performed (NCT03100708). Thirteen patients with intestinal cancer (median age 61 years [range 49–77]) underwent 26 PIPAC procedures with a median number of 2 interventions per patient (range 1–6). A chemoaerosol consisting of cisplatin/doxorubicin was administered during standard laparoscopy. Results: The median peritoneal carcinomatosis index according to Sugarbaker before the first PIPAC was 14 (range 2–27), and the median ascites volume was 10 mL (range 0–6300 mL). Six patients who received 2 or more PIPAC procedures had decreased and stable ascites volumes, while only 1 patient displayed increased ascites. The median overall survival was 303 days (range 30–490) after the first PIPAC procedure. Conclusions: PIPAC offers a novel treatment option for patients with PM. Our data show that PIPAC is safe and well-tolerated. Ascites production can be controlled by PIPAC in patients with intestinal cancer. Further studies are required to document the significance of PIPAC within palliative therapy concepts. Trial registration: NCT03100708

Innovations and new advancements in intraoperative real-time imaging have gained significant importance in the field of gastric cancer surgery in the recent past. Currently, the most promising procedures include indocyanine green fluorescence imaging (ICG-FI) and hyperspectral imaging or multispectral imaging (HSI, MSI). ICG-FI is utilized in a broad range of clinical applications, e.g., assessment of perfusion or lymphatic drainage, and additional implementations are currently investigated. HSI is still in the experimental phase and its value and clinical relevance require further evaluation, but initial studies have shown a successful application in perfusion assessment, and prospects concerning non-invasive tissue and tumor classification are promising. The application of machine learning and artificial intelligence technologies might enable an automatic evaluation of the acquired image data in the future. Both methods facilitate the accurate visualization of tissue characteristics that are initially indistinguishable for the human eye. By aiding surgeons in optimizing the surgical procedure, image-guided surgery can contribute to the oncologic safety and reduction of complications in gastric cancer surgery and recent advances hold promise for the application of HSI in intraoperative tissue diagnostics.