Preoperative CT-Based Deep Learning Model for Predicting Risk Stratification in Patients With Gastrointestinal Stromal Tumors

ObjectiveTo develop and evaluate a deep learning model (DLM) for predicting the risk stratification of gastrointestinal stromal tumors (GISTs).MethodsPreoperative contrast-enhanced CT images of 733 patients with GISTs were retrospectively obtained from two centers between January 2011 and June 2020....
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Autor*in:	Bing Kang [verfasserIn] Xianshun Yuan [verfasserIn] Hexiang Wang [verfasserIn] Songnan Qin [verfasserIn] Xuelin Song [verfasserIn] Xinxin Yu [verfasserIn] Shuai Zhang [verfasserIn] Cong Sun [verfasserIn] Qing Zhou [verfasserIn] Ying Wei [verfasserIn] Feng Shi [verfasserIn] Shifeng Yang [verfasserIn] Ximing Wang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	gastrointestinal stromal tumors risk assessment deep learning tomography X-ray computed prediction model

Übergeordnetes Werk:	In: Frontiers in Oncology - Frontiers Media S.A., 2012, 11(2021)
Übergeordnetes Werk:	volume:11 ; year:2021

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fonc.2021.750875

Katalog-ID:	DOAJ049088114

Internformat


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245	1	0	\|a Preoperative CT-Based Deep Learning Model for Predicting Risk Stratification in Patients With Gastrointestinal Stromal Tumors
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520			\|a ObjectiveTo develop and evaluate a deep learning model (DLM) for predicting the risk stratification of gastrointestinal stromal tumors (GISTs).MethodsPreoperative contrast-enhanced CT images of 733 patients with GISTs were retrospectively obtained from two centers between January 2011 and June 2020. The datasets were split into training (n = 241), testing (n = 104), and external validation cohorts (n = 388). A DLM for predicting the risk stratification of GISTs was developed using a convolutional neural network and evaluated in the testing and external validation cohorts. The performance of the DLM was compared with that of radiomics model by using the area under the receiver operating characteristic curves (AUROCs) and the Obuchowski index. The attention area of the DLM was visualized as a heatmap by gradient-weighted class activation mapping.ResultsIn the testing cohort, the DLM had AUROCs of 0.90 (95% confidence interval [CI]: 0.84, 0.96), 0.80 (95% CI: 0.72, 0.88), and 0.89 (95% CI: 0.83, 0.95) for low-malignant, intermediate-malignant, and high-malignant GISTs, respectively. In the external validation cohort, the AUROCs of the DLM were 0.87 (95% CI: 0.83, 0.91), 0.64 (95% CI: 0.60, 0.68), and 0.85 (95% CI: 0.81, 0.89) for low-malignant, intermediate-malignant, and high-malignant GISTs, respectively. The DLM (Obuchowski index: training, 0.84; external validation, 0.79) outperformed the radiomics model (Obuchowski index: training, 0.77; external validation, 0.77) for predicting risk stratification of GISTs. The relevant subregions were successfully highlighted with attention heatmap on the CT images for further clinical review.ConclusionThe DLM showed good performance for predicting the risk stratification of GISTs using CT images and achieved better performance than that of radiomics model.
650		4	\|a gastrointestinal stromal tumors
650		4	\|a risk assessment
650		4	\|a deep learning
650		4	\|a tomography
650		4	\|a X-ray computed
650		4	\|a prediction model
653		0	\|a Neoplasms. Tumors. Oncology. Including cancer and carcinogens
700	0		\|a Bing Kang \|e verfasserin \|4 aut
700	0		\|a Xianshun Yuan \|e verfasserin \|4 aut
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700	0		\|a Shuai Zhang \|e verfasserin \|4 aut
700	0		\|a Cong Sun \|e verfasserin \|4 aut
700	0		\|a Qing Zhou \|e verfasserin \|4 aut
700	0		\|a Ying Wei \|e verfasserin \|4 aut
700	0		\|a Feng Shi \|e verfasserin \|4 aut
700	0		\|a Shifeng Yang \|e verfasserin \|4 aut
700	0		\|a Ximing Wang \|e verfasserin \|4 aut
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allfields	10.3389/fonc.2021.750875 doi (DE-627)DOAJ049088114 (DE-599)DOAJ9c5d0c83da294c949412eb05e45ac734 DE-627 ger DE-627 rakwb eng RC254-282 Bing Kang verfasserin aut Preoperative CT-Based Deep Learning Model for Predicting Risk Stratification in Patients With Gastrointestinal Stromal Tumors 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier ObjectiveTo develop and evaluate a deep learning model (DLM) for predicting the risk stratification of gastrointestinal stromal tumors (GISTs).MethodsPreoperative contrast-enhanced CT images of 733 patients with GISTs were retrospectively obtained from two centers between January 2011 and June 2020. The datasets were split into training (n = 241), testing (n = 104), and external validation cohorts (n = 388). A DLM for predicting the risk stratification of GISTs was developed using a convolutional neural network and evaluated in the testing and external validation cohorts. The performance of the DLM was compared with that of radiomics model by using the area under the receiver operating characteristic curves (AUROCs) and the Obuchowski index. The attention area of the DLM was visualized as a heatmap by gradient-weighted class activation mapping.ResultsIn the testing cohort, the DLM had AUROCs of 0.90 (95% confidence interval [CI]: 0.84, 0.96), 0.80 (95% CI: 0.72, 0.88), and 0.89 (95% CI: 0.83, 0.95) for low-malignant, intermediate-malignant, and high-malignant GISTs, respectively. In the external validation cohort, the AUROCs of the DLM were 0.87 (95% CI: 0.83, 0.91), 0.64 (95% CI: 0.60, 0.68), and 0.85 (95% CI: 0.81, 0.89) for low-malignant, intermediate-malignant, and high-malignant GISTs, respectively. The DLM (Obuchowski index: training, 0.84; external validation, 0.79) outperformed the radiomics model (Obuchowski index: training, 0.77; external validation, 0.77) for predicting risk stratification of GISTs. The relevant subregions were successfully highlighted with attention heatmap on the CT images for further clinical review.ConclusionThe DLM showed good performance for predicting the risk stratification of GISTs using CT images and achieved better performance than that of radiomics model. gastrointestinal stromal tumors risk assessment deep learning tomography X-ray computed prediction model Neoplasms. Tumors. Oncology. Including cancer and carcinogens Bing Kang verfasserin aut Xianshun Yuan verfasserin aut Xianshun Yuan verfasserin aut Hexiang Wang verfasserin aut Songnan Qin verfasserin aut Songnan Qin verfasserin aut Xuelin Song verfasserin aut Xinxin Yu verfasserin aut Xinxin Yu verfasserin aut Shuai Zhang verfasserin aut Cong Sun verfasserin aut Qing Zhou verfasserin aut Ying Wei verfasserin aut Feng Shi verfasserin aut Shifeng Yang verfasserin aut Ximing Wang verfasserin aut In Frontiers in Oncology Frontiers Media S.A., 2012 11(2021) (DE-627)684965518 (DE-600)2649216-7 2234943X nnns volume:11 year:2021 https://doi.org/10.3389/fonc.2021.750875 kostenfrei https://doaj.org/article/9c5d0c83da294c949412eb05e45ac734 kostenfrei https://www.frontiersin.org/articles/10.3389/fonc.2021.750875/full kostenfrei https://doaj.org/toc/2234-943X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021
spelling	10.3389/fonc.2021.750875 doi (DE-627)DOAJ049088114 (DE-599)DOAJ9c5d0c83da294c949412eb05e45ac734 DE-627 ger DE-627 rakwb eng RC254-282 Bing Kang verfasserin aut Preoperative CT-Based Deep Learning Model for Predicting Risk Stratification in Patients With Gastrointestinal Stromal Tumors 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier ObjectiveTo develop and evaluate a deep learning model (DLM) for predicting the risk stratification of gastrointestinal stromal tumors (GISTs).MethodsPreoperative contrast-enhanced CT images of 733 patients with GISTs were retrospectively obtained from two centers between January 2011 and June 2020. The datasets were split into training (n = 241), testing (n = 104), and external validation cohorts (n = 388). A DLM for predicting the risk stratification of GISTs was developed using a convolutional neural network and evaluated in the testing and external validation cohorts. The performance of the DLM was compared with that of radiomics model by using the area under the receiver operating characteristic curves (AUROCs) and the Obuchowski index. The attention area of the DLM was visualized as a heatmap by gradient-weighted class activation mapping.ResultsIn the testing cohort, the DLM had AUROCs of 0.90 (95% confidence interval [CI]: 0.84, 0.96), 0.80 (95% CI: 0.72, 0.88), and 0.89 (95% CI: 0.83, 0.95) for low-malignant, intermediate-malignant, and high-malignant GISTs, respectively. In the external validation cohort, the AUROCs of the DLM were 0.87 (95% CI: 0.83, 0.91), 0.64 (95% CI: 0.60, 0.68), and 0.85 (95% CI: 0.81, 0.89) for low-malignant, intermediate-malignant, and high-malignant GISTs, respectively. The DLM (Obuchowski index: training, 0.84; external validation, 0.79) outperformed the radiomics model (Obuchowski index: training, 0.77; external validation, 0.77) for predicting risk stratification of GISTs. The relevant subregions were successfully highlighted with attention heatmap on the CT images for further clinical review.ConclusionThe DLM showed good performance for predicting the risk stratification of GISTs using CT images and achieved better performance than that of radiomics model. gastrointestinal stromal tumors risk assessment deep learning tomography X-ray computed prediction model Neoplasms. Tumors. Oncology. Including cancer and carcinogens Bing Kang verfasserin aut Xianshun Yuan verfasserin aut Xianshun Yuan verfasserin aut Hexiang Wang verfasserin aut Songnan Qin verfasserin aut Songnan Qin verfasserin aut Xuelin Song verfasserin aut Xinxin Yu verfasserin aut Xinxin Yu verfasserin aut Shuai Zhang verfasserin aut Cong Sun verfasserin aut Qing Zhou verfasserin aut Ying Wei verfasserin aut Feng Shi verfasserin aut Shifeng Yang verfasserin aut Ximing Wang verfasserin aut In Frontiers in Oncology Frontiers Media S.A., 2012 11(2021) (DE-627)684965518 (DE-600)2649216-7 2234943X nnns volume:11 year:2021 https://doi.org/10.3389/fonc.2021.750875 kostenfrei https://doaj.org/article/9c5d0c83da294c949412eb05e45ac734 kostenfrei https://www.frontiersin.org/articles/10.3389/fonc.2021.750875/full kostenfrei https://doaj.org/toc/2234-943X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021
allfields_unstemmed	10.3389/fonc.2021.750875 doi (DE-627)DOAJ049088114 (DE-599)DOAJ9c5d0c83da294c949412eb05e45ac734 DE-627 ger DE-627 rakwb eng RC254-282 Bing Kang verfasserin aut Preoperative CT-Based Deep Learning Model for Predicting Risk Stratification in Patients With Gastrointestinal Stromal Tumors 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier ObjectiveTo develop and evaluate a deep learning model (DLM) for predicting the risk stratification of gastrointestinal stromal tumors (GISTs).MethodsPreoperative contrast-enhanced CT images of 733 patients with GISTs were retrospectively obtained from two centers between January 2011 and June 2020. The datasets were split into training (n = 241), testing (n = 104), and external validation cohorts (n = 388). A DLM for predicting the risk stratification of GISTs was developed using a convolutional neural network and evaluated in the testing and external validation cohorts. The performance of the DLM was compared with that of radiomics model by using the area under the receiver operating characteristic curves (AUROCs) and the Obuchowski index. The attention area of the DLM was visualized as a heatmap by gradient-weighted class activation mapping.ResultsIn the testing cohort, the DLM had AUROCs of 0.90 (95% confidence interval [CI]: 0.84, 0.96), 0.80 (95% CI: 0.72, 0.88), and 0.89 (95% CI: 0.83, 0.95) for low-malignant, intermediate-malignant, and high-malignant GISTs, respectively. In the external validation cohort, the AUROCs of the DLM were 0.87 (95% CI: 0.83, 0.91), 0.64 (95% CI: 0.60, 0.68), and 0.85 (95% CI: 0.81, 0.89) for low-malignant, intermediate-malignant, and high-malignant GISTs, respectively. The DLM (Obuchowski index: training, 0.84; external validation, 0.79) outperformed the radiomics model (Obuchowski index: training, 0.77; external validation, 0.77) for predicting risk stratification of GISTs. The relevant subregions were successfully highlighted with attention heatmap on the CT images for further clinical review.ConclusionThe DLM showed good performance for predicting the risk stratification of GISTs using CT images and achieved better performance than that of radiomics model. gastrointestinal stromal tumors risk assessment deep learning tomography X-ray computed prediction model Neoplasms. Tumors. Oncology. Including cancer and carcinogens Bing Kang verfasserin aut Xianshun Yuan verfasserin aut Xianshun Yuan verfasserin aut Hexiang Wang verfasserin aut Songnan Qin verfasserin aut Songnan Qin verfasserin aut Xuelin Song verfasserin aut Xinxin Yu verfasserin aut Xinxin Yu verfasserin aut Shuai Zhang verfasserin aut Cong Sun verfasserin aut Qing Zhou verfasserin aut Ying Wei verfasserin aut Feng Shi verfasserin aut Shifeng Yang verfasserin aut Ximing Wang verfasserin aut In Frontiers in Oncology Frontiers Media S.A., 2012 11(2021) (DE-627)684965518 (DE-600)2649216-7 2234943X nnns volume:11 year:2021 https://doi.org/10.3389/fonc.2021.750875 kostenfrei https://doaj.org/article/9c5d0c83da294c949412eb05e45ac734 kostenfrei https://www.frontiersin.org/articles/10.3389/fonc.2021.750875/full kostenfrei https://doaj.org/toc/2234-943X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021
allfieldsGer	10.3389/fonc.2021.750875 doi (DE-627)DOAJ049088114 (DE-599)DOAJ9c5d0c83da294c949412eb05e45ac734 DE-627 ger DE-627 rakwb eng RC254-282 Bing Kang verfasserin aut Preoperative CT-Based Deep Learning Model for Predicting Risk Stratification in Patients With Gastrointestinal Stromal Tumors 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier ObjectiveTo develop and evaluate a deep learning model (DLM) for predicting the risk stratification of gastrointestinal stromal tumors (GISTs).MethodsPreoperative contrast-enhanced CT images of 733 patients with GISTs were retrospectively obtained from two centers between January 2011 and June 2020. The datasets were split into training (n = 241), testing (n = 104), and external validation cohorts (n = 388). A DLM for predicting the risk stratification of GISTs was developed using a convolutional neural network and evaluated in the testing and external validation cohorts. The performance of the DLM was compared with that of radiomics model by using the area under the receiver operating characteristic curves (AUROCs) and the Obuchowski index. The attention area of the DLM was visualized as a heatmap by gradient-weighted class activation mapping.ResultsIn the testing cohort, the DLM had AUROCs of 0.90 (95% confidence interval [CI]: 0.84, 0.96), 0.80 (95% CI: 0.72, 0.88), and 0.89 (95% CI: 0.83, 0.95) for low-malignant, intermediate-malignant, and high-malignant GISTs, respectively. In the external validation cohort, the AUROCs of the DLM were 0.87 (95% CI: 0.83, 0.91), 0.64 (95% CI: 0.60, 0.68), and 0.85 (95% CI: 0.81, 0.89) for low-malignant, intermediate-malignant, and high-malignant GISTs, respectively. The DLM (Obuchowski index: training, 0.84; external validation, 0.79) outperformed the radiomics model (Obuchowski index: training, 0.77; external validation, 0.77) for predicting risk stratification of GISTs. The relevant subregions were successfully highlighted with attention heatmap on the CT images for further clinical review.ConclusionThe DLM showed good performance for predicting the risk stratification of GISTs using CT images and achieved better performance than that of radiomics model. gastrointestinal stromal tumors risk assessment deep learning tomography X-ray computed prediction model Neoplasms. Tumors. Oncology. Including cancer and carcinogens Bing Kang verfasserin aut Xianshun Yuan verfasserin aut Xianshun Yuan verfasserin aut Hexiang Wang verfasserin aut Songnan Qin verfasserin aut Songnan Qin verfasserin aut Xuelin Song verfasserin aut Xinxin Yu verfasserin aut Xinxin Yu verfasserin aut Shuai Zhang verfasserin aut Cong Sun verfasserin aut Qing Zhou verfasserin aut Ying Wei verfasserin aut Feng Shi verfasserin aut Shifeng Yang verfasserin aut Ximing Wang verfasserin aut In Frontiers in Oncology Frontiers Media S.A., 2012 11(2021) (DE-627)684965518 (DE-600)2649216-7 2234943X nnns volume:11 year:2021 https://doi.org/10.3389/fonc.2021.750875 kostenfrei https://doaj.org/article/9c5d0c83da294c949412eb05e45ac734 kostenfrei https://www.frontiersin.org/articles/10.3389/fonc.2021.750875/full kostenfrei https://doaj.org/toc/2234-943X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021
allfieldsSound	10.3389/fonc.2021.750875 doi (DE-627)DOAJ049088114 (DE-599)DOAJ9c5d0c83da294c949412eb05e45ac734 DE-627 ger DE-627 rakwb eng RC254-282 Bing Kang verfasserin aut Preoperative CT-Based Deep Learning Model for Predicting Risk Stratification in Patients With Gastrointestinal Stromal Tumors 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier ObjectiveTo develop and evaluate a deep learning model (DLM) for predicting the risk stratification of gastrointestinal stromal tumors (GISTs).MethodsPreoperative contrast-enhanced CT images of 733 patients with GISTs were retrospectively obtained from two centers between January 2011 and June 2020. The datasets were split into training (n = 241), testing (n = 104), and external validation cohorts (n = 388). A DLM for predicting the risk stratification of GISTs was developed using a convolutional neural network and evaluated in the testing and external validation cohorts. The performance of the DLM was compared with that of radiomics model by using the area under the receiver operating characteristic curves (AUROCs) and the Obuchowski index. The attention area of the DLM was visualized as a heatmap by gradient-weighted class activation mapping.ResultsIn the testing cohort, the DLM had AUROCs of 0.90 (95% confidence interval [CI]: 0.84, 0.96), 0.80 (95% CI: 0.72, 0.88), and 0.89 (95% CI: 0.83, 0.95) for low-malignant, intermediate-malignant, and high-malignant GISTs, respectively. In the external validation cohort, the AUROCs of the DLM were 0.87 (95% CI: 0.83, 0.91), 0.64 (95% CI: 0.60, 0.68), and 0.85 (95% CI: 0.81, 0.89) for low-malignant, intermediate-malignant, and high-malignant GISTs, respectively. The DLM (Obuchowski index: training, 0.84; external validation, 0.79) outperformed the radiomics model (Obuchowski index: training, 0.77; external validation, 0.77) for predicting risk stratification of GISTs. The relevant subregions were successfully highlighted with attention heatmap on the CT images for further clinical review.ConclusionThe DLM showed good performance for predicting the risk stratification of GISTs using CT images and achieved better performance than that of radiomics model. gastrointestinal stromal tumors risk assessment deep learning tomography X-ray computed prediction model Neoplasms. Tumors. Oncology. Including cancer and carcinogens Bing Kang verfasserin aut Xianshun Yuan verfasserin aut Xianshun Yuan verfasserin aut Hexiang Wang verfasserin aut Songnan Qin verfasserin aut Songnan Qin verfasserin aut Xuelin Song verfasserin aut Xinxin Yu verfasserin aut Xinxin Yu verfasserin aut Shuai Zhang verfasserin aut Cong Sun verfasserin aut Qing Zhou verfasserin aut Ying Wei verfasserin aut Feng Shi verfasserin aut Shifeng Yang verfasserin aut Ximing Wang verfasserin aut In Frontiers in Oncology Frontiers Media S.A., 2012 11(2021) (DE-627)684965518 (DE-600)2649216-7 2234943X nnns volume:11 year:2021 https://doi.org/10.3389/fonc.2021.750875 kostenfrei https://doaj.org/article/9c5d0c83da294c949412eb05e45ac734 kostenfrei https://www.frontiersin.org/articles/10.3389/fonc.2021.750875/full kostenfrei https://doaj.org/toc/2234-943X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021
language	English
source	In Frontiers in Oncology 11(2021) volume:11 year:2021
sourceStr	In Frontiers in Oncology 11(2021) volume:11 year:2021
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	gastrointestinal stromal tumors risk assessment deep learning tomography X-ray computed prediction model Neoplasms. Tumors. Oncology. Including cancer and carcinogens
isfreeaccess_bool	true
container_title	Frontiers in Oncology
authorswithroles_txt_mv	Bing Kang @@aut@@ Xianshun Yuan @@aut@@ Hexiang Wang @@aut@@ Songnan Qin @@aut@@ Xuelin Song @@aut@@ Xinxin Yu @@aut@@ Shuai Zhang @@aut@@ Cong Sun @@aut@@ Qing Zhou @@aut@@ Ying Wei @@aut@@ Feng Shi @@aut@@ Shifeng Yang @@aut@@ Ximing Wang @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	684965518
id	DOAJ049088114
language_de	englisch
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The datasets were split into training (n = 241), testing (n = 104), and external validation cohorts (n = 388). A DLM for predicting the risk stratification of GISTs was developed using a convolutional neural network and evaluated in the testing and external validation cohorts. The performance of the DLM was compared with that of radiomics model by using the area under the receiver operating characteristic curves (AUROCs) and the Obuchowski index. The attention area of the DLM was visualized as a heatmap by gradient-weighted class activation mapping.ResultsIn the testing cohort, the DLM had AUROCs of 0.90 (95% confidence interval [CI]: 0.84, 0.96), 0.80 (95% CI: 0.72, 0.88), and 0.89 (95% CI: 0.83, 0.95) for low-malignant, intermediate-malignant, and high-malignant GISTs, respectively. In the external validation cohort, the AUROCs of the DLM were 0.87 (95% CI: 0.83, 0.91), 0.64 (95% CI: 0.60, 0.68), and 0.85 (95% CI: 0.81, 0.89) for low-malignant, intermediate-malignant, and high-malignant GISTs, respectively. The DLM (Obuchowski index: training, 0.84; external validation, 0.79) outperformed the radiomics model (Obuchowski index: training, 0.77; external validation, 0.77) for predicting risk stratification of GISTs. The relevant subregions were successfully highlighted with attention heatmap on the CT images for further clinical review.ConclusionThe DLM showed good performance for predicting the risk stratification of GISTs using CT images and achieved better performance than that of radiomics model.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">gastrointestinal stromal tumors</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">risk assessment</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">deep learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">tomography</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">X-ray computed</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">prediction model</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Neoplasms. Tumors. Oncology. 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callnumber-first	R - Medicine
author	Bing Kang
spellingShingle	Bing Kang misc RC254-282 misc gastrointestinal stromal tumors misc risk assessment misc deep learning misc tomography misc X-ray computed misc prediction model misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens Preoperative CT-Based Deep Learning Model for Predicting Risk Stratification in Patients With Gastrointestinal Stromal Tumors
authorStr	Bing Kang
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topic_title	RC254-282 Preoperative CT-Based Deep Learning Model for Predicting Risk Stratification in Patients With Gastrointestinal Stromal Tumors gastrointestinal stromal tumors risk assessment deep learning tomography X-ray computed prediction model
topic	misc RC254-282 misc gastrointestinal stromal tumors misc risk assessment misc deep learning misc tomography misc X-ray computed misc prediction model misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
topic_unstemmed	misc RC254-282 misc gastrointestinal stromal tumors misc risk assessment misc deep learning misc tomography misc X-ray computed misc prediction model misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
topic_browse	misc RC254-282 misc gastrointestinal stromal tumors misc risk assessment misc deep learning misc tomography misc X-ray computed misc prediction model misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
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title	Preoperative CT-Based Deep Learning Model for Predicting Risk Stratification in Patients With Gastrointestinal Stromal Tumors
ctrlnum	(DE-627)DOAJ049088114 (DE-599)DOAJ9c5d0c83da294c949412eb05e45ac734
title_full	Preoperative CT-Based Deep Learning Model for Predicting Risk Stratification in Patients With Gastrointestinal Stromal Tumors
author_sort	Bing Kang
journal	Frontiers in Oncology
journalStr	Frontiers in Oncology
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author_browse	Bing Kang Xianshun Yuan Hexiang Wang Songnan Qin Xuelin Song Xinxin Yu Shuai Zhang Cong Sun Qing Zhou Ying Wei Feng Shi Shifeng Yang Ximing Wang
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author-letter	Bing Kang
doi_str_mv	10.3389/fonc.2021.750875
author2-role	verfasserin
title_sort	preoperative ct-based deep learning model for predicting risk stratification in patients with gastrointestinal stromal tumors
callnumber	RC254-282
title_auth	Preoperative CT-Based Deep Learning Model for Predicting Risk Stratification in Patients With Gastrointestinal Stromal Tumors
abstract	ObjectiveTo develop and evaluate a deep learning model (DLM) for predicting the risk stratification of gastrointestinal stromal tumors (GISTs).MethodsPreoperative contrast-enhanced CT images of 733 patients with GISTs were retrospectively obtained from two centers between January 2011 and June 2020. The datasets were split into training (n = 241), testing (n = 104), and external validation cohorts (n = 388). A DLM for predicting the risk stratification of GISTs was developed using a convolutional neural network and evaluated in the testing and external validation cohorts. The performance of the DLM was compared with that of radiomics model by using the area under the receiver operating characteristic curves (AUROCs) and the Obuchowski index. The attention area of the DLM was visualized as a heatmap by gradient-weighted class activation mapping.ResultsIn the testing cohort, the DLM had AUROCs of 0.90 (95% confidence interval [CI]: 0.84, 0.96), 0.80 (95% CI: 0.72, 0.88), and 0.89 (95% CI: 0.83, 0.95) for low-malignant, intermediate-malignant, and high-malignant GISTs, respectively. In the external validation cohort, the AUROCs of the DLM were 0.87 (95% CI: 0.83, 0.91), 0.64 (95% CI: 0.60, 0.68), and 0.85 (95% CI: 0.81, 0.89) for low-malignant, intermediate-malignant, and high-malignant GISTs, respectively. The DLM (Obuchowski index: training, 0.84; external validation, 0.79) outperformed the radiomics model (Obuchowski index: training, 0.77; external validation, 0.77) for predicting risk stratification of GISTs. The relevant subregions were successfully highlighted with attention heatmap on the CT images for further clinical review.ConclusionThe DLM showed good performance for predicting the risk stratification of GISTs using CT images and achieved better performance than that of radiomics model.
abstractGer	ObjectiveTo develop and evaluate a deep learning model (DLM) for predicting the risk stratification of gastrointestinal stromal tumors (GISTs).MethodsPreoperative contrast-enhanced CT images of 733 patients with GISTs were retrospectively obtained from two centers between January 2011 and June 2020. The datasets were split into training (n = 241), testing (n = 104), and external validation cohorts (n = 388). A DLM for predicting the risk stratification of GISTs was developed using a convolutional neural network and evaluated in the testing and external validation cohorts. The performance of the DLM was compared with that of radiomics model by using the area under the receiver operating characteristic curves (AUROCs) and the Obuchowski index. The attention area of the DLM was visualized as a heatmap by gradient-weighted class activation mapping.ResultsIn the testing cohort, the DLM had AUROCs of 0.90 (95% confidence interval [CI]: 0.84, 0.96), 0.80 (95% CI: 0.72, 0.88), and 0.89 (95% CI: 0.83, 0.95) for low-malignant, intermediate-malignant, and high-malignant GISTs, respectively. In the external validation cohort, the AUROCs of the DLM were 0.87 (95% CI: 0.83, 0.91), 0.64 (95% CI: 0.60, 0.68), and 0.85 (95% CI: 0.81, 0.89) for low-malignant, intermediate-malignant, and high-malignant GISTs, respectively. The DLM (Obuchowski index: training, 0.84; external validation, 0.79) outperformed the radiomics model (Obuchowski index: training, 0.77; external validation, 0.77) for predicting risk stratification of GISTs. The relevant subregions were successfully highlighted with attention heatmap on the CT images for further clinical review.ConclusionThe DLM showed good performance for predicting the risk stratification of GISTs using CT images and achieved better performance than that of radiomics model.
abstract_unstemmed	ObjectiveTo develop and evaluate a deep learning model (DLM) for predicting the risk stratification of gastrointestinal stromal tumors (GISTs).MethodsPreoperative contrast-enhanced CT images of 733 patients with GISTs were retrospectively obtained from two centers between January 2011 and June 2020. The datasets were split into training (n = 241), testing (n = 104), and external validation cohorts (n = 388). A DLM for predicting the risk stratification of GISTs was developed using a convolutional neural network and evaluated in the testing and external validation cohorts. The performance of the DLM was compared with that of radiomics model by using the area under the receiver operating characteristic curves (AUROCs) and the Obuchowski index. The attention area of the DLM was visualized as a heatmap by gradient-weighted class activation mapping.ResultsIn the testing cohort, the DLM had AUROCs of 0.90 (95% confidence interval [CI]: 0.84, 0.96), 0.80 (95% CI: 0.72, 0.88), and 0.89 (95% CI: 0.83, 0.95) for low-malignant, intermediate-malignant, and high-malignant GISTs, respectively. In the external validation cohort, the AUROCs of the DLM were 0.87 (95% CI: 0.83, 0.91), 0.64 (95% CI: 0.60, 0.68), and 0.85 (95% CI: 0.81, 0.89) for low-malignant, intermediate-malignant, and high-malignant GISTs, respectively. The DLM (Obuchowski index: training, 0.84; external validation, 0.79) outperformed the radiomics model (Obuchowski index: training, 0.77; external validation, 0.77) for predicting risk stratification of GISTs. The relevant subregions were successfully highlighted with attention heatmap on the CT images for further clinical review.ConclusionThe DLM showed good performance for predicting the risk stratification of GISTs using CT images and achieved better performance than that of radiomics model.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
title_short	Preoperative CT-Based Deep Learning Model for Predicting Risk Stratification in Patients With Gastrointestinal Stromal Tumors
url	https://doi.org/10.3389/fonc.2021.750875 https://doaj.org/article/9c5d0c83da294c949412eb05e45ac734 https://www.frontiersin.org/articles/10.3389/fonc.2021.750875/full https://doaj.org/toc/2234-943X
remote_bool	true
author2	Bing Kang Xianshun Yuan Hexiang Wang Songnan Qin Xuelin Song Xinxin Yu Shuai Zhang Cong Sun Qing Zhou Ying Wei Feng Shi Shifeng Yang Ximing Wang
author2Str	Bing Kang Xianshun Yuan Hexiang Wang Songnan Qin Xuelin Song Xinxin Yu Shuai Zhang Cong Sun Qing Zhou Ying Wei Feng Shi Shifeng Yang Ximing Wang
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doi_str	10.3389/fonc.2021.750875
callnumber-a	RC254-282
up_date	2024-07-03T21:21:10.096Z
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