  Ovarian cancer is one of the most serious cancers that threaten women around
the world. Epithelial ovarian cancer (EOC), as the most commonly seen subtype
of ovarian cancer, has rather high mortality rate and poor prognosis among
various gynecological cancers. Survival analysis outcome is able to provide
treatment advices to doctors. In recent years, with the development of medical
imaging technology, survival prediction approaches based on pathological images
have been proposed. In this study, we designed a deep framework named EOCSA
which analyzes the prognosis of EOC patients based on pathological whole slide
images (WSIs). Specifically, we first randomly extracted patches from WSIs and
grouped them into multiple clusters. Next, we developed a survival prediction
model, named DeepConvAttentionSurv (DCAS), which was able to extract
patch-level features, removed less discriminative clusters and predicted the
EOC survival precisely. Particularly, channel attention, spatial attention, and
neuron attention mechanisms were used to improve the performance of feature
extraction. Then patient-level features were generated from our weight
calculation method and the survival time was finally estimated using LASSO-Cox
model. The proposed EOCSA is efficient and effective in predicting prognosis of
EOC and the DCAS ensures more informative and discriminative features can be
extracted. As far as we know, our work is the first to analyze the survival of
EOC based on WSIs and deep neural network technologies. The experimental
results demonstrate that our proposed framework has achieved state-of-the-art
performance of 0.980 C-index. The implementation of the approach can be found
at https://github.com/RanSuLab/EOCprognosis.
