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 w...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-11-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2020.601869/full |
| _version_ | 1818458157259685888 |
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| author | Zixing Huang Dan Liu Xinzu Chen Du He Pengxin Yu Baiyun Liu Bing Wu Jiankun Hu Bin Song |
| author_facet | Zixing Huang Dan Liu Xinzu Chen Du He Pengxin Yu Baiyun Liu Bing Wu Jiankun Hu Bin Song |
| author_sort | Zixing Huang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T22:54:00Z |
| format | Article |
| id | doaj.art-e602d0a286644f80a3306d334b9baabf |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-12-14T22:54:00Z |
| publishDate | 2020-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-e602d0a286644f80a3306d334b9baabf2022-12-21T22:44:37ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2020-11-011010.3389/fonc.2020.601869601869Deep Convolutional Neural Network Based on Computed Tomography Images for the Preoperative Diagnosis of Occult Peritoneal Metastasis in Advanced Gastric CancerZixing Huang0Dan Liu1Xinzu Chen2Du He3Pengxin Yu4Baiyun Liu5Bing Wu6Jiankun Hu7Bin Song8Department of Radiology, West China Hospital, Sichuan University, Chengdu, ChinaDepartment of Radiology, West China Hospital, Sichuan University, Chengdu, ChinaState Key Laboratory of Biotherapy, Department of Gastrointestinal Surgery and Laboratory of Gastric Cancer, Collaborative Innovation Center for Biotherapy, West China Hospital, Sichuan University, Chengdu, ChinaDepartment of Pathology, West China Hospital, Sichuan University, Chengdu, ChinaInstitute of Advanced Research, Infervision, Beijing, ChinaInstitute of Advanced Research, Infervision, Beijing, ChinaDepartment of Radiology, West China Hospital, Sichuan University, Chengdu, ChinaState Key Laboratory of Biotherapy, Department of Gastrointestinal Surgery and Laboratory of Gastric Cancer, Collaborative Innovation Center for Biotherapy, West China Hospital, Sichuan University, Chengdu, ChinaDepartment of Radiology, West China Hospital, Sichuan University, Chengdu, ChinaWe 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.https://www.frontiersin.org/articles/10.3389/fonc.2020.601869/fullstomach neoplasmsperitoneal neoplasmsdeep learningtomographyx-ray computedneural networks |
| spellingShingle | Zixing Huang Dan Liu Xinzu Chen Du He Pengxin Yu Baiyun Liu Bing Wu Jiankun Hu Bin Song Deep Convolutional Neural Network Based on Computed Tomography Images for the Preoperative Diagnosis of Occult Peritoneal Metastasis in Advanced Gastric Cancer Frontiers in Oncology stomach neoplasms peritoneal neoplasms deep learning tomography x-ray computed neural networks |
| title | Deep Convolutional Neural Network Based on Computed Tomography Images for the Preoperative Diagnosis of Occult Peritoneal Metastasis in Advanced Gastric Cancer |
| title_full | Deep Convolutional Neural Network Based on Computed Tomography Images for the Preoperative Diagnosis of Occult Peritoneal Metastasis in Advanced Gastric Cancer |
| title_fullStr | Deep Convolutional Neural Network Based on Computed Tomography Images for the Preoperative Diagnosis of Occult Peritoneal Metastasis in Advanced Gastric Cancer |
| title_full_unstemmed | Deep Convolutional Neural Network Based on Computed Tomography Images for the Preoperative Diagnosis of Occult Peritoneal Metastasis in Advanced Gastric Cancer |
| title_short | Deep Convolutional Neural Network Based on Computed Tomography Images for the Preoperative Diagnosis of Occult Peritoneal Metastasis in Advanced Gastric Cancer |
| title_sort | deep convolutional neural network based on computed tomography images for the preoperative diagnosis of occult peritoneal metastasis in advanced gastric cancer |
| topic | stomach neoplasms peritoneal neoplasms deep learning tomography x-ray computed neural networks |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2020.601869/full |
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