Clinical target volume automatic segmentation based on lymph node stations for lung cancer with bulky lump lymph nodes

Abstract Background The lack of standardized delineation of lymph node station in lung cancer radiotherapy leads to nonstandard clinical target volume (CTV) contouring, especially in patients with bulky lump gross target volume lymph nodes (GTVnd). This study defines lymph node region boundaries in...
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Autor*in:	Jie Shen [verfasserIn] Fuquan Zhang [verfasserIn] Mingyi Di [verfasserIn] Jing Shen [verfasserIn] Shaobin Wang [verfasserIn] Qi Chen [verfasserIn] Yu Chen [verfasserIn] Zhikai Liu [verfasserIn] Xin Lian [verfasserIn] Jiabin Ma [verfasserIn] Tingtian Pang [verfasserIn] Tingting Dong [verfasserIn] Bei Wang [verfasserIn] Qiu Guan [verfasserIn] Lei He [verfasserIn] Yue Zhang [verfasserIn] Hao Liang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	gross target volume lymph nodes (GTVnd) lung cancer lymph node lymph node clinical target volume (CTV)

Übergeordnetes Werk:	In: Thoracic Cancer - Wiley, 2015, 13(2022), 20, Seite 2897-2903
Übergeordnetes Werk:	volume:13 ; year:2022 ; number:20 ; pages:2897-2903

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.1111/1759-7714.14638

Katalog-ID:	DOAJ029404398

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245	1	0	\|a Clinical target volume automatic segmentation based on lymph node stations for lung cancer with bulky lump lymph nodes
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520			\|a Abstract Background The lack of standardized delineation of lymph node station in lung cancer radiotherapy leads to nonstandard clinical target volume (CTV) contouring, especially in patients with bulky lump gross target volume lymph nodes (GTVnd). This study defines lymph node region boundaries in radiotherapy for lung cancer and automatically contours lymph node stations based on the International Association for the Study of Lung Cancer (IASLC) lymph node map. Methods Computed tomography (CT) scans of 200 patients with small cell lung cancer were collected. The lymph node zone boundaries were defined based on the IASLC lymph node map, with adjustments to meet radiotherapy requirements. Contours of lymph node stations were confirmed by two experienced oncologists. A model (DiUNet) was constructed by incorporating the contours of GTVnd to precisely contour the boundaries. Quantitative evaluation metrics and clinical evaluations were conducted. Results The mean 3D Dice similarity coefficient (Dice similarity coefficient) values of DiUNet in most lymph node stations was greater than 0.7, 98.87% of the lymph node station slices are accepted. The mean DiUNet score was not significantly different from that of the man contoured in the evaluation of lymph node stations and CTV. Conclusion This is the first study to propose a method that automatically contours lymph node regions station by station based on the IASLC lymph node map with bulky lump GTVnd. Delineation of lymph node stations based on the DiUNet model is a promising strategy to obtain accuracy and efficiency for CTV delineation in lung cancer patients, especially for bulky lump GTVnd.
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653		0	\|a Neoplasms. Tumors. Oncology. Including cancer and carcinogens
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700	0		\|a Lei He \|e verfasserin \|4 aut
700	0		\|a Yue Zhang \|e verfasserin \|4 aut
700	0		\|a Hao Liang \|e verfasserin \|4 aut
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allfields	10.1111/1759-7714.14638 doi (DE-627)DOAJ029404398 (DE-599)DOAJ726ed4d5e930495bb90e659305e01ff0 DE-627 ger DE-627 rakwb eng RC254-282 Jie Shen verfasserin aut Clinical target volume automatic segmentation based on lymph node stations for lung cancer with bulky lump lymph nodes 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background The lack of standardized delineation of lymph node station in lung cancer radiotherapy leads to nonstandard clinical target volume (CTV) contouring, especially in patients with bulky lump gross target volume lymph nodes (GTVnd). This study defines lymph node region boundaries in radiotherapy for lung cancer and automatically contours lymph node stations based on the International Association for the Study of Lung Cancer (IASLC) lymph node map. Methods Computed tomography (CT) scans of 200 patients with small cell lung cancer were collected. The lymph node zone boundaries were defined based on the IASLC lymph node map, with adjustments to meet radiotherapy requirements. Contours of lymph node stations were confirmed by two experienced oncologists. A model (DiUNet) was constructed by incorporating the contours of GTVnd to precisely contour the boundaries. Quantitative evaluation metrics and clinical evaluations were conducted. Results The mean 3D Dice similarity coefficient (Dice similarity coefficient) values of DiUNet in most lymph node stations was greater than 0.7, 98.87% of the lymph node station slices are accepted. The mean DiUNet score was not significantly different from that of the man contoured in the evaluation of lymph node stations and CTV. Conclusion This is the first study to propose a method that automatically contours lymph node regions station by station based on the IASLC lymph node map with bulky lump GTVnd. Delineation of lymph node stations based on the DiUNet model is a promising strategy to obtain accuracy and efficiency for CTV delineation in lung cancer patients, especially for bulky lump GTVnd. gross target volume lymph nodes (GTVnd) lung cancer lymph node lymph node clinical target volume (CTV) Neoplasms. Tumors. Oncology. Including cancer and carcinogens Fuquan Zhang verfasserin aut Mingyi Di verfasserin aut Jing Shen verfasserin aut Shaobin Wang verfasserin aut Qi Chen verfasserin aut Yu Chen verfasserin aut Zhikai Liu verfasserin aut Xin Lian verfasserin aut Jiabin Ma verfasserin aut Tingtian Pang verfasserin aut Tingting Dong verfasserin aut Bei Wang verfasserin aut Qiu Guan verfasserin aut Lei He verfasserin aut Yue Zhang verfasserin aut Hao Liang verfasserin aut In Thoracic Cancer Wiley, 2015 13(2022), 20, Seite 2897-2903 (DE-627)629836809 (DE-600)2559245-2 17597714 nnns volume:13 year:2022 number:20 pages:2897-2903 https://doi.org/10.1111/1759-7714.14638 kostenfrei https://doaj.org/article/726ed4d5e930495bb90e659305e01ff0 kostenfrei https://doi.org/10.1111/1759-7714.14638 kostenfrei https://doaj.org/toc/1759-7706 Journal toc kostenfrei https://doaj.org/toc/1759-7714 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2022 20 2897-2903
spelling	10.1111/1759-7714.14638 doi (DE-627)DOAJ029404398 (DE-599)DOAJ726ed4d5e930495bb90e659305e01ff0 DE-627 ger DE-627 rakwb eng RC254-282 Jie Shen verfasserin aut Clinical target volume automatic segmentation based on lymph node stations for lung cancer with bulky lump lymph nodes 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background The lack of standardized delineation of lymph node station in lung cancer radiotherapy leads to nonstandard clinical target volume (CTV) contouring, especially in patients with bulky lump gross target volume lymph nodes (GTVnd). This study defines lymph node region boundaries in radiotherapy for lung cancer and automatically contours lymph node stations based on the International Association for the Study of Lung Cancer (IASLC) lymph node map. Methods Computed tomography (CT) scans of 200 patients with small cell lung cancer were collected. The lymph node zone boundaries were defined based on the IASLC lymph node map, with adjustments to meet radiotherapy requirements. Contours of lymph node stations were confirmed by two experienced oncologists. A model (DiUNet) was constructed by incorporating the contours of GTVnd to precisely contour the boundaries. Quantitative evaluation metrics and clinical evaluations were conducted. Results The mean 3D Dice similarity coefficient (Dice similarity coefficient) values of DiUNet in most lymph node stations was greater than 0.7, 98.87% of the lymph node station slices are accepted. The mean DiUNet score was not significantly different from that of the man contoured in the evaluation of lymph node stations and CTV. Conclusion This is the first study to propose a method that automatically contours lymph node regions station by station based on the IASLC lymph node map with bulky lump GTVnd. Delineation of lymph node stations based on the DiUNet model is a promising strategy to obtain accuracy and efficiency for CTV delineation in lung cancer patients, especially for bulky lump GTVnd. gross target volume lymph nodes (GTVnd) lung cancer lymph node lymph node clinical target volume (CTV) Neoplasms. Tumors. Oncology. Including cancer and carcinogens Fuquan Zhang verfasserin aut Mingyi Di verfasserin aut Jing Shen verfasserin aut Shaobin Wang verfasserin aut Qi Chen verfasserin aut Yu Chen verfasserin aut Zhikai Liu verfasserin aut Xin Lian verfasserin aut Jiabin Ma verfasserin aut Tingtian Pang verfasserin aut Tingting Dong verfasserin aut Bei Wang verfasserin aut Qiu Guan verfasserin aut Lei He verfasserin aut Yue Zhang verfasserin aut Hao Liang verfasserin aut In Thoracic Cancer Wiley, 2015 13(2022), 20, Seite 2897-2903 (DE-627)629836809 (DE-600)2559245-2 17597714 nnns volume:13 year:2022 number:20 pages:2897-2903 https://doi.org/10.1111/1759-7714.14638 kostenfrei https://doaj.org/article/726ed4d5e930495bb90e659305e01ff0 kostenfrei https://doi.org/10.1111/1759-7714.14638 kostenfrei https://doaj.org/toc/1759-7706 Journal toc kostenfrei https://doaj.org/toc/1759-7714 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2022 20 2897-2903
allfields_unstemmed	10.1111/1759-7714.14638 doi (DE-627)DOAJ029404398 (DE-599)DOAJ726ed4d5e930495bb90e659305e01ff0 DE-627 ger DE-627 rakwb eng RC254-282 Jie Shen verfasserin aut Clinical target volume automatic segmentation based on lymph node stations for lung cancer with bulky lump lymph nodes 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background The lack of standardized delineation of lymph node station in lung cancer radiotherapy leads to nonstandard clinical target volume (CTV) contouring, especially in patients with bulky lump gross target volume lymph nodes (GTVnd). This study defines lymph node region boundaries in radiotherapy for lung cancer and automatically contours lymph node stations based on the International Association for the Study of Lung Cancer (IASLC) lymph node map. Methods Computed tomography (CT) scans of 200 patients with small cell lung cancer were collected. The lymph node zone boundaries were defined based on the IASLC lymph node map, with adjustments to meet radiotherapy requirements. Contours of lymph node stations were confirmed by two experienced oncologists. A model (DiUNet) was constructed by incorporating the contours of GTVnd to precisely contour the boundaries. Quantitative evaluation metrics and clinical evaluations were conducted. Results The mean 3D Dice similarity coefficient (Dice similarity coefficient) values of DiUNet in most lymph node stations was greater than 0.7, 98.87% of the lymph node station slices are accepted. The mean DiUNet score was not significantly different from that of the man contoured in the evaluation of lymph node stations and CTV. Conclusion This is the first study to propose a method that automatically contours lymph node regions station by station based on the IASLC lymph node map with bulky lump GTVnd. Delineation of lymph node stations based on the DiUNet model is a promising strategy to obtain accuracy and efficiency for CTV delineation in lung cancer patients, especially for bulky lump GTVnd. gross target volume lymph nodes (GTVnd) lung cancer lymph node lymph node clinical target volume (CTV) Neoplasms. Tumors. Oncology. Including cancer and carcinogens Fuquan Zhang verfasserin aut Mingyi Di verfasserin aut Jing Shen verfasserin aut Shaobin Wang verfasserin aut Qi Chen verfasserin aut Yu Chen verfasserin aut Zhikai Liu verfasserin aut Xin Lian verfasserin aut Jiabin Ma verfasserin aut Tingtian Pang verfasserin aut Tingting Dong verfasserin aut Bei Wang verfasserin aut Qiu Guan verfasserin aut Lei He verfasserin aut Yue Zhang verfasserin aut Hao Liang verfasserin aut In Thoracic Cancer Wiley, 2015 13(2022), 20, Seite 2897-2903 (DE-627)629836809 (DE-600)2559245-2 17597714 nnns volume:13 year:2022 number:20 pages:2897-2903 https://doi.org/10.1111/1759-7714.14638 kostenfrei https://doaj.org/article/726ed4d5e930495bb90e659305e01ff0 kostenfrei https://doi.org/10.1111/1759-7714.14638 kostenfrei https://doaj.org/toc/1759-7706 Journal toc kostenfrei https://doaj.org/toc/1759-7714 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2022 20 2897-2903
allfieldsGer	10.1111/1759-7714.14638 doi (DE-627)DOAJ029404398 (DE-599)DOAJ726ed4d5e930495bb90e659305e01ff0 DE-627 ger DE-627 rakwb eng RC254-282 Jie Shen verfasserin aut Clinical target volume automatic segmentation based on lymph node stations for lung cancer with bulky lump lymph nodes 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background The lack of standardized delineation of lymph node station in lung cancer radiotherapy leads to nonstandard clinical target volume (CTV) contouring, especially in patients with bulky lump gross target volume lymph nodes (GTVnd). This study defines lymph node region boundaries in radiotherapy for lung cancer and automatically contours lymph node stations based on the International Association for the Study of Lung Cancer (IASLC) lymph node map. Methods Computed tomography (CT) scans of 200 patients with small cell lung cancer were collected. The lymph node zone boundaries were defined based on the IASLC lymph node map, with adjustments to meet radiotherapy requirements. Contours of lymph node stations were confirmed by two experienced oncologists. A model (DiUNet) was constructed by incorporating the contours of GTVnd to precisely contour the boundaries. Quantitative evaluation metrics and clinical evaluations were conducted. Results The mean 3D Dice similarity coefficient (Dice similarity coefficient) values of DiUNet in most lymph node stations was greater than 0.7, 98.87% of the lymph node station slices are accepted. The mean DiUNet score was not significantly different from that of the man contoured in the evaluation of lymph node stations and CTV. Conclusion This is the first study to propose a method that automatically contours lymph node regions station by station based on the IASLC lymph node map with bulky lump GTVnd. Delineation of lymph node stations based on the DiUNet model is a promising strategy to obtain accuracy and efficiency for CTV delineation in lung cancer patients, especially for bulky lump GTVnd. gross target volume lymph nodes (GTVnd) lung cancer lymph node lymph node clinical target volume (CTV) Neoplasms. Tumors. Oncology. Including cancer and carcinogens Fuquan Zhang verfasserin aut Mingyi Di verfasserin aut Jing Shen verfasserin aut Shaobin Wang verfasserin aut Qi Chen verfasserin aut Yu Chen verfasserin aut Zhikai Liu verfasserin aut Xin Lian verfasserin aut Jiabin Ma verfasserin aut Tingtian Pang verfasserin aut Tingting Dong verfasserin aut Bei Wang verfasserin aut Qiu Guan verfasserin aut Lei He verfasserin aut Yue Zhang verfasserin aut Hao Liang verfasserin aut In Thoracic Cancer Wiley, 2015 13(2022), 20, Seite 2897-2903 (DE-627)629836809 (DE-600)2559245-2 17597714 nnns volume:13 year:2022 number:20 pages:2897-2903 https://doi.org/10.1111/1759-7714.14638 kostenfrei https://doaj.org/article/726ed4d5e930495bb90e659305e01ff0 kostenfrei https://doi.org/10.1111/1759-7714.14638 kostenfrei https://doaj.org/toc/1759-7706 Journal toc kostenfrei https://doaj.org/toc/1759-7714 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2022 20 2897-2903
allfieldsSound	10.1111/1759-7714.14638 doi (DE-627)DOAJ029404398 (DE-599)DOAJ726ed4d5e930495bb90e659305e01ff0 DE-627 ger DE-627 rakwb eng RC254-282 Jie Shen verfasserin aut Clinical target volume automatic segmentation based on lymph node stations for lung cancer with bulky lump lymph nodes 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background The lack of standardized delineation of lymph node station in lung cancer radiotherapy leads to nonstandard clinical target volume (CTV) contouring, especially in patients with bulky lump gross target volume lymph nodes (GTVnd). This study defines lymph node region boundaries in radiotherapy for lung cancer and automatically contours lymph node stations based on the International Association for the Study of Lung Cancer (IASLC) lymph node map. Methods Computed tomography (CT) scans of 200 patients with small cell lung cancer were collected. The lymph node zone boundaries were defined based on the IASLC lymph node map, with adjustments to meet radiotherapy requirements. Contours of lymph node stations were confirmed by two experienced oncologists. A model (DiUNet) was constructed by incorporating the contours of GTVnd to precisely contour the boundaries. Quantitative evaluation metrics and clinical evaluations were conducted. Results The mean 3D Dice similarity coefficient (Dice similarity coefficient) values of DiUNet in most lymph node stations was greater than 0.7, 98.87% of the lymph node station slices are accepted. The mean DiUNet score was not significantly different from that of the man contoured in the evaluation of lymph node stations and CTV. Conclusion This is the first study to propose a method that automatically contours lymph node regions station by station based on the IASLC lymph node map with bulky lump GTVnd. Delineation of lymph node stations based on the DiUNet model is a promising strategy to obtain accuracy and efficiency for CTV delineation in lung cancer patients, especially for bulky lump GTVnd. gross target volume lymph nodes (GTVnd) lung cancer lymph node lymph node clinical target volume (CTV) Neoplasms. Tumors. Oncology. Including cancer and carcinogens Fuquan Zhang verfasserin aut Mingyi Di verfasserin aut Jing Shen verfasserin aut Shaobin Wang verfasserin aut Qi Chen verfasserin aut Yu Chen verfasserin aut Zhikai Liu verfasserin aut Xin Lian verfasserin aut Jiabin Ma verfasserin aut Tingtian Pang verfasserin aut Tingting Dong verfasserin aut Bei Wang verfasserin aut Qiu Guan verfasserin aut Lei He verfasserin aut Yue Zhang verfasserin aut Hao Liang verfasserin aut In Thoracic Cancer Wiley, 2015 13(2022), 20, Seite 2897-2903 (DE-627)629836809 (DE-600)2559245-2 17597714 nnns volume:13 year:2022 number:20 pages:2897-2903 https://doi.org/10.1111/1759-7714.14638 kostenfrei https://doaj.org/article/726ed4d5e930495bb90e659305e01ff0 kostenfrei https://doi.org/10.1111/1759-7714.14638 kostenfrei https://doaj.org/toc/1759-7706 Journal toc kostenfrei https://doaj.org/toc/1759-7714 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2022 20 2897-2903
language	English
source	In Thoracic Cancer 13(2022), 20, Seite 2897-2903 volume:13 year:2022 number:20 pages:2897-2903
sourceStr	In Thoracic Cancer 13(2022), 20, Seite 2897-2903 volume:13 year:2022 number:20 pages:2897-2903
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	gross target volume lymph nodes (GTVnd) lung cancer lymph node lymph node clinical target volume (CTV) Neoplasms. Tumors. Oncology. Including cancer and carcinogens
isfreeaccess_bool	true
container_title	Thoracic Cancer
authorswithroles_txt_mv	Jie Shen @@aut@@ Fuquan Zhang @@aut@@ Mingyi Di @@aut@@ Jing Shen @@aut@@ Shaobin Wang @@aut@@ Qi Chen @@aut@@ Yu Chen @@aut@@ Zhikai Liu @@aut@@ Xin Lian @@aut@@ Jiabin Ma @@aut@@ Tingtian Pang @@aut@@ Tingting Dong @@aut@@ Bei Wang @@aut@@ Qiu Guan @@aut@@ Lei He @@aut@@ Yue Zhang @@aut@@ Hao Liang @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	629836809
id	DOAJ029404398
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ029404398</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230307134859.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230226s2022 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1111/1759-7714.14638</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ029404398</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ726ed4d5e930495bb90e659305e01ff0</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">RC254-282</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Jie Shen</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Clinical target volume automatic segmentation based on lymph node stations for lung cancer with bulky lump lymph nodes</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Background The lack of standardized delineation of lymph node station in lung cancer radiotherapy leads to nonstandard clinical target volume (CTV) contouring, especially in patients with bulky lump gross target volume lymph nodes (GTVnd). This study defines lymph node region boundaries in radiotherapy for lung cancer and automatically contours lymph node stations based on the International Association for the Study of Lung Cancer (IASLC) lymph node map. Methods Computed tomography (CT) scans of 200 patients with small cell lung cancer were collected. The lymph node zone boundaries were defined based on the IASLC lymph node map, with adjustments to meet radiotherapy requirements. Contours of lymph node stations were confirmed by two experienced oncologists. A model (DiUNet) was constructed by incorporating the contours of GTVnd to precisely contour the boundaries. Quantitative evaluation metrics and clinical evaluations were conducted. Results The mean 3D Dice similarity coefficient (Dice similarity coefficient) values of DiUNet in most lymph node stations was greater than 0.7, 98.87% of the lymph node station slices are accepted. The mean DiUNet score was not significantly different from that of the man contoured in the evaluation of lymph node stations and CTV. Conclusion This is the first study to propose a method that automatically contours lymph node regions station by station based on the IASLC lymph node map with bulky lump GTVnd. 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callnumber-first	R - Medicine
author	Jie Shen
spellingShingle	Jie Shen misc RC254-282 misc gross target volume lymph nodes (GTVnd) misc lung cancer misc lymph node misc lymph node clinical target volume (CTV) misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens Clinical target volume automatic segmentation based on lymph node stations for lung cancer with bulky lump lymph nodes
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topic_title	RC254-282 Clinical target volume automatic segmentation based on lymph node stations for lung cancer with bulky lump lymph nodes gross target volume lymph nodes (GTVnd) lung cancer lymph node lymph node clinical target volume (CTV)
topic	misc RC254-282 misc gross target volume lymph nodes (GTVnd) misc lung cancer misc lymph node misc lymph node clinical target volume (CTV) misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
topic_unstemmed	misc RC254-282 misc gross target volume lymph nodes (GTVnd) misc lung cancer misc lymph node misc lymph node clinical target volume (CTV) misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
topic_browse	misc RC254-282 misc gross target volume lymph nodes (GTVnd) misc lung cancer misc lymph node misc lymph node clinical target volume (CTV) misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
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title	Clinical target volume automatic segmentation based on lymph node stations for lung cancer with bulky lump lymph nodes
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title_full	Clinical target volume automatic segmentation based on lymph node stations for lung cancer with bulky lump lymph nodes
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author_browse	Jie Shen Fuquan Zhang Mingyi Di Jing Shen Shaobin Wang Qi Chen Yu Chen Zhikai Liu Xin Lian Jiabin Ma Tingtian Pang Tingting Dong Bei Wang Qiu Guan Lei He Yue Zhang Hao Liang
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title_sort	clinical target volume automatic segmentation based on lymph node stations for lung cancer with bulky lump lymph nodes
callnumber	RC254-282
title_auth	Clinical target volume automatic segmentation based on lymph node stations for lung cancer with bulky lump lymph nodes
abstract	Abstract Background The lack of standardized delineation of lymph node station in lung cancer radiotherapy leads to nonstandard clinical target volume (CTV) contouring, especially in patients with bulky lump gross target volume lymph nodes (GTVnd). This study defines lymph node region boundaries in radiotherapy for lung cancer and automatically contours lymph node stations based on the International Association for the Study of Lung Cancer (IASLC) lymph node map. Methods Computed tomography (CT) scans of 200 patients with small cell lung cancer were collected. The lymph node zone boundaries were defined based on the IASLC lymph node map, with adjustments to meet radiotherapy requirements. Contours of lymph node stations were confirmed by two experienced oncologists. A model (DiUNet) was constructed by incorporating the contours of GTVnd to precisely contour the boundaries. Quantitative evaluation metrics and clinical evaluations were conducted. Results The mean 3D Dice similarity coefficient (Dice similarity coefficient) values of DiUNet in most lymph node stations was greater than 0.7, 98.87% of the lymph node station slices are accepted. The mean DiUNet score was not significantly different from that of the man contoured in the evaluation of lymph node stations and CTV. Conclusion This is the first study to propose a method that automatically contours lymph node regions station by station based on the IASLC lymph node map with bulky lump GTVnd. Delineation of lymph node stations based on the DiUNet model is a promising strategy to obtain accuracy and efficiency for CTV delineation in lung cancer patients, especially for bulky lump GTVnd.
abstractGer	Abstract Background The lack of standardized delineation of lymph node station in lung cancer radiotherapy leads to nonstandard clinical target volume (CTV) contouring, especially in patients with bulky lump gross target volume lymph nodes (GTVnd). This study defines lymph node region boundaries in radiotherapy for lung cancer and automatically contours lymph node stations based on the International Association for the Study of Lung Cancer (IASLC) lymph node map. Methods Computed tomography (CT) scans of 200 patients with small cell lung cancer were collected. The lymph node zone boundaries were defined based on the IASLC lymph node map, with adjustments to meet radiotherapy requirements. Contours of lymph node stations were confirmed by two experienced oncologists. A model (DiUNet) was constructed by incorporating the contours of GTVnd to precisely contour the boundaries. Quantitative evaluation metrics and clinical evaluations were conducted. Results The mean 3D Dice similarity coefficient (Dice similarity coefficient) values of DiUNet in most lymph node stations was greater than 0.7, 98.87% of the lymph node station slices are accepted. The mean DiUNet score was not significantly different from that of the man contoured in the evaluation of lymph node stations and CTV. Conclusion This is the first study to propose a method that automatically contours lymph node regions station by station based on the IASLC lymph node map with bulky lump GTVnd. Delineation of lymph node stations based on the DiUNet model is a promising strategy to obtain accuracy and efficiency for CTV delineation in lung cancer patients, especially for bulky lump GTVnd.
abstract_unstemmed	Abstract Background The lack of standardized delineation of lymph node station in lung cancer radiotherapy leads to nonstandard clinical target volume (CTV) contouring, especially in patients with bulky lump gross target volume lymph nodes (GTVnd). This study defines lymph node region boundaries in radiotherapy for lung cancer and automatically contours lymph node stations based on the International Association for the Study of Lung Cancer (IASLC) lymph node map. Methods Computed tomography (CT) scans of 200 patients with small cell lung cancer were collected. The lymph node zone boundaries were defined based on the IASLC lymph node map, with adjustments to meet radiotherapy requirements. Contours of lymph node stations were confirmed by two experienced oncologists. A model (DiUNet) was constructed by incorporating the contours of GTVnd to precisely contour the boundaries. Quantitative evaluation metrics and clinical evaluations were conducted. Results The mean 3D Dice similarity coefficient (Dice similarity coefficient) values of DiUNet in most lymph node stations was greater than 0.7, 98.87% of the lymph node station slices are accepted. The mean DiUNet score was not significantly different from that of the man contoured in the evaluation of lymph node stations and CTV. Conclusion This is the first study to propose a method that automatically contours lymph node regions station by station based on the IASLC lymph node map with bulky lump GTVnd. Delineation of lymph node stations based on the DiUNet model is a promising strategy to obtain accuracy and efficiency for CTV delineation in lung cancer patients, especially for bulky lump GTVnd.
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title_short	Clinical target volume automatic segmentation based on lymph node stations for lung cancer with bulky lump lymph nodes
url	https://doi.org/10.1111/1759-7714.14638 https://doaj.org/article/726ed4d5e930495bb90e659305e01ff0 https://doaj.org/toc/1759-7706 https://doaj.org/toc/1759-7714
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author2	Fuquan Zhang Mingyi Di Jing Shen Shaobin Wang Qi Chen Yu Chen Zhikai Liu Xin Lian Jiabin Ma Tingtian Pang Tingting Dong Bei Wang Qiu Guan Lei He Yue Zhang Hao Liang
author2Str	Fuquan Zhang Mingyi Di Jing Shen Shaobin Wang Qi Chen Yu Chen Zhikai Liu Xin Lian Jiabin Ma Tingtian Pang Tingting Dong Bei Wang Qiu Guan Lei He Yue Zhang Hao Liang
ppnlink	629836809
callnumber-subject	RC - Internal Medicine
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.1111/1759-7714.14638
callnumber-a	RC254-282
up_date	2024-07-03T22:43:26.289Z
_version_	1803599590239240192
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">DOAJ029404398</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230307134859.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230226s2022 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1111/1759-7714.14638</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ029404398</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ726ed4d5e930495bb90e659305e01ff0</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">RC254-282</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Jie Shen</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Clinical target volume automatic segmentation based on lymph node stations for lung cancer with bulky lump lymph nodes</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Background The lack of standardized delineation of lymph node station in lung cancer radiotherapy leads to nonstandard clinical target volume (CTV) contouring, especially in patients with bulky lump gross target volume lymph nodes (GTVnd). This study defines lymph node region boundaries in radiotherapy for lung cancer and automatically contours lymph node stations based on the International Association for the Study of Lung Cancer (IASLC) lymph node map. Methods Computed tomography (CT) scans of 200 patients with small cell lung cancer were collected. The lymph node zone boundaries were defined based on the IASLC lymph node map, with adjustments to meet radiotherapy requirements. Contours of lymph node stations were confirmed by two experienced oncologists. A model (DiUNet) was constructed by incorporating the contours of GTVnd to precisely contour the boundaries. Quantitative evaluation metrics and clinical evaluations were conducted. Results The mean 3D Dice similarity coefficient (Dice similarity coefficient) values of DiUNet in most lymph node stations was greater than 0.7, 98.87% of the lymph node station slices are accepted. The mean DiUNet score was not significantly different from that of the man contoured in the evaluation of lymph node stations and CTV. Conclusion This is the first study to propose a method that automatically contours lymph node regions station by station based on the IASLC lymph node map with bulky lump GTVnd. 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Clinical target volume automatic segmentation based on lymph node stations for lung cancer with bulky lump lymph nodes

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