Establishment and Applicability of a Diagnostic System for Advanced Gastric Cancer T Staging Based on a Faster Region-Based Convolutional Neural Network
Copyright © 2020 Zheng, Zhang, Hu, Gao, Zhang, Zhang, Li, Zhou, Niu, Lu and Wang..
Background: The accurate prediction of the tumor infiltration depth in the gastric wall based on enhanced CT images of gastric cancer is crucial for screening gastric cancer diseases and formulating treatment plans. Convolutional neural networks perform well in image segmentation. In this study, a convolutional neural network was used to construct a framework for automatic tumor recognition based on enhanced CT images of gastric cancer for the identification of lesion areas and the analysis and prediction of T staging of gastric cancer. Methods: Enhanced CT venous phase images of 225 patients with advanced gastric cancer from January 2017 to June 2018 were retrospectively collected. Ftable LabelImg software was used to identify the cancerous areas consistent with the postoperative pathological T stage. The training set images were enhanced to train the Faster RCNN detection model. Finally, the accuracy, specificity, recall rate, F1 index, ROC curve, and AUC were used to quantify the classification performance of T staging on this system. Results: The AUC of the Faster RCNN operating system was 0.93, and the recognition accuracies for T2, T3, and T4 were 90, 93, and 95%, respectively. The time required to automatically recognize a single image was 0.2 s, while the interpretation time of an imaging expert was ~10 s. Conclusion: In enhanced CT images of gastric cancer before treatment, the application of Faster RCNN to diagnosis the T stage of gastric cancer has high accuracy and feasibility.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:10 |
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| Enthalten in: |
Frontiers in oncology - 10(2020) vom: 18., Seite 1238 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Zheng, Longbo [VerfasserIn] |
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| Links: |
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| Themen: |
Advanced gastric cancer |
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| Anmerkungen: |
Date Revised 28.09.2020 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.3389/fonc.2020.01238 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM314236627 |
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| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a Copyright © 2020 Zheng, Zhang, Hu, Gao, Zhang, Zhang, Li, Zhou, Niu, Lu and Wang. | ||
| 520 | |a Background: The accurate prediction of the tumor infiltration depth in the gastric wall based on enhanced CT images of gastric cancer is crucial for screening gastric cancer diseases and formulating treatment plans. Convolutional neural networks perform well in image segmentation. In this study, a convolutional neural network was used to construct a framework for automatic tumor recognition based on enhanced CT images of gastric cancer for the identification of lesion areas and the analysis and prediction of T staging of gastric cancer. Methods: Enhanced CT venous phase images of 225 patients with advanced gastric cancer from January 2017 to June 2018 were retrospectively collected. Ftable LabelImg software was used to identify the cancerous areas consistent with the postoperative pathological T stage. The training set images were enhanced to train the Faster RCNN detection model. Finally, the accuracy, specificity, recall rate, F1 index, ROC curve, and AUC were used to quantify the classification performance of T staging on this system. Results: The AUC of the Faster RCNN operating system was 0.93, and the recognition accuracies for T2, T3, and T4 were 90, 93, and 95%, respectively. The time required to automatically recognize a single image was 0.2 s, while the interpretation time of an imaging expert was ~10 s. Conclusion: In enhanced CT images of gastric cancer before treatment, the application of Faster RCNN to diagnosis the T stage of gastric cancer has high accuracy and feasibility | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a T staging | |
| 650 | 4 | |a advanced gastric cancer | |
| 650 | 4 | |a artificial intelligence | |
| 650 | 4 | |a convolutional neural network | |
| 650 | 4 | |a faster RCNN | |
| 700 | 1 | |a Zhang, Xunying |e verfasserin |4 aut | |
| 700 | 1 | |a Hu, Jilin |e verfasserin |4 aut | |
| 700 | 1 | |a Gao, Yuan |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Xianxiang |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Maoshen |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Shuai |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Xiaoming |e verfasserin |4 aut | |
| 700 | 1 | |a Niu, Tianye |e verfasserin |4 aut | |
| 700 | 1 | |a Lu, Yun |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Dongsheng |e verfasserin |4 aut | |
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