Deep Learning Histology for Prediction of Lymph Node Metastases and Tumor Regression after Neoadjuvant FLOT Therapy of Gastroesophageal Adenocarcinoma.

Background: The aim of this study was to establish a deep learning prediction model for neoadjuvant FLOT chemotherapy response. The neural network utilized clinical data and visual information from whole-slide images (WSIs) of therapy-naïve gastroesophageal cancer biopsies.

Methods: This study included 78 patients from the University Hospital of Cologne and 59 patients from the University Hospital of Heidelberg used as external validation.

First experiences with machine learning predictions of accelerated declining eGFR slope of living kidney donors 3 years after donation.

Background: Living kidney donors are screened pre-donation to estimate the risk of end-stage kidney disease (ESKD). We evaluate Machine Learning (ML) to predict the progression of kidney function deterioration over time using the estimated GFR (eGFR) slope as the target variable.

Methods: We included 238 living kidney donors who underwent donor nephrectomy.

2-Step Scores with optional nephropathology for the prediction of adverse outcomes for brain-dead donor kidneys in Eurotransplant.

Background: The decision to accept or discard the increasingly rare and marginal brain-dead donor kidneys in Eurotransplant (ET) countries has to be made without solid evidence. Thus, we developed and validated flexible clinicopathological scores called 2-Step Scores for the prognosis of delayed graft function (DGF) and 1-year death-censored transplant loss (1y-tl) reflecting the current practice of six ET countries including Croatia and Belgium.

Methods: The training set was n = 620 for DGF and n = 711 for 1y-tl, with validation sets n = 158 and n = 162, respectively.

[Tailored surgery in the treatment of gastroesophageal cancer].

The surgical options and particularly perioperative treatment, have significantly advanced in the case of gastroesophageal cancer. This progress enables a 5-year survival rate of nearly 50% to be achieved through curative multimodal treatment concepts for locally advanced cancer. Therefore, in tumor boards and surgical case discussions the question increasingly arises regarding the type of treatment that provides optimal oncological and functional outcomes for individual patients with pre-existing diseases.