Deep Convolutional Neural Network Based on Computed Tomography Images for the Preoperative Diagnosis of Occult Peritoneal Metastasis in Advanced Gastric Cancer.

Zixing Huang,Bing Wu,Baiyun Liu,Du He,Xinzu Chen,Dan Liu,Pengxin Yu,Jiankun Hu,Bin Song

doi:10.3389/fonc.2020.601869

Abstract

We aimed to develop a deep convolutional neural network (DCNN) model based on computed tomography (CT) images for the preoperative diagnosis of occult peritoneal metastasis (OPM) in advanced gastric cancer (AGC). A total of 544 patients with AGC were retrospectively enrolled. Seventy-nine patients were confirmed with OPM during surgery or laparoscopy. CT images collected during the initial visit were randomly split into a training cohort and a testing cohort for DCNN model development and performance evaluation, respectively. A conventional clinical model using multivariable logistic regression was also developed to estimate the pretest probability of OPM in patients with gastric cancer. The DCNN model showed an AUC of 0.900 (95% CI: 0.851–0.953), outperforming the conventional clinical model (AUC = 0.670, 95% CI: 0.615–0.739; p < 0.001). The proposed DCNN model demonstrated the diagnostic detection of occult PM, with a sensitivity of 81.0% and specificity of 87.5% using the cutoff value according to the Youden index. Our study shows that the proposed deep learning algorithm, developed with CT images, may be used as an effective tool to preoperatively diagnose OPM in AGC.

Highlights

According to the GLOBOCAN 2018 data, gastric cancer (GC) remains the fifth most common cancer and the third most deadly cancer worldwide [1]
We found that if the threshold probability for the clinical decision was less than 80%, the patient would benefit more from the findings of the deep convolutional neural network (DCNN) model than either the all-laparoscopy or no-laparoscopy schemes
We developed a DCNN model to identify occult peritoneal metastasis (OPM) in advanced gastric cancer (AGC) patients prior to surgical treatment

Summary

Introduction

According to the GLOBOCAN 2018 data, gastric cancer (GC) remains the fifth most common cancer and the third most deadly cancer worldwide [1]. Abdominal enhanced CT is considered the most common noninvasive modality of preoperative diagnosis in GC patients [5, 7, 9,10,11]. Occult peritoneal metastasis (OPM) often refers to PM negativity on initial CT diagnosis that is revised to PM positivity following subsequent laparoscopy or surgery [12, 13]. Due to the nature of OPM, it is often missed by radiologists when interpreting CT images alone, resulting in low detection sensitivity and diagnostic accuracy in AGC patients. MRI and PET/CT are considered second choices because they are less sensitive than abdominal enhanced CT in detecting peritoneal metastases [17,18,19]. The development of a noninvasive method to facilitate the targeted diagnosis of OPM beyond conventional imaging is urgently needed

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Conclusion