A CT-based nomogram established for differentiating gastrointestinal heterotopic pancreas from gastrointestinal stromal tumor: compared with a machine-learning model

Abstract Objective To identify CT features and establish a nomogram, compared with a machine learning-based model for distinguishing gastrointestinal heterotopic pancreas (HP) from gastrointestinal stromal tumor (GIST). Materials and methods This retrospective study included 148 patients with pathol...
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Autor*in:	Na Feng [verfasserIn] Hai-Yan Chen [verfasserIn] Xiao-Jie Wang [verfasserIn] Yuan-Fei Lu [verfasserIn] Jia-Ping Zhou [verfasserIn] Qiao-Mei Zhou [verfasserIn] Xin-Bin Wang [verfasserIn] Jie-Ni Yu [verfasserIn] Ri-Sheng Yu [verfasserIn] Jian-Xia Xu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Heterotopic pancreas Gastrointestinal stromal tumor Nomogram Random Forest Computed tomography

Übergeordnetes Werk:	In: BMC Medical Imaging - BMC, 2003, 23(2023), 1, Seite 12
Übergeordnetes Werk:	volume:23 ; year:2023 ; number:1 ; pages:12

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/s12880-023-01094-3

Katalog-ID:	DOAJ092850227

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520			\|a Abstract Objective To identify CT features and establish a nomogram, compared with a machine learning-based model for distinguishing gastrointestinal heterotopic pancreas (HP) from gastrointestinal stromal tumor (GIST). Materials and methods This retrospective study included 148 patients with pathologically confirmed HP (n = 48) and GIST (n = 100) in the stomach or small intestine that were less than 3 cm in size. Clinical information and CT characteristics were collected. A nomogram on account of lasso regression and multivariate logistic regression, and a RandomForest (RF) model based on significant variables in univariate analyses were established. Receiver operating characteristic (ROC) curve, mean area under the curve (AUC), calibration curve and decision curve analysis (DCA) were carried out to evaluate and compare the diagnostic ability of models. Results The nomogram identified five CT features as independent predictors of HP diagnosis: age, location, LD/SD ratio, duct-like structure, and HU lesion/pancreas A. Five features were included in RF model and ranked according to their relevance to the differential diagnosis: LD/SD ratio, HU lesion/pancreas A, location, peritumoral hypodensity line and age. The nomogram and RF model yielded AUC of 0.951 (95% CI: 0.842–0.993) and 0.894 (95% CI: 0.766–0.966), respectively. The DeLong test found no statistically significant difference in diagnostic performance (p < 0.05), but DCA revealed that the nomogram surpassed the RF model in clinical usefulness. Conclusion Two diagnostic prediction models based on a nomogram as well as RF method were reliable and easy-to-use for distinguishing between HP and GIST, which might also assist treatment planning.
650		4	\|a Heterotopic pancreas
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allfields	10.1186/s12880-023-01094-3 doi (DE-627)DOAJ092850227 (DE-599)DOAJ8ba3c953909d4fbf8be756da56a4dcae DE-627 ger DE-627 rakwb eng R855-855.5 Na Feng verfasserin aut A CT-based nomogram established for differentiating gastrointestinal heterotopic pancreas from gastrointestinal stromal tumor: compared with a machine-learning model 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Objective To identify CT features and establish a nomogram, compared with a machine learning-based model for distinguishing gastrointestinal heterotopic pancreas (HP) from gastrointestinal stromal tumor (GIST). Materials and methods This retrospective study included 148 patients with pathologically confirmed HP (n = 48) and GIST (n = 100) in the stomach or small intestine that were less than 3 cm in size. Clinical information and CT characteristics were collected. A nomogram on account of lasso regression and multivariate logistic regression, and a RandomForest (RF) model based on significant variables in univariate analyses were established. Receiver operating characteristic (ROC) curve, mean area under the curve (AUC), calibration curve and decision curve analysis (DCA) were carried out to evaluate and compare the diagnostic ability of models. Results The nomogram identified five CT features as independent predictors of HP diagnosis: age, location, LD/SD ratio, duct-like structure, and HU lesion/pancreas A. Five features were included in RF model and ranked according to their relevance to the differential diagnosis: LD/SD ratio, HU lesion/pancreas A, location, peritumoral hypodensity line and age. The nomogram and RF model yielded AUC of 0.951 (95% CI: 0.842–0.993) and 0.894 (95% CI: 0.766–0.966), respectively. The DeLong test found no statistically significant difference in diagnostic performance (p < 0.05), but DCA revealed that the nomogram surpassed the RF model in clinical usefulness. Conclusion Two diagnostic prediction models based on a nomogram as well as RF method were reliable and easy-to-use for distinguishing between HP and GIST, which might also assist treatment planning. Heterotopic pancreas Gastrointestinal stromal tumor Nomogram Random Forest Computed tomography Medical technology Hai-Yan Chen verfasserin aut Xiao-Jie Wang verfasserin aut Yuan-Fei Lu verfasserin aut Jia-Ping Zhou verfasserin aut Qiao-Mei Zhou verfasserin aut Xin-Bin Wang verfasserin aut Jie-Ni Yu verfasserin aut Ri-Sheng Yu verfasserin aut Jian-Xia Xu verfasserin aut In BMC Medical Imaging BMC, 2003 23(2023), 1, Seite 12 (DE-627)33679911X (DE-600)2061975-3 14712342 nnns volume:23 year:2023 number:1 pages:12 https://doi.org/10.1186/s12880-023-01094-3 kostenfrei https://doaj.org/article/8ba3c953909d4fbf8be756da56a4dcae kostenfrei https://doi.org/10.1186/s12880-023-01094-3 kostenfrei https://doaj.org/toc/1471-2342 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 23 2023 1 12
spelling	10.1186/s12880-023-01094-3 doi (DE-627)DOAJ092850227 (DE-599)DOAJ8ba3c953909d4fbf8be756da56a4dcae DE-627 ger DE-627 rakwb eng R855-855.5 Na Feng verfasserin aut A CT-based nomogram established for differentiating gastrointestinal heterotopic pancreas from gastrointestinal stromal tumor: compared with a machine-learning model 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Objective To identify CT features and establish a nomogram, compared with a machine learning-based model for distinguishing gastrointestinal heterotopic pancreas (HP) from gastrointestinal stromal tumor (GIST). Materials and methods This retrospective study included 148 patients with pathologically confirmed HP (n = 48) and GIST (n = 100) in the stomach or small intestine that were less than 3 cm in size. Clinical information and CT characteristics were collected. A nomogram on account of lasso regression and multivariate logistic regression, and a RandomForest (RF) model based on significant variables in univariate analyses were established. Receiver operating characteristic (ROC) curve, mean area under the curve (AUC), calibration curve and decision curve analysis (DCA) were carried out to evaluate and compare the diagnostic ability of models. Results The nomogram identified five CT features as independent predictors of HP diagnosis: age, location, LD/SD ratio, duct-like structure, and HU lesion/pancreas A. Five features were included in RF model and ranked according to their relevance to the differential diagnosis: LD/SD ratio, HU lesion/pancreas A, location, peritumoral hypodensity line and age. The nomogram and RF model yielded AUC of 0.951 (95% CI: 0.842–0.993) and 0.894 (95% CI: 0.766–0.966), respectively. The DeLong test found no statistically significant difference in diagnostic performance (p < 0.05), but DCA revealed that the nomogram surpassed the RF model in clinical usefulness. Conclusion Two diagnostic prediction models based on a nomogram as well as RF method were reliable and easy-to-use for distinguishing between HP and GIST, which might also assist treatment planning. Heterotopic pancreas Gastrointestinal stromal tumor Nomogram Random Forest Computed tomography Medical technology Hai-Yan Chen verfasserin aut Xiao-Jie Wang verfasserin aut Yuan-Fei Lu verfasserin aut Jia-Ping Zhou verfasserin aut Qiao-Mei Zhou verfasserin aut Xin-Bin Wang verfasserin aut Jie-Ni Yu verfasserin aut Ri-Sheng Yu verfasserin aut Jian-Xia Xu verfasserin aut In BMC Medical Imaging BMC, 2003 23(2023), 1, Seite 12 (DE-627)33679911X (DE-600)2061975-3 14712342 nnns volume:23 year:2023 number:1 pages:12 https://doi.org/10.1186/s12880-023-01094-3 kostenfrei https://doaj.org/article/8ba3c953909d4fbf8be756da56a4dcae kostenfrei https://doi.org/10.1186/s12880-023-01094-3 kostenfrei https://doaj.org/toc/1471-2342 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 23 2023 1 12
allfields_unstemmed	10.1186/s12880-023-01094-3 doi (DE-627)DOAJ092850227 (DE-599)DOAJ8ba3c953909d4fbf8be756da56a4dcae DE-627 ger DE-627 rakwb eng R855-855.5 Na Feng verfasserin aut A CT-based nomogram established for differentiating gastrointestinal heterotopic pancreas from gastrointestinal stromal tumor: compared with a machine-learning model 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Objective To identify CT features and establish a nomogram, compared with a machine learning-based model for distinguishing gastrointestinal heterotopic pancreas (HP) from gastrointestinal stromal tumor (GIST). Materials and methods This retrospective study included 148 patients with pathologically confirmed HP (n = 48) and GIST (n = 100) in the stomach or small intestine that were less than 3 cm in size. Clinical information and CT characteristics were collected. A nomogram on account of lasso regression and multivariate logistic regression, and a RandomForest (RF) model based on significant variables in univariate analyses were established. Receiver operating characteristic (ROC) curve, mean area under the curve (AUC), calibration curve and decision curve analysis (DCA) were carried out to evaluate and compare the diagnostic ability of models. Results The nomogram identified five CT features as independent predictors of HP diagnosis: age, location, LD/SD ratio, duct-like structure, and HU lesion/pancreas A. Five features were included in RF model and ranked according to their relevance to the differential diagnosis: LD/SD ratio, HU lesion/pancreas A, location, peritumoral hypodensity line and age. The nomogram and RF model yielded AUC of 0.951 (95% CI: 0.842–0.993) and 0.894 (95% CI: 0.766–0.966), respectively. The DeLong test found no statistically significant difference in diagnostic performance (p < 0.05), but DCA revealed that the nomogram surpassed the RF model in clinical usefulness. Conclusion Two diagnostic prediction models based on a nomogram as well as RF method were reliable and easy-to-use for distinguishing between HP and GIST, which might also assist treatment planning. Heterotopic pancreas Gastrointestinal stromal tumor Nomogram Random Forest Computed tomography Medical technology Hai-Yan Chen verfasserin aut Xiao-Jie Wang verfasserin aut Yuan-Fei Lu verfasserin aut Jia-Ping Zhou verfasserin aut Qiao-Mei Zhou verfasserin aut Xin-Bin Wang verfasserin aut Jie-Ni Yu verfasserin aut Ri-Sheng Yu verfasserin aut Jian-Xia Xu verfasserin aut In BMC Medical Imaging BMC, 2003 23(2023), 1, Seite 12 (DE-627)33679911X (DE-600)2061975-3 14712342 nnns volume:23 year:2023 number:1 pages:12 https://doi.org/10.1186/s12880-023-01094-3 kostenfrei https://doaj.org/article/8ba3c953909d4fbf8be756da56a4dcae kostenfrei https://doi.org/10.1186/s12880-023-01094-3 kostenfrei https://doaj.org/toc/1471-2342 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 23 2023 1 12
allfieldsGer	10.1186/s12880-023-01094-3 doi (DE-627)DOAJ092850227 (DE-599)DOAJ8ba3c953909d4fbf8be756da56a4dcae DE-627 ger DE-627 rakwb eng R855-855.5 Na Feng verfasserin aut A CT-based nomogram established for differentiating gastrointestinal heterotopic pancreas from gastrointestinal stromal tumor: compared with a machine-learning model 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Objective To identify CT features and establish a nomogram, compared with a machine learning-based model for distinguishing gastrointestinal heterotopic pancreas (HP) from gastrointestinal stromal tumor (GIST). Materials and methods This retrospective study included 148 patients with pathologically confirmed HP (n = 48) and GIST (n = 100) in the stomach or small intestine that were less than 3 cm in size. Clinical information and CT characteristics were collected. A nomogram on account of lasso regression and multivariate logistic regression, and a RandomForest (RF) model based on significant variables in univariate analyses were established. Receiver operating characteristic (ROC) curve, mean area under the curve (AUC), calibration curve and decision curve analysis (DCA) were carried out to evaluate and compare the diagnostic ability of models. Results The nomogram identified five CT features as independent predictors of HP diagnosis: age, location, LD/SD ratio, duct-like structure, and HU lesion/pancreas A. Five features were included in RF model and ranked according to their relevance to the differential diagnosis: LD/SD ratio, HU lesion/pancreas A, location, peritumoral hypodensity line and age. The nomogram and RF model yielded AUC of 0.951 (95% CI: 0.842–0.993) and 0.894 (95% CI: 0.766–0.966), respectively. The DeLong test found no statistically significant difference in diagnostic performance (p < 0.05), but DCA revealed that the nomogram surpassed the RF model in clinical usefulness. Conclusion Two diagnostic prediction models based on a nomogram as well as RF method were reliable and easy-to-use for distinguishing between HP and GIST, which might also assist treatment planning. Heterotopic pancreas Gastrointestinal stromal tumor Nomogram Random Forest Computed tomography Medical technology Hai-Yan Chen verfasserin aut Xiao-Jie Wang verfasserin aut Yuan-Fei Lu verfasserin aut Jia-Ping Zhou verfasserin aut Qiao-Mei Zhou verfasserin aut Xin-Bin Wang verfasserin aut Jie-Ni Yu verfasserin aut Ri-Sheng Yu verfasserin aut Jian-Xia Xu verfasserin aut In BMC Medical Imaging BMC, 2003 23(2023), 1, Seite 12 (DE-627)33679911X (DE-600)2061975-3 14712342 nnns volume:23 year:2023 number:1 pages:12 https://doi.org/10.1186/s12880-023-01094-3 kostenfrei https://doaj.org/article/8ba3c953909d4fbf8be756da56a4dcae kostenfrei https://doi.org/10.1186/s12880-023-01094-3 kostenfrei https://doaj.org/toc/1471-2342 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 23 2023 1 12
allfieldsSound	10.1186/s12880-023-01094-3 doi (DE-627)DOAJ092850227 (DE-599)DOAJ8ba3c953909d4fbf8be756da56a4dcae DE-627 ger DE-627 rakwb eng R855-855.5 Na Feng verfasserin aut A CT-based nomogram established for differentiating gastrointestinal heterotopic pancreas from gastrointestinal stromal tumor: compared with a machine-learning model 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Objective To identify CT features and establish a nomogram, compared with a machine learning-based model for distinguishing gastrointestinal heterotopic pancreas (HP) from gastrointestinal stromal tumor (GIST). Materials and methods This retrospective study included 148 patients with pathologically confirmed HP (n = 48) and GIST (n = 100) in the stomach or small intestine that were less than 3 cm in size. Clinical information and CT characteristics were collected. A nomogram on account of lasso regression and multivariate logistic regression, and a RandomForest (RF) model based on significant variables in univariate analyses were established. Receiver operating characteristic (ROC) curve, mean area under the curve (AUC), calibration curve and decision curve analysis (DCA) were carried out to evaluate and compare the diagnostic ability of models. Results The nomogram identified five CT features as independent predictors of HP diagnosis: age, location, LD/SD ratio, duct-like structure, and HU lesion/pancreas A. Five features were included in RF model and ranked according to their relevance to the differential diagnosis: LD/SD ratio, HU lesion/pancreas A, location, peritumoral hypodensity line and age. The nomogram and RF model yielded AUC of 0.951 (95% CI: 0.842–0.993) and 0.894 (95% CI: 0.766–0.966), respectively. The DeLong test found no statistically significant difference in diagnostic performance (p < 0.05), but DCA revealed that the nomogram surpassed the RF model in clinical usefulness. Conclusion Two diagnostic prediction models based on a nomogram as well as RF method were reliable and easy-to-use for distinguishing between HP and GIST, which might also assist treatment planning. Heterotopic pancreas Gastrointestinal stromal tumor Nomogram Random Forest Computed tomography Medical technology Hai-Yan Chen verfasserin aut Xiao-Jie Wang verfasserin aut Yuan-Fei Lu verfasserin aut Jia-Ping Zhou verfasserin aut Qiao-Mei Zhou verfasserin aut Xin-Bin Wang verfasserin aut Jie-Ni Yu verfasserin aut Ri-Sheng Yu verfasserin aut Jian-Xia Xu verfasserin aut In BMC Medical Imaging BMC, 2003 23(2023), 1, Seite 12 (DE-627)33679911X (DE-600)2061975-3 14712342 nnns volume:23 year:2023 number:1 pages:12 https://doi.org/10.1186/s12880-023-01094-3 kostenfrei https://doaj.org/article/8ba3c953909d4fbf8be756da56a4dcae kostenfrei https://doi.org/10.1186/s12880-023-01094-3 kostenfrei https://doaj.org/toc/1471-2342 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 23 2023 1 12
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topic_facet	Heterotopic pancreas Gastrointestinal stromal tumor Nomogram Random Forest Computed tomography Medical technology
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authorswithroles_txt_mv	Na Feng @@aut@@ Hai-Yan Chen @@aut@@ Xiao-Jie Wang @@aut@@ Yuan-Fei Lu @@aut@@ Jia-Ping Zhou @@aut@@ Qiao-Mei Zhou @@aut@@ Xin-Bin Wang @@aut@@ Jie-Ni Yu @@aut@@ Ri-Sheng Yu @@aut@@ Jian-Xia Xu @@aut@@
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Materials and methods This retrospective study included 148 patients with pathologically confirmed HP (n = 48) and GIST (n = 100) in the stomach or small intestine that were less than 3 cm in size. Clinical information and CT characteristics were collected. A nomogram on account of lasso regression and multivariate logistic regression, and a RandomForest (RF) model based on significant variables in univariate analyses were established. Receiver operating characteristic (ROC) curve, mean area under the curve (AUC), calibration curve and decision curve analysis (DCA) were carried out to evaluate and compare the diagnostic ability of models. Results The nomogram identified five CT features as independent predictors of HP diagnosis: age, location, LD/SD ratio, duct-like structure, and HU lesion/pancreas A. Five features were included in RF model and ranked according to their relevance to the differential diagnosis: LD/SD ratio, HU lesion/pancreas A, location, peritumoral hypodensity line and age. The nomogram and RF model yielded AUC of 0.951 (95% CI: 0.842–0.993) and 0.894 (95% CI: 0.766–0.966), respectively. The DeLong test found no statistically significant difference in diagnostic performance (p < 0.05), but DCA revealed that the nomogram surpassed the RF model in clinical usefulness. 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callnumber-first	R - Medicine
author	Na Feng
spellingShingle	Na Feng misc R855-855.5 misc Heterotopic pancreas misc Gastrointestinal stromal tumor misc Nomogram misc Random Forest misc Computed tomography misc Medical technology A CT-based nomogram established for differentiating gastrointestinal heterotopic pancreas from gastrointestinal stromal tumor: compared with a machine-learning model
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topic_title	R855-855.5 A CT-based nomogram established for differentiating gastrointestinal heterotopic pancreas from gastrointestinal stromal tumor: compared with a machine-learning model Heterotopic pancreas Gastrointestinal stromal tumor Nomogram Random Forest Computed tomography
topic	misc R855-855.5 misc Heterotopic pancreas misc Gastrointestinal stromal tumor misc Nomogram misc Random Forest misc Computed tomography misc Medical technology
topic_unstemmed	misc R855-855.5 misc Heterotopic pancreas misc Gastrointestinal stromal tumor misc Nomogram misc Random Forest misc Computed tomography misc Medical technology
topic_browse	misc R855-855.5 misc Heterotopic pancreas misc Gastrointestinal stromal tumor misc Nomogram misc Random Forest misc Computed tomography misc Medical technology
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title	A CT-based nomogram established for differentiating gastrointestinal heterotopic pancreas from gastrointestinal stromal tumor: compared with a machine-learning model
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title_full	A CT-based nomogram established for differentiating gastrointestinal heterotopic pancreas from gastrointestinal stromal tumor: compared with a machine-learning model
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title_sort	ct-based nomogram established for differentiating gastrointestinal heterotopic pancreas from gastrointestinal stromal tumor: compared with a machine-learning model
callnumber	R855-855.5
title_auth	A CT-based nomogram established for differentiating gastrointestinal heterotopic pancreas from gastrointestinal stromal tumor: compared with a machine-learning model
abstract	Abstract Objective To identify CT features and establish a nomogram, compared with a machine learning-based model for distinguishing gastrointestinal heterotopic pancreas (HP) from gastrointestinal stromal tumor (GIST). Materials and methods This retrospective study included 148 patients with pathologically confirmed HP (n = 48) and GIST (n = 100) in the stomach or small intestine that were less than 3 cm in size. Clinical information and CT characteristics were collected. A nomogram on account of lasso regression and multivariate logistic regression, and a RandomForest (RF) model based on significant variables in univariate analyses were established. Receiver operating characteristic (ROC) curve, mean area under the curve (AUC), calibration curve and decision curve analysis (DCA) were carried out to evaluate and compare the diagnostic ability of models. Results The nomogram identified five CT features as independent predictors of HP diagnosis: age, location, LD/SD ratio, duct-like structure, and HU lesion/pancreas A. Five features were included in RF model and ranked according to their relevance to the differential diagnosis: LD/SD ratio, HU lesion/pancreas A, location, peritumoral hypodensity line and age. The nomogram and RF model yielded AUC of 0.951 (95% CI: 0.842–0.993) and 0.894 (95% CI: 0.766–0.966), respectively. The DeLong test found no statistically significant difference in diagnostic performance (p < 0.05), but DCA revealed that the nomogram surpassed the RF model in clinical usefulness. Conclusion Two diagnostic prediction models based on a nomogram as well as RF method were reliable and easy-to-use for distinguishing between HP and GIST, which might also assist treatment planning.
abstractGer	Abstract Objective To identify CT features and establish a nomogram, compared with a machine learning-based model for distinguishing gastrointestinal heterotopic pancreas (HP) from gastrointestinal stromal tumor (GIST). Materials and methods This retrospective study included 148 patients with pathologically confirmed HP (n = 48) and GIST (n = 100) in the stomach or small intestine that were less than 3 cm in size. Clinical information and CT characteristics were collected. A nomogram on account of lasso regression and multivariate logistic regression, and a RandomForest (RF) model based on significant variables in univariate analyses were established. Receiver operating characteristic (ROC) curve, mean area under the curve (AUC), calibration curve and decision curve analysis (DCA) were carried out to evaluate and compare the diagnostic ability of models. Results The nomogram identified five CT features as independent predictors of HP diagnosis: age, location, LD/SD ratio, duct-like structure, and HU lesion/pancreas A. Five features were included in RF model and ranked according to their relevance to the differential diagnosis: LD/SD ratio, HU lesion/pancreas A, location, peritumoral hypodensity line and age. The nomogram and RF model yielded AUC of 0.951 (95% CI: 0.842–0.993) and 0.894 (95% CI: 0.766–0.966), respectively. The DeLong test found no statistically significant difference in diagnostic performance (p < 0.05), but DCA revealed that the nomogram surpassed the RF model in clinical usefulness. Conclusion Two diagnostic prediction models based on a nomogram as well as RF method were reliable and easy-to-use for distinguishing between HP and GIST, which might also assist treatment planning.
abstract_unstemmed	Abstract Objective To identify CT features and establish a nomogram, compared with a machine learning-based model for distinguishing gastrointestinal heterotopic pancreas (HP) from gastrointestinal stromal tumor (GIST). Materials and methods This retrospective study included 148 patients with pathologically confirmed HP (n = 48) and GIST (n = 100) in the stomach or small intestine that were less than 3 cm in size. Clinical information and CT characteristics were collected. A nomogram on account of lasso regression and multivariate logistic regression, and a RandomForest (RF) model based on significant variables in univariate analyses were established. Receiver operating characteristic (ROC) curve, mean area under the curve (AUC), calibration curve and decision curve analysis (DCA) were carried out to evaluate and compare the diagnostic ability of models. Results The nomogram identified five CT features as independent predictors of HP diagnosis: age, location, LD/SD ratio, duct-like structure, and HU lesion/pancreas A. Five features were included in RF model and ranked according to their relevance to the differential diagnosis: LD/SD ratio, HU lesion/pancreas A, location, peritumoral hypodensity line and age. The nomogram and RF model yielded AUC of 0.951 (95% CI: 0.842–0.993) and 0.894 (95% CI: 0.766–0.966), respectively. The DeLong test found no statistically significant difference in diagnostic performance (p < 0.05), but DCA revealed that the nomogram surpassed the RF model in clinical usefulness. Conclusion Two diagnostic prediction models based on a nomogram as well as RF method were reliable and easy-to-use for distinguishing between HP and GIST, which might also assist treatment planning.
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title_short	A CT-based nomogram established for differentiating gastrointestinal heterotopic pancreas from gastrointestinal stromal tumor: compared with a machine-learning model
url	https://doi.org/10.1186/s12880-023-01094-3 https://doaj.org/article/8ba3c953909d4fbf8be756da56a4dcae https://doaj.org/toc/1471-2342
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up_date	2024-07-03T13:55:40.986Z
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Materials and methods This retrospective study included 148 patients with pathologically confirmed HP (n = 48) and GIST (n = 100) in the stomach or small intestine that were less than 3 cm in size. Clinical information and CT characteristics were collected. A nomogram on account of lasso regression and multivariate logistic regression, and a RandomForest (RF) model based on significant variables in univariate analyses were established. Receiver operating characteristic (ROC) curve, mean area under the curve (AUC), calibration curve and decision curve analysis (DCA) were carried out to evaluate and compare the diagnostic ability of models. Results The nomogram identified five CT features as independent predictors of HP diagnosis: age, location, LD/SD ratio, duct-like structure, and HU lesion/pancreas A. Five features were included in RF model and ranked according to their relevance to the differential diagnosis: LD/SD ratio, HU lesion/pancreas A, location, peritumoral hypodensity line and age. The nomogram and RF model yielded AUC of 0.951 (95% CI: 0.842–0.993) and 0.894 (95% CI: 0.766–0.966), respectively. The DeLong test found no statistically significant difference in diagnostic performance (p < 0.05), but DCA revealed that the nomogram surpassed the RF model in clinical usefulness. Conclusion Two diagnostic prediction models based on a nomogram as well as RF method were reliable and easy-to-use for distinguishing between HP and GIST, which might also assist treatment planning.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Heterotopic pancreas</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Gastrointestinal stromal tumor</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Nomogram</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Random Forest</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computed tomography</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Medical technology</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Hai-Yan Chen</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield 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A CT-based nomogram established for differentiating gastrointestinal heterotopic pancreas from gastrointestinal stromal tumor: compared with a machine-learning model

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