An attention-based deep learning network for predicting Platinum
  resistance in ovarian cancer
Haoming Zhuang Beibei Li Jingtong Ma Patrice Monkam Shouliang Qi Wei Qian Dianning He
Background: Ovarian cancer is among the three most frequent gynecologic
cancers globally. High-grade serous ovarian cancer (HGSOC) is the most common
and aggressive histological type. Guided treatment for HGSOC typically involves
platinum-based combination chemotherapy, necessitating an assessment of whether
the patient is platinum-resistant. The purpose of this study is to propose a
deep learning-based method to determine whether a patient is platinum-resistant
using multimodal positron emission tomography/computed tomography (PET/CT)
images. Methods: 289 patients with HGSOC were included in this study. An
end-to-end SE-SPP-DenseNet model was built by adding Squeeze-Excitation Block
(SE Block) and Spatial Pyramid Pooling Layer (SPPLayer) to Dense Convolutional
Network (DenseNet). Multimodal data from PET/CT images of the regions of
interest (ROI) were used to predict platinum resistance in patients. Results:
Through five-fold cross-validation, SE-SPP-DenseNet achieved a high accuracy
rate and an area under the curve (AUC) in predicting platinum resistance in
patients, which were 92.6% and 0.93, respectively. The importance of
incorporating SE Block and SPPLayer into the deep learning model, and
considering multimodal data was substantiated by carrying out ablation studies
and experiments with single modality data. Conclusions: The obtained
classification results indicate that our proposed deep learning framework
performs better in predicting platinum resistance in patients, which can help
gynecologists make better treatment decisions. Keywords: PET/CT, CNN, SE Block,
SPP Layer, Platinum resistance, Ovarian cancer