  The distinction between malignant and benign tumors is essential to the
treatment of cancer. The tissue's elasticity can be used as an indicator for
the required tissue characterization. Optical coherence elastography (OCE)
probes have been proposed for needle insertions but have so far lacked the
necessary load sensing capabilities. We present a novel OCE needle probe that
provides simultaneous optical coherence tomography (OCT) imaging and load
sensing at the needle tip. We demonstrate the application of the needle probe
in indentation experiments on gelatin phantoms with varying gelatin
concentrations. We further implement two deep learning methods for the
end-to-end sample characterization from the acquired OCT data. We report the
estimation of gelatin sample concentrations in unseen samples with a mean error
of $1.21 \pm 0.91$ wt\%. Both evaluated deep learning models successfully
provide sample characterization with different advantages regarding the
accuracy and inference time.
