  Lung squamous cell carcinoma (LSCC) has a high recurrence and metastasis
rate. Factors influencing recurrence and metastasis are currently unknown and
there are no distinct histopathological or morphological features indicating
the risks of recurrence and metastasis in LSCC. Our study focuses on the
recurrence prediction of LSCC based on H&E-stained histopathological
whole-slide images (WSI). Due to the small size of LSCC cohorts in terms of
patients with available recurrence information, standard end-to-end learning
with various convolutional neural networks for this task tends to overfit.
Also, the predictions made by these models are hard to interpret.
Histopathology WSIs are typically very large and are therefore processed as a
set of smaller tiles. In this work, we propose a novel conditional
self-supervised learning (SSL) method to learn representations of WSI at the
tile level first, and leverage clustering algorithms to identify the tiles with
similar histopathological representations. The resulting representations and
clusters from self-supervision are used as features of a survival model for
recurrence prediction at the patient level. Using two publicly available
datasets from TCGA and CPTAC, we show that our LSCC recurrence prediction
survival model outperforms both LSCC pathological stage-based approach and
machine learning baselines such as multiple instance learning. The proposed
method also enables us to explain the recurrence histopathological risk factors
via the derived clusters. This can help pathologists derive new hypotheses
regarding morphological features associated with LSCC recurrence.
