The Effect of Artificial Neural Network Model Combined with Six Tumor Markers in Auxiliary Diagnosis of Lung Cancer

Abstract To evaluate the diagnosis potential of artificial neural network (ANN) model combined with six tumor markers in auxiliary diagnosis of lung cancer, to differentiate lung cancer from lung benign disease, normal control, and gastrointestinal cancers. Serum carcino-embryonic antigen (CEA), gas...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Feng, Feifei [verfasserIn] Wu, Yiming [verfasserIn] Wu, Yongjun [verfasserIn] Nie, Guangjin [verfasserIn] Ni, Ran [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2011

Schlagwörter:	Artificial neural network Lung cancer Tumor marker Gastrointestinal cancer

Übergeordnetes Werk:	Enthalten in: Journal of medical systems - New York, NY : Plenum Press, 1977, 36(2011), 5 vom: 01. Sept., Seite 2973-2980
Übergeordnetes Werk:	volume:36 ; year:2011 ; number:5 ; day:01 ; month:09 ; pages:2973-2980

Links:	Volltext

DOI / URN:	10.1007/s10916-011-9775-1

Katalog-ID:	SPR014630028

Internformat


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520			\|a Abstract To evaluate the diagnosis potential of artificial neural network (ANN) model combined with six tumor markers in auxiliary diagnosis of lung cancer, to differentiate lung cancer from lung benign disease, normal control, and gastrointestinal cancers. Serum carcino-embryonic antigen (CEA), gastrin, neurone specific enolase (NSE), sialic acid (SA), Cu/Zn, Ca were measured through different experimental procedures in 117 lung cancer patients, 93 lung benign disease patients, 111 normal control, 47 gastric cancer patients, 50 patients with colon cancer and 50 esophagus cancer patients, 19 parameters of basic information were surveyed among lung cancer, lung benign disease and normal control, then developed and evaluated ANN models to distinguish lung cancer. Using the ANN model with the six serum tumor markers and 19 parameters to distinguish lung cancer from benign lung disease and healthy people, the sensitivity was 98.3%, the specificity was 99.5% and the accuracy was 96.9%. Another three ANN models with the six serum tumor markers were employed to differentiate lung cancer from three gastrointestinal cancers, the sensitivity, specificity and accuracy of distinguishing lung cancer from gastric cancer by the ANN model of lung cancer-gastric cancer were 100%, 83.3% and 93.5%, respectively; The sensitivity, specificity and accuracy of discriminating lung cancer by lung cancer-colon cancer ANN model were 90.0%, 90.0%, and 90.0%; And which were 86.7%, 84.6%, and 86.0%, respectively, by lung cancer-esophagus cancer ANN model. ANN model built with the six serum tumor markers could distinguish lung cancer, not only from lung benign disease and normal people, but also from three common gastrointestinal cancers. And our evidence indicates the ANN model maybe is an excellent and intelligent system to discriminate lung cancer.
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700	1		\|a Ni, Ran \|e verfasserin \|4 aut
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912			\|a GBV_ILN_73
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912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_171
912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_250
912			\|a GBV_ILN_281
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_636
912			\|a GBV_ILN_702
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912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
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912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
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912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2037
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2039
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2057
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2070
912			\|a GBV_ILN_2086
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2093
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2107
912			\|a GBV_ILN_2108
912			\|a GBV_ILN_2110
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2113
912			\|a GBV_ILN_2116
912			\|a GBV_ILN_2118
912			\|a GBV_ILN_2119
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912			\|a GBV_ILN_4112
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912			\|a GBV_ILN_4242
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author_variant	f f ff y w yw y w yw g n gn r n rn
matchkey_str	article:1573689X:2011----::hefcoatfcanuantokoecmiewtsxuomresnui
hierarchy_sort_str	2011
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publishDate	2011
allfields	10.1007/s10916-011-9775-1 doi (DE-627)SPR014630028 (SPR)s10916-011-9775-1-e DE-627 ger DE-627 rakwb eng 610 ASE 44.00 bkl Feng, Feifei verfasserin aut The Effect of Artificial Neural Network Model Combined with Six Tumor Markers in Auxiliary Diagnosis of Lung Cancer 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract To evaluate the diagnosis potential of artificial neural network (ANN) model combined with six tumor markers in auxiliary diagnosis of lung cancer, to differentiate lung cancer from lung benign disease, normal control, and gastrointestinal cancers. Serum carcino-embryonic antigen (CEA), gastrin, neurone specific enolase (NSE), sialic acid (SA), Cu/Zn, Ca were measured through different experimental procedures in 117 lung cancer patients, 93 lung benign disease patients, 111 normal control, 47 gastric cancer patients, 50 patients with colon cancer and 50 esophagus cancer patients, 19 parameters of basic information were surveyed among lung cancer, lung benign disease and normal control, then developed and evaluated ANN models to distinguish lung cancer. Using the ANN model with the six serum tumor markers and 19 parameters to distinguish lung cancer from benign lung disease and healthy people, the sensitivity was 98.3%, the specificity was 99.5% and the accuracy was 96.9%. Another three ANN models with the six serum tumor markers were employed to differentiate lung cancer from three gastrointestinal cancers, the sensitivity, specificity and accuracy of distinguishing lung cancer from gastric cancer by the ANN model of lung cancer-gastric cancer were 100%, 83.3% and 93.5%, respectively; The sensitivity, specificity and accuracy of discriminating lung cancer by lung cancer-colon cancer ANN model were 90.0%, 90.0%, and 90.0%; And which were 86.7%, 84.6%, and 86.0%, respectively, by lung cancer-esophagus cancer ANN model. ANN model built with the six serum tumor markers could distinguish lung cancer, not only from lung benign disease and normal people, but also from three common gastrointestinal cancers. And our evidence indicates the ANN model maybe is an excellent and intelligent system to discriminate lung cancer. Artificial neural network (dpeaa)DE-He213 Lung cancer (dpeaa)DE-He213 Tumor marker (dpeaa)DE-He213 Gastrointestinal cancer (dpeaa)DE-He213 Wu, Yiming verfasserin aut Wu, Yongjun verfasserin aut Nie, Guangjin verfasserin aut Ni, Ran verfasserin aut Enthalten in Journal of medical systems New York, NY : Plenum Press, 1977 36(2011), 5 vom: 01. Sept., Seite 2973-2980 (DE-627)320575543 (DE-600)2017001-4 1573-689X nnns volume:36 year:2011 number:5 day:01 month:09 pages:2973-2980 https://dx.doi.org/10.1007/s10916-011-9775-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2244 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 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_4246 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_4393 GBV_ILN_4700 44.00 ASE AR 36 2011 5 01 09 2973-2980
spelling	10.1007/s10916-011-9775-1 doi (DE-627)SPR014630028 (SPR)s10916-011-9775-1-e DE-627 ger DE-627 rakwb eng 610 ASE 44.00 bkl Feng, Feifei verfasserin aut The Effect of Artificial Neural Network Model Combined with Six Tumor Markers in Auxiliary Diagnosis of Lung Cancer 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract To evaluate the diagnosis potential of artificial neural network (ANN) model combined with six tumor markers in auxiliary diagnosis of lung cancer, to differentiate lung cancer from lung benign disease, normal control, and gastrointestinal cancers. Serum carcino-embryonic antigen (CEA), gastrin, neurone specific enolase (NSE), sialic acid (SA), Cu/Zn, Ca were measured through different experimental procedures in 117 lung cancer patients, 93 lung benign disease patients, 111 normal control, 47 gastric cancer patients, 50 patients with colon cancer and 50 esophagus cancer patients, 19 parameters of basic information were surveyed among lung cancer, lung benign disease and normal control, then developed and evaluated ANN models to distinguish lung cancer. Using the ANN model with the six serum tumor markers and 19 parameters to distinguish lung cancer from benign lung disease and healthy people, the sensitivity was 98.3%, the specificity was 99.5% and the accuracy was 96.9%. Another three ANN models with the six serum tumor markers were employed to differentiate lung cancer from three gastrointestinal cancers, the sensitivity, specificity and accuracy of distinguishing lung cancer from gastric cancer by the ANN model of lung cancer-gastric cancer were 100%, 83.3% and 93.5%, respectively; The sensitivity, specificity and accuracy of discriminating lung cancer by lung cancer-colon cancer ANN model were 90.0%, 90.0%, and 90.0%; And which were 86.7%, 84.6%, and 86.0%, respectively, by lung cancer-esophagus cancer ANN model. ANN model built with the six serum tumor markers could distinguish lung cancer, not only from lung benign disease and normal people, but also from three common gastrointestinal cancers. And our evidence indicates the ANN model maybe is an excellent and intelligent system to discriminate lung cancer. Artificial neural network (dpeaa)DE-He213 Lung cancer (dpeaa)DE-He213 Tumor marker (dpeaa)DE-He213 Gastrointestinal cancer (dpeaa)DE-He213 Wu, Yiming verfasserin aut Wu, Yongjun verfasserin aut Nie, Guangjin verfasserin aut Ni, Ran verfasserin aut Enthalten in Journal of medical systems New York, NY : Plenum Press, 1977 36(2011), 5 vom: 01. Sept., Seite 2973-2980 (DE-627)320575543 (DE-600)2017001-4 1573-689X nnns volume:36 year:2011 number:5 day:01 month:09 pages:2973-2980 https://dx.doi.org/10.1007/s10916-011-9775-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2244 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 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_4246 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_4393 GBV_ILN_4700 44.00 ASE AR 36 2011 5 01 09 2973-2980
allfields_unstemmed	10.1007/s10916-011-9775-1 doi (DE-627)SPR014630028 (SPR)s10916-011-9775-1-e DE-627 ger DE-627 rakwb eng 610 ASE 44.00 bkl Feng, Feifei verfasserin aut The Effect of Artificial Neural Network Model Combined with Six Tumor Markers in Auxiliary Diagnosis of Lung Cancer 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract To evaluate the diagnosis potential of artificial neural network (ANN) model combined with six tumor markers in auxiliary diagnosis of lung cancer, to differentiate lung cancer from lung benign disease, normal control, and gastrointestinal cancers. Serum carcino-embryonic antigen (CEA), gastrin, neurone specific enolase (NSE), sialic acid (SA), Cu/Zn, Ca were measured through different experimental procedures in 117 lung cancer patients, 93 lung benign disease patients, 111 normal control, 47 gastric cancer patients, 50 patients with colon cancer and 50 esophagus cancer patients, 19 parameters of basic information were surveyed among lung cancer, lung benign disease and normal control, then developed and evaluated ANN models to distinguish lung cancer. Using the ANN model with the six serum tumor markers and 19 parameters to distinguish lung cancer from benign lung disease and healthy people, the sensitivity was 98.3%, the specificity was 99.5% and the accuracy was 96.9%. Another three ANN models with the six serum tumor markers were employed to differentiate lung cancer from three gastrointestinal cancers, the sensitivity, specificity and accuracy of distinguishing lung cancer from gastric cancer by the ANN model of lung cancer-gastric cancer were 100%, 83.3% and 93.5%, respectively; The sensitivity, specificity and accuracy of discriminating lung cancer by lung cancer-colon cancer ANN model were 90.0%, 90.0%, and 90.0%; And which were 86.7%, 84.6%, and 86.0%, respectively, by lung cancer-esophagus cancer ANN model. ANN model built with the six serum tumor markers could distinguish lung cancer, not only from lung benign disease and normal people, but also from three common gastrointestinal cancers. And our evidence indicates the ANN model maybe is an excellent and intelligent system to discriminate lung cancer. Artificial neural network (dpeaa)DE-He213 Lung cancer (dpeaa)DE-He213 Tumor marker (dpeaa)DE-He213 Gastrointestinal cancer (dpeaa)DE-He213 Wu, Yiming verfasserin aut Wu, Yongjun verfasserin aut Nie, Guangjin verfasserin aut Ni, Ran verfasserin aut Enthalten in Journal of medical systems New York, NY : Plenum Press, 1977 36(2011), 5 vom: 01. Sept., Seite 2973-2980 (DE-627)320575543 (DE-600)2017001-4 1573-689X nnns volume:36 year:2011 number:5 day:01 month:09 pages:2973-2980 https://dx.doi.org/10.1007/s10916-011-9775-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2244 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 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_4246 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_4393 GBV_ILN_4700 44.00 ASE AR 36 2011 5 01 09 2973-2980
allfieldsGer	10.1007/s10916-011-9775-1 doi (DE-627)SPR014630028 (SPR)s10916-011-9775-1-e DE-627 ger DE-627 rakwb eng 610 ASE 44.00 bkl Feng, Feifei verfasserin aut The Effect of Artificial Neural Network Model Combined with Six Tumor Markers in Auxiliary Diagnosis of Lung Cancer 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract To evaluate the diagnosis potential of artificial neural network (ANN) model combined with six tumor markers in auxiliary diagnosis of lung cancer, to differentiate lung cancer from lung benign disease, normal control, and gastrointestinal cancers. Serum carcino-embryonic antigen (CEA), gastrin, neurone specific enolase (NSE), sialic acid (SA), Cu/Zn, Ca were measured through different experimental procedures in 117 lung cancer patients, 93 lung benign disease patients, 111 normal control, 47 gastric cancer patients, 50 patients with colon cancer and 50 esophagus cancer patients, 19 parameters of basic information were surveyed among lung cancer, lung benign disease and normal control, then developed and evaluated ANN models to distinguish lung cancer. Using the ANN model with the six serum tumor markers and 19 parameters to distinguish lung cancer from benign lung disease and healthy people, the sensitivity was 98.3%, the specificity was 99.5% and the accuracy was 96.9%. Another three ANN models with the six serum tumor markers were employed to differentiate lung cancer from three gastrointestinal cancers, the sensitivity, specificity and accuracy of distinguishing lung cancer from gastric cancer by the ANN model of lung cancer-gastric cancer were 100%, 83.3% and 93.5%, respectively; The sensitivity, specificity and accuracy of discriminating lung cancer by lung cancer-colon cancer ANN model were 90.0%, 90.0%, and 90.0%; And which were 86.7%, 84.6%, and 86.0%, respectively, by lung cancer-esophagus cancer ANN model. ANN model built with the six serum tumor markers could distinguish lung cancer, not only from lung benign disease and normal people, but also from three common gastrointestinal cancers. And our evidence indicates the ANN model maybe is an excellent and intelligent system to discriminate lung cancer. Artificial neural network (dpeaa)DE-He213 Lung cancer (dpeaa)DE-He213 Tumor marker (dpeaa)DE-He213 Gastrointestinal cancer (dpeaa)DE-He213 Wu, Yiming verfasserin aut Wu, Yongjun verfasserin aut Nie, Guangjin verfasserin aut Ni, Ran verfasserin aut Enthalten in Journal of medical systems New York, NY : Plenum Press, 1977 36(2011), 5 vom: 01. Sept., Seite 2973-2980 (DE-627)320575543 (DE-600)2017001-4 1573-689X nnns volume:36 year:2011 number:5 day:01 month:09 pages:2973-2980 https://dx.doi.org/10.1007/s10916-011-9775-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2244 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 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_4246 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_4393 GBV_ILN_4700 44.00 ASE AR 36 2011 5 01 09 2973-2980
allfieldsSound	10.1007/s10916-011-9775-1 doi (DE-627)SPR014630028 (SPR)s10916-011-9775-1-e DE-627 ger DE-627 rakwb eng 610 ASE 44.00 bkl Feng, Feifei verfasserin aut The Effect of Artificial Neural Network Model Combined with Six Tumor Markers in Auxiliary Diagnosis of Lung Cancer 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract To evaluate the diagnosis potential of artificial neural network (ANN) model combined with six tumor markers in auxiliary diagnosis of lung cancer, to differentiate lung cancer from lung benign disease, normal control, and gastrointestinal cancers. Serum carcino-embryonic antigen (CEA), gastrin, neurone specific enolase (NSE), sialic acid (SA), Cu/Zn, Ca were measured through different experimental procedures in 117 lung cancer patients, 93 lung benign disease patients, 111 normal control, 47 gastric cancer patients, 50 patients with colon cancer and 50 esophagus cancer patients, 19 parameters of basic information were surveyed among lung cancer, lung benign disease and normal control, then developed and evaluated ANN models to distinguish lung cancer. Using the ANN model with the six serum tumor markers and 19 parameters to distinguish lung cancer from benign lung disease and healthy people, the sensitivity was 98.3%, the specificity was 99.5% and the accuracy was 96.9%. Another three ANN models with the six serum tumor markers were employed to differentiate lung cancer from three gastrointestinal cancers, the sensitivity, specificity and accuracy of distinguishing lung cancer from gastric cancer by the ANN model of lung cancer-gastric cancer were 100%, 83.3% and 93.5%, respectively; The sensitivity, specificity and accuracy of discriminating lung cancer by lung cancer-colon cancer ANN model were 90.0%, 90.0%, and 90.0%; And which were 86.7%, 84.6%, and 86.0%, respectively, by lung cancer-esophagus cancer ANN model. ANN model built with the six serum tumor markers could distinguish lung cancer, not only from lung benign disease and normal people, but also from three common gastrointestinal cancers. And our evidence indicates the ANN model maybe is an excellent and intelligent system to discriminate lung cancer. Artificial neural network (dpeaa)DE-He213 Lung cancer (dpeaa)DE-He213 Tumor marker (dpeaa)DE-He213 Gastrointestinal cancer (dpeaa)DE-He213 Wu, Yiming verfasserin aut Wu, Yongjun verfasserin aut Nie, Guangjin verfasserin aut Ni, Ran verfasserin aut Enthalten in Journal of medical systems New York, NY : Plenum Press, 1977 36(2011), 5 vom: 01. Sept., Seite 2973-2980 (DE-627)320575543 (DE-600)2017001-4 1573-689X nnns volume:36 year:2011 number:5 day:01 month:09 pages:2973-2980 https://dx.doi.org/10.1007/s10916-011-9775-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2244 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 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_4246 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_4393 GBV_ILN_4700 44.00 ASE AR 36 2011 5 01 09 2973-2980
language	English
source	Enthalten in Journal of medical systems 36(2011), 5 vom: 01. Sept., Seite 2973-2980 volume:36 year:2011 number:5 day:01 month:09 pages:2973-2980
sourceStr	Enthalten in Journal of medical systems 36(2011), 5 vom: 01. Sept., Seite 2973-2980 volume:36 year:2011 number:5 day:01 month:09 pages:2973-2980
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Artificial neural network Lung cancer Tumor marker Gastrointestinal cancer
dewey-raw	610
isfreeaccess_bool	false
container_title	Journal of medical systems
authorswithroles_txt_mv	Feng, Feifei @@aut@@ Wu, Yiming @@aut@@ Wu, Yongjun @@aut@@ Nie, Guangjin @@aut@@ Ni, Ran @@aut@@
publishDateDaySort_date	2011-09-01T00:00:00Z
hierarchy_top_id	320575543
dewey-sort	3610
id	SPR014630028
language_de	englisch
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author	Feng, Feifei
spellingShingle	Feng, Feifei ddc 610 bkl 44.00 misc Artificial neural network misc Lung cancer misc Tumor marker misc Gastrointestinal cancer The Effect of Artificial Neural Network Model Combined with Six Tumor Markers in Auxiliary Diagnosis of Lung Cancer
authorStr	Feng, Feifei
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topic_title	610 ASE 44.00 bkl The Effect of Artificial Neural Network Model Combined with Six Tumor Markers in Auxiliary Diagnosis of Lung Cancer Artificial neural network (dpeaa)DE-He213 Lung cancer (dpeaa)DE-He213 Tumor marker (dpeaa)DE-He213 Gastrointestinal cancer (dpeaa)DE-He213
topic	ddc 610 bkl 44.00 misc Artificial neural network misc Lung cancer misc Tumor marker misc Gastrointestinal cancer
topic_unstemmed	ddc 610 bkl 44.00 misc Artificial neural network misc Lung cancer misc Tumor marker misc Gastrointestinal cancer
topic_browse	ddc 610 bkl 44.00 misc Artificial neural network misc Lung cancer misc Tumor marker misc Gastrointestinal cancer
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title	The Effect of Artificial Neural Network Model Combined with Six Tumor Markers in Auxiliary Diagnosis of Lung Cancer
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title_full	The Effect of Artificial Neural Network Model Combined with Six Tumor Markers in Auxiliary Diagnosis of Lung Cancer
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author_browse	Feng, Feifei Wu, Yiming Wu, Yongjun Nie, Guangjin Ni, Ran
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title_sort	effect of artificial neural network model combined with six tumor markers in auxiliary diagnosis of lung cancer
title_auth	The Effect of Artificial Neural Network Model Combined with Six Tumor Markers in Auxiliary Diagnosis of Lung Cancer
abstract	Abstract To evaluate the diagnosis potential of artificial neural network (ANN) model combined with six tumor markers in auxiliary diagnosis of lung cancer, to differentiate lung cancer from lung benign disease, normal control, and gastrointestinal cancers. Serum carcino-embryonic antigen (CEA), gastrin, neurone specific enolase (NSE), sialic acid (SA), Cu/Zn, Ca were measured through different experimental procedures in 117 lung cancer patients, 93 lung benign disease patients, 111 normal control, 47 gastric cancer patients, 50 patients with colon cancer and 50 esophagus cancer patients, 19 parameters of basic information were surveyed among lung cancer, lung benign disease and normal control, then developed and evaluated ANN models to distinguish lung cancer. Using the ANN model with the six serum tumor markers and 19 parameters to distinguish lung cancer from benign lung disease and healthy people, the sensitivity was 98.3%, the specificity was 99.5% and the accuracy was 96.9%. Another three ANN models with the six serum tumor markers were employed to differentiate lung cancer from three gastrointestinal cancers, the sensitivity, specificity and accuracy of distinguishing lung cancer from gastric cancer by the ANN model of lung cancer-gastric cancer were 100%, 83.3% and 93.5%, respectively; The sensitivity, specificity and accuracy of discriminating lung cancer by lung cancer-colon cancer ANN model were 90.0%, 90.0%, and 90.0%; And which were 86.7%, 84.6%, and 86.0%, respectively, by lung cancer-esophagus cancer ANN model. ANN model built with the six serum tumor markers could distinguish lung cancer, not only from lung benign disease and normal people, but also from three common gastrointestinal cancers. And our evidence indicates the ANN model maybe is an excellent and intelligent system to discriminate lung cancer.
abstractGer	Abstract To evaluate the diagnosis potential of artificial neural network (ANN) model combined with six tumor markers in auxiliary diagnosis of lung cancer, to differentiate lung cancer from lung benign disease, normal control, and gastrointestinal cancers. Serum carcino-embryonic antigen (CEA), gastrin, neurone specific enolase (NSE), sialic acid (SA), Cu/Zn, Ca were measured through different experimental procedures in 117 lung cancer patients, 93 lung benign disease patients, 111 normal control, 47 gastric cancer patients, 50 patients with colon cancer and 50 esophagus cancer patients, 19 parameters of basic information were surveyed among lung cancer, lung benign disease and normal control, then developed and evaluated ANN models to distinguish lung cancer. Using the ANN model with the six serum tumor markers and 19 parameters to distinguish lung cancer from benign lung disease and healthy people, the sensitivity was 98.3%, the specificity was 99.5% and the accuracy was 96.9%. Another three ANN models with the six serum tumor markers were employed to differentiate lung cancer from three gastrointestinal cancers, the sensitivity, specificity and accuracy of distinguishing lung cancer from gastric cancer by the ANN model of lung cancer-gastric cancer were 100%, 83.3% and 93.5%, respectively; The sensitivity, specificity and accuracy of discriminating lung cancer by lung cancer-colon cancer ANN model were 90.0%, 90.0%, and 90.0%; And which were 86.7%, 84.6%, and 86.0%, respectively, by lung cancer-esophagus cancer ANN model. ANN model built with the six serum tumor markers could distinguish lung cancer, not only from lung benign disease and normal people, but also from three common gastrointestinal cancers. And our evidence indicates the ANN model maybe is an excellent and intelligent system to discriminate lung cancer.
abstract_unstemmed	Abstract To evaluate the diagnosis potential of artificial neural network (ANN) model combined with six tumor markers in auxiliary diagnosis of lung cancer, to differentiate lung cancer from lung benign disease, normal control, and gastrointestinal cancers. Serum carcino-embryonic antigen (CEA), gastrin, neurone specific enolase (NSE), sialic acid (SA), Cu/Zn, Ca were measured through different experimental procedures in 117 lung cancer patients, 93 lung benign disease patients, 111 normal control, 47 gastric cancer patients, 50 patients with colon cancer and 50 esophagus cancer patients, 19 parameters of basic information were surveyed among lung cancer, lung benign disease and normal control, then developed and evaluated ANN models to distinguish lung cancer. Using the ANN model with the six serum tumor markers and 19 parameters to distinguish lung cancer from benign lung disease and healthy people, the sensitivity was 98.3%, the specificity was 99.5% and the accuracy was 96.9%. Another three ANN models with the six serum tumor markers were employed to differentiate lung cancer from three gastrointestinal cancers, the sensitivity, specificity and accuracy of distinguishing lung cancer from gastric cancer by the ANN model of lung cancer-gastric cancer were 100%, 83.3% and 93.5%, respectively; The sensitivity, specificity and accuracy of discriminating lung cancer by lung cancer-colon cancer ANN model were 90.0%, 90.0%, and 90.0%; And which were 86.7%, 84.6%, and 86.0%, respectively, by lung cancer-esophagus cancer ANN model. ANN model built with the six serum tumor markers could distinguish lung cancer, not only from lung benign disease and normal people, but also from three common gastrointestinal cancers. And our evidence indicates the ANN model maybe is an excellent and intelligent system to discriminate lung cancer.
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container_issue	5
title_short	The Effect of Artificial Neural Network Model Combined with Six Tumor Markers in Auxiliary Diagnosis of Lung Cancer
url	https://dx.doi.org/10.1007/s10916-011-9775-1
remote_bool	true
author2	Wu, Yiming Wu, Yongjun Nie, Guangjin Ni, Ran
author2Str	Wu, Yiming Wu, Yongjun Nie, Guangjin Ni, Ran
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doi_str	10.1007/s10916-011-9775-1
up_date	2024-07-04T02:30:17.237Z
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