PS02.070: Deep Neural Network to Predict Poor Prognostic Factors in Patients With Esophageal Cancer

Abstract

Background Both lymphovascular invasion, which is characterized by penetration of tumor cells into the peritumoural vascular or lymphatic network, and perineural invasion, which is characterized by involvement of tumor cells surrounding nerve fibers, are considered as an important step for tumor spreading, and are known poor prognostic factors in esophageal cancer. However, the information of these histological features is unavailable until pathological examination of surgical resected specimens. We aim to predict the presence or absence of these factors by positron emission tomography images during staging workup. Methods The positron emission tomography images before treatment and pathological reports of 278 patients who underwent esophagectomy for squamous cell carcinoma were collected. Stepwise convolutional neural network was constructed to distinguish patient with either lymphovascular invasion or perineural invasion from those without. Results Randomly selected 248 patients were included in the testing set. Stepwise approach was used in training our custom neural network. The performance of fine-tuned neural network was tested in another independent 30 patients. The accuracy rate of predicting the presence or absence of either lymphovascular invasion or perineural invasion was 66.7% (20 of 30 were accurate). Conclusion Using pre-treatment positron emission tomography images alone to predict the presence of absence of poor prognostic histological factors, i.e. lymphovascular invasion or perineural invasion, with deep convolutional neural network is possible. The technique of deep learning may identify patients with poor prognosis and enable personalized medicine in esophageal cancer. Disclosure All authors have declared no conflicts of interest.

Texture synthesis of ecological plant protection image based on convolution neural network

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