Patients with lymphovascular invasion and extracapsular invasion are at a high risk of distant recurrence after preoperative chemoradiotherapy and oesophagectomy. Effective systemic therapy and intensive surveillance are necessary in this group of patients.
Neoadjuvant chemoradiation non-responders demonstrated no benefit and an even worse outcome compared with those receiving primary resection for locally advanced oesophageal squamous cell carcinoma. However, no significant clinical parameters could be implemented in the clinics to predict the response to neoadjuvant chemoradiation before treatment.
In esophageal cancer, few prediction tools can be confidently used in current clinical practice. We developed a deep convolutional neural network (CNN) with 798 positron emission tomography (PET) scans of esophageal squamous cell carcinoma and 309 PET scans of stage I lung cancer. In the first stage, we pretrained a 3D-CNN with all PET scans for a task to classify the scans into esophageal cancer or lung cancer. Overall, 548 of 798 PET scans of esophageal cancer patients were included in the second stage with an aim to classify patients who expired within or survived more than one year after diagnosis. The area under the receiver operating characteristic curve (AUC) was used to evaluate model performance. In the pretrain model, the deep CNN attained an AUC of 0.738 in identifying patients who expired within one year after diagnosis. In the survival analysis, patients who were predicted to be expired but were alive at one year after diagnosis had a 5-year survival rate of 32.6%, which was significantly worse than the 5-year survival rate of the patients who were predicted to survive and were alive at one year after diagnosis (50.5%, p < 0.001). These results suggest that the prediction model could identify tumors with more aggressive behavior. In the multivariable analysis, the prediction result remained an independent prognostic factor (hazard ratio: 2.830; 95% confidence interval: 2.252–3.555, p < 0.001). We conclude that a 3D-CNN can be trained with PET image datasets to predict esophageal cancer outcome with acceptable accuracy.
Background: The presence of lymphovascular invasion (LVI) and perineural invasion (PNI) are of great prognostic importance in esophageal squamous cell carcinoma. Currently, positron emission tomography (PET) scans are the only means of functional assessment prior to treatment. We aimed to predict the presence of LVI and PNI in esophageal squamous cell carcinoma using PET imaging data by training a three-dimensional convolution neural network (3D-CNN).Methods: Seven hundred and ninety-eight PET scans of patients with esophageal squamous cell carcinoma and 309 PET scans of patients with stage I lung cancer were collected. In the first part of this study, we built a 3D-CNN based on a residual network, ResNet, for a task to classify the scans into esophageal cancer or lung cancer. In the second stage, we collected the PET scans of 278 patients undergoing esophagectomy for a task to classify and predict the presence of LVI/PNI.Results: In the first part, the model performance attained an area under the receiver operating characteristic curve (AUC) of 0.860. In the second part, we randomly split 80%, 10%, and 10% of our dataset into training set, validation set and testing set, respectively, for a task to classify the scans into the presence of LVI/PNI and evaluated the model performance on the testing set. Our 3D-CNN model attained an AUC of 0.668 in the testing set, which shows a better discriminative ability than random guessing.Conclusions: A 3D-CNN can be trained, using PET imaging datasets, to predict LNV/PNI in esophageal cancer with acceptable accuracy.
OBJECTIVES
Recurrent laryngeal nerve lymph node dissection (LND) has been incorporated into oesophagectomy for patients with oesophageal squamous cell carcinoma, but with uncertain oncological efficacy.
METHODS
The data of patients with oesophageal squamous cell carcinoma, including who underwent upfront surgery (surgery group) and those who received neoadjuvant therapy followed by surgery (neoadjuvant chemoradiotherapy group), were retrospectively examined. The overall survival (OS) and disease-free survival (DFS) were compared between patients with and without recurrent laryngeal nerve LND.
RESULTS
Among the 312 patients, no significant differences were found in 3-year OS and DFS between patients with and without recurrent laryngeal nerve LND in the entire cohort (OS: 57% vs 52%, P = 0.33; DFS: 47% vs 41%, P = 0.186), or the surgery group (n = 173, OS: 69% vs 58%, P = 0.43; DFS: 52% vs. 48%, P = 0.30) and the neoadjuvant chemoradiotherapy group (n = 139, OS: 44% vs 43%, P = 0.44; DFS: 39% vs 32%, P = 0.27). However, among patients with clinical positive recurrent laryngeal nerve lymph node involvement before treatment, there was significant OS and DFS differences between patients with and without recurrent laryngeal nerve LND (OS: 62% vs 33%, P = 0.029; DFS: 49% vs 26%, P = 0.031).
CONCLUSIONS
Recurrent laryngeal nerve LND is not a significant prognostic factor in patients with oesophageal squamous cell carcinoma; however, it is associated with better outcomes in patients with pre-treatment radiological evidence of recurrent laryngeal nerve lymph node involvement.
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