ArticleAnnals of translational medicine2021
Predicting aggressive histopathological features in esophageal cancer with positron emission tomography using a deep convolutional neural network.
Article in Annals of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.
- Perineural Invasion Is a Significant Indicator of High Malignant Degree and Poor Prognosis in Esophageal Cancer: A Systematic Review and Meta-Analysis.Frontiers in oncology · 2022Pooled it
- Atom Search Optimization with the Deep Transfer Learning-Driven Esophageal Cancer Classification Model.Computational intelligence and neuroscience · 2022Article
- Applications of Artificial Intelligence for the Diagnosis of Gastrointestinal Diseases.Diagnostics (Basel, Switzerland) · 2021Review
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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).
methodsSeven 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.
resultsIn 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.
conclusionsA 3D-CNN can be trained, using PET imaging datasets, to predict LNV/PNI in esophageal cancer with acceptable accuracy.
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