Effect of Pre-Transplant Recipient Underweight on the Postoperative Outcome and Graft Survival in Primary Kidney Transplantation.

BACKGROUND: The objective of this study was to evaluate the influence of recipient underweight on the short- and long-term outcomes of patients undergoing primary kidney transplantation (KT). PATIENTS AND METHODS: Three hundred thirty-three patients receiving primary KT in our department between 1993 and 2017 were included in the study. Patients were divided according to their body mass index (BMI) into underweight (BMI <18.5 kg/m2; N = 29) and normal weight (BMI 18.5-24.9 kg/m2; N = 304) groups. Clinicopathological characteristics, postoperative outcomes, and graft and patient survival were analyzed retrospectively. RESULTS: The postoperative rate of surgical complications and renal function were comparable between the groups. One year and 3 years after KT, 70% and 92.9%, respectively, of the pre-transplant underweight patients reached a normal BMI (≥18.5 kg/m2). The mean death-censored graft survival was significantly lower in pre-transplant underweight patients than in pre-transplant normal-weight patients (11.5 ± 1.6 years vs 16.3 ± 0.6 years, respectively; P = .045). Especially KT recipients with a moderate or severe pre-transplant underweight (BMI <17 kg/m2; N = 8) showed an increased rate of graft loss (5- and 10-year graft survival: 21.4% each). No statistical difference could be observed between the 2 groups regarding causes of graft loss. In multivariate analysis, recipient underweight (P = .024) remained an independent prognostic factor for graft survival. CONCLUSION: Being underweight did not affect the early postoperative outcome after primary KT. However, underweight, and especially moderate and severe thinness, is associated with reduced long-term kidney graft survival, and therefore this group of patients should be monitored with special attention.

Tumor cell identification and classification in esophageal adenocarcinoma specimens by hyperspectral imaging.

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.