Surgical strategies for gastric cancer with synchronous peritoneal carcinomatosis (Br J Surg 2006; 93: 1530–1535)

Retrospective imaging studies of gastric cancer: Study protocol clinical trial (SPIRIT Compliant)

INTRODUCTION

Peritoneal metastasis (PM) is a frequent condition in patients presenting with gastric cancer, especially in younger patients with advanced tumor stages. Computer tomography (CT) is the most common noninvasive modality for preoperative staging in gastric cancer. However, the challenges of limited CT soft tissue contrast result in poor CT depiction of small peritoneal tumors. The sensitivity for detecting PM remains low. About 16% of PM are undetected. Deep learning belongs to the category of artificial intelligence and has demonstrated amazing results in medical image analyses. So far, there has been no deep learning study based on CT images for the diagnosis of PM in gastric cancer.

WE PROPOSED A HYPOTHESIS

CT images in the primary tumor region of gastric cancer had valuable information that could predict occult PM of gastric cancer, which could be extracted effectively through deep learning.

OBJECTIVE

To develop a deep learning model for accurate preoperative diagnosis of PM in gastric cancer.

METHOD

All patients with gastric cancer were retrospectively enrolled. All patients were initially diagnosed as PM negative by CT and later confirmed as positive through surgery or laparoscopy. The dataset was randomly split into training cohort (70% of all patients) and testing cohort (30% of all patients). To develop deep convolutional neural network (DCNN) models with high generalizability, 5-fold cross-validation and model ensemble were utilized. The area under the receiver operating characteristic curve, sensitivity and specificity were used to evaluate DCNN models on the testing cohort.

DISCUSSION

This study will help us know whether deep learning can improve the performance of CT in diagnosing PM in gastric cancer.

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

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.

Chemoresponse after non-curative gastrectomy for M1 gastric cancer

Background

M1 gastric cancer has a poor oncologic outcome with a median survival of less than 1 year despite aggressive chemotherapy. Recent trials include chemotherapy combined non-curative gastrectomy. This study evaluated the chemoresponse after non-curative gastrectomy in M1 gastric cancer and the survival benefit.

Methods

Between January 2000 and December 2010, 660 patients received chemotherapy for gastric cancer at the Department of Hemato-Oncology, Dongsan Medical Center, Keimyung University School of Medicine, Daegu, Korea. Data was collected retrospectively from the medical records. Patients who received preoperative or adjuvant chemotherapy, who underwent other surgeries like gastrojejunal bypass or exploratory laparotomy, who died within 3 months due to seriously advanced gastric cancer, who were lost to follow-up, or whose medical records were unsuitable for data collection were excluded. The remaining 101 patients had received chemotherapy only (CTx group, n = 76) or chemotherapy after non-curative gastrectomy (NCG + CTx group, n = 25). Clinicopathologic characteristics, chemoresponse, and overall survival were compared between the two groups.

Results

There were no significant differences between the two groups in clinicopathologic characteristics including age, sex, body mass index (BMI), comorbidity, histologic differentiation, tumor location, clinical T stage, and initial site of distant metastasis. Chemoresponse was checked on two separate occasions from the initiation of chemotherapy: first chemotherapy regimen and until the third regimen change. The NCG + CTx group showed more favorable chemoresponse than the CTx group in both checks (60% and 72% vs. 18.4% and 23.7%). The NCG + CTx group showed longer overall survival than the CTx group (26 vs. 11 months).

Conclusions

Non-curative gastrectomy in M1 gastric cancer could improve chemoresponse and extend overall survival.