2018 PRESENTERS


Armin Meier
Research Scientist
Definiens AG

 


Katharina Nekolla

Research Scientist
Definiens AG

 

Presenting
End-to-end Learning Using Convolutional Neural Networks to Predict Survival in Patients with Gastric Cancer

Abstract
Background We applied a survival convolutional neural network (CNN) approach on immunohistochemically (IHC) stained tissue microarrays (TMAs) from gastric cancer (GC) patients to directly learn survival-related risk values for patient stratification. Methods Image patches (80µm x 80µm) were extracted from 469 TMA cores from 248 patients, scanned after IHC for CD8 and KI67. For each stain, survival CNNs were trained to maximize a log partial likelihood derived from the Cox proportional hazards model and to predict patch-based risks for cancer-specific death in a 10-fold pre-validation procedure. Patient risks were assessed by averaging the risks from each patients patches. Results Stratifying patients into low- and high-risk groups by taking the cohort median as threshold led to a significant log-rank test p-value (<0.01). whereas="" kaplan-meier="" curves="" for="" tnm="" staging="" 2a="" 2b="" and="" 3a="" had="" no="" significant="" prognostic="" value="" the="" risk="" score="" significantly="" stratified="" same="" subcohort="" p="" 0="" 05="" median="" as="" threshold="" visual="" assessment="" of="" heatmaps="" revealed="" an="" association="" low-risk="" regions="" with="" clusters="" cd8="" cells="" presence="" in="" stroma="" tumor="" epithelium="" a="" low="" density="" are="" associated="" higher="" risks="" conclusions="" we="" applied="" survival="" cnns="" to="" digital="" ihc-stained="" gc="" tissue="" sections="" directly="" associate="" image="" cancer-specific="" death="" this="" information="" may="" be="" used="" deepen="" our="" knowledge="" on="" how="" morphology="" relates="" findings="" will="" extended="" other="" biomarkers="" validated="" using="" data="" from="" another="" clinical="" site="">

Objectives

  1. Understand end-to-end learning 
  2. Understand survival loss

Biographies

Armin Meier is a computer scientist by training with a background in bioinformatics. In his masters at the University of Munich, he worked on quantile regression methods for medical data. He received his doctorate from the same university on probabilistic models for computational protein structure prediction. Subsequently, he joined Definiens AG as a research scientist. At Definiens AG, the tissue phenomics methodology is applied to discover novel prognostic and predictive biomarkers mainly in the field of cancer immunotherapy. Armin's focus now lies on machine learning and data analysis methods for efficient and quantitative studies, specifically in medical image data.

Katharina Nekolla is a biophysicist by training who already focused on biomedical image and data analysis during her bachelor and master theses. She received her doctorate from the University of Munich on the distribution of nanoparticles in the tissue and their interaction with immune cells. Afterwards, she joined Definiens AG as a research scientist. At Definiens AG, the tissue phenomics methodology is applied to discover novel prognostic and predictive biomarkers, mainly in the field of immuno-oncology. Katharina actively participates in several collaborative research projects, in which she uses image and data mining techniques to better understand disease biology and to exploit the full predictive potential of tissue sections.