Predictive modelling for high-risk stage II colon cancer using auto-artificial intelligence
Background Recently, stratification of high-risk stage II colon cancer (CC) and the need for adjuvant chemotherapy have been the focus of attention. The aim of this retrospective study was to define high-risk factors for recurrent stage II CC using Prediction One auto-artificial intelligence (AI) so...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ishizaki, Tetsuo [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Anmerkung: |
© Springer Nature Switzerland AG 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Techniques in coloproctology - Milano : Springer Italia, 1999, 27(2022), 3 vom: 28. Aug., Seite 183-188 |
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| Übergeordnetes Werk: |
volume:27 ; year:2022 ; number:3 ; day:28 ; month:08 ; pages:183-188 |
| Links: |
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| DOI / URN: |
10.1007/s10151-022-02685-y |
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| Katalog-ID: |
SPR049238043 |
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| 520 | |a Background Recently, stratification of high-risk stage II colon cancer (CC) and the need for adjuvant chemotherapy have been the focus of attention. The aim of this retrospective study was to define high-risk factors for recurrent stage II CC using Prediction One auto-artificial intelligence (AI) software and develop a new predictive model for high-risk stage II CC. Methods The study included 259 consecutive pathological stage II CC patients undergoing curative resection at our institution between January 2000 and December 2016. Prediction One software with five-fold cross-validation was used to create a predictive model and receiver operating characteristic (ROC) curve. Predictive accuracy of AI was evaluated using the area under the ROC curve (AUC). We also evaluated the importance of variables (IOV) using a method based on permutation feature importance (IOV > 0.01 defined high-risk factors) to evaluate disease-free survival (DFS). Results The median observation period was 6.1 (range = 0.3–15.8) years. Thirty-seven patients had recurrence (14.3%); the AUC of the AI model was 0.775. Preoperative carcinoembryonic antigen > 5.0 ng/mL (IOV = 0.047), venous invasion (IOV = 0.014), and obstruction (IOV = 0.012) were high-risk factors contributing to cancer recurrence. Patients with 2–3 high-risk factors had lower 5-year DFS than those with 0–1 factor (87.4% vs 62.7%, p < 0.001). Conclusions We developed a new predictive model that could predict recurrent high-risk stage II CC with high probability using auto-AI Prediction One software. Patients with ≥ 2 of the aforementioned factors are considered to have high risks for recurrent stage II CC and may benefit from adjuvant chemotherapy. | ||
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| 650 | 4 | |a Stage II colon cancer |7 (dpeaa)DE-He213 | |
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| 650 | 4 | |a Auto-artificial intelligence |7 (dpeaa)DE-He213 | |
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| 700 | 1 | |a Mazaki, Junichi |4 aut | |
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| 700 | 1 | |a Tago, Tomoya |4 aut | |
| 700 | 1 | |a Kasahara, Kenta |4 aut | |
| 700 | 1 | |a Nagakawa, Yuichi |4 aut | |
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10.1007/s10151-022-02685-y doi (DE-627)SPR049238043 (SPR)s10151-022-02685-y-e DE-627 ger DE-627 rakwb eng Ishizaki, Tetsuo verfasserin (orcid)0000-0001-8375-3849 aut Predictive modelling for high-risk stage II colon cancer using auto-artificial intelligence 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Nature Switzerland AG 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Background Recently, stratification of high-risk stage II colon cancer (CC) and the need for adjuvant chemotherapy have been the focus of attention. The aim of this retrospective study was to define high-risk factors for recurrent stage II CC using Prediction One auto-artificial intelligence (AI) software and develop a new predictive model for high-risk stage II CC. Methods The study included 259 consecutive pathological stage II CC patients undergoing curative resection at our institution between January 2000 and December 2016. Prediction One software with five-fold cross-validation was used to create a predictive model and receiver operating characteristic (ROC) curve. Predictive accuracy of AI was evaluated using the area under the ROC curve (AUC). We also evaluated the importance of variables (IOV) using a method based on permutation feature importance (IOV > 0.01 defined high-risk factors) to evaluate disease-free survival (DFS). Results The median observation period was 6.1 (range = 0.3–15.8) years. Thirty-seven patients had recurrence (14.3%); the AUC of the AI model was 0.775. Preoperative carcinoembryonic antigen > 5.0 ng/mL (IOV = 0.047), venous invasion (IOV = 0.014), and obstruction (IOV = 0.012) were high-risk factors contributing to cancer recurrence. Patients with 2–3 high-risk factors had lower 5-year DFS than those with 0–1 factor (87.4% vs 62.7%, p < 0.001). Conclusions We developed a new predictive model that could predict recurrent high-risk stage II CC with high probability using auto-AI Prediction One software. Patients with ≥ 2 of the aforementioned factors are considered to have high risks for recurrent stage II CC and may benefit from adjuvant chemotherapy. Predictive modelling (dpeaa)DE-He213 Stage II colon cancer (dpeaa)DE-He213 High-risk (dpeaa)DE-He213 Auto-artificial intelligence (dpeaa)DE-He213 Prediction (dpeaa)DE-He213 Adjuvant chemotherapy (dpeaa)DE-He213 Mazaki, Junichi aut Enomoto, Masanobu aut Udo, Ryutaro aut Tago, Tomoya aut Kasahara, Kenta aut Nagakawa, Yuichi aut Enthalten in Techniques in coloproctology Milano : Springer Italia, 1999 27(2022), 3 vom: 28. Aug., Seite 183-188 (DE-627)312410735 (DE-600)2011444-8 1128-045X nnns volume:27 year:2022 number:3 day:28 month:08 pages:183-188 https://dx.doi.org/10.1007/s10151-022-02685-y 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_65 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_152 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_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 27 2022 3 28 08 183-188 |
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10.1007/s10151-022-02685-y doi (DE-627)SPR049238043 (SPR)s10151-022-02685-y-e DE-627 ger DE-627 rakwb eng Ishizaki, Tetsuo verfasserin (orcid)0000-0001-8375-3849 aut Predictive modelling for high-risk stage II colon cancer using auto-artificial intelligence 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Nature Switzerland AG 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Background Recently, stratification of high-risk stage II colon cancer (CC) and the need for adjuvant chemotherapy have been the focus of attention. The aim of this retrospective study was to define high-risk factors for recurrent stage II CC using Prediction One auto-artificial intelligence (AI) software and develop a new predictive model for high-risk stage II CC. Methods The study included 259 consecutive pathological stage II CC patients undergoing curative resection at our institution between January 2000 and December 2016. Prediction One software with five-fold cross-validation was used to create a predictive model and receiver operating characteristic (ROC) curve. Predictive accuracy of AI was evaluated using the area under the ROC curve (AUC). We also evaluated the importance of variables (IOV) using a method based on permutation feature importance (IOV > 0.01 defined high-risk factors) to evaluate disease-free survival (DFS). Results The median observation period was 6.1 (range = 0.3–15.8) years. Thirty-seven patients had recurrence (14.3%); the AUC of the AI model was 0.775. Preoperative carcinoembryonic antigen > 5.0 ng/mL (IOV = 0.047), venous invasion (IOV = 0.014), and obstruction (IOV = 0.012) were high-risk factors contributing to cancer recurrence. Patients with 2–3 high-risk factors had lower 5-year DFS than those with 0–1 factor (87.4% vs 62.7%, p < 0.001). Conclusions We developed a new predictive model that could predict recurrent high-risk stage II CC with high probability using auto-AI Prediction One software. Patients with ≥ 2 of the aforementioned factors are considered to have high risks for recurrent stage II CC and may benefit from adjuvant chemotherapy. Predictive modelling (dpeaa)DE-He213 Stage II colon cancer (dpeaa)DE-He213 High-risk (dpeaa)DE-He213 Auto-artificial intelligence (dpeaa)DE-He213 Prediction (dpeaa)DE-He213 Adjuvant chemotherapy (dpeaa)DE-He213 Mazaki, Junichi aut Enomoto, Masanobu aut Udo, Ryutaro aut Tago, Tomoya aut Kasahara, Kenta aut Nagakawa, Yuichi aut Enthalten in Techniques in coloproctology Milano : Springer Italia, 1999 27(2022), 3 vom: 28. Aug., Seite 183-188 (DE-627)312410735 (DE-600)2011444-8 1128-045X nnns volume:27 year:2022 number:3 day:28 month:08 pages:183-188 https://dx.doi.org/10.1007/s10151-022-02685-y 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_65 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_152 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_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 27 2022 3 28 08 183-188 |
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10.1007/s10151-022-02685-y doi (DE-627)SPR049238043 (SPR)s10151-022-02685-y-e DE-627 ger DE-627 rakwb eng Ishizaki, Tetsuo verfasserin (orcid)0000-0001-8375-3849 aut Predictive modelling for high-risk stage II colon cancer using auto-artificial intelligence 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Nature Switzerland AG 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Background Recently, stratification of high-risk stage II colon cancer (CC) and the need for adjuvant chemotherapy have been the focus of attention. The aim of this retrospective study was to define high-risk factors for recurrent stage II CC using Prediction One auto-artificial intelligence (AI) software and develop a new predictive model for high-risk stage II CC. Methods The study included 259 consecutive pathological stage II CC patients undergoing curative resection at our institution between January 2000 and December 2016. Prediction One software with five-fold cross-validation was used to create a predictive model and receiver operating characteristic (ROC) curve. Predictive accuracy of AI was evaluated using the area under the ROC curve (AUC). We also evaluated the importance of variables (IOV) using a method based on permutation feature importance (IOV > 0.01 defined high-risk factors) to evaluate disease-free survival (DFS). Results The median observation period was 6.1 (range = 0.3–15.8) years. Thirty-seven patients had recurrence (14.3%); the AUC of the AI model was 0.775. Preoperative carcinoembryonic antigen > 5.0 ng/mL (IOV = 0.047), venous invasion (IOV = 0.014), and obstruction (IOV = 0.012) were high-risk factors contributing to cancer recurrence. Patients with 2–3 high-risk factors had lower 5-year DFS than those with 0–1 factor (87.4% vs 62.7%, p < 0.001). Conclusions We developed a new predictive model that could predict recurrent high-risk stage II CC with high probability using auto-AI Prediction One software. Patients with ≥ 2 of the aforementioned factors are considered to have high risks for recurrent stage II CC and may benefit from adjuvant chemotherapy. Predictive modelling (dpeaa)DE-He213 Stage II colon cancer (dpeaa)DE-He213 High-risk (dpeaa)DE-He213 Auto-artificial intelligence (dpeaa)DE-He213 Prediction (dpeaa)DE-He213 Adjuvant chemotherapy (dpeaa)DE-He213 Mazaki, Junichi aut Enomoto, Masanobu aut Udo, Ryutaro aut Tago, Tomoya aut Kasahara, Kenta aut Nagakawa, Yuichi aut Enthalten in Techniques in coloproctology Milano : Springer Italia, 1999 27(2022), 3 vom: 28. Aug., Seite 183-188 (DE-627)312410735 (DE-600)2011444-8 1128-045X nnns volume:27 year:2022 number:3 day:28 month:08 pages:183-188 https://dx.doi.org/10.1007/s10151-022-02685-y 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_65 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_152 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_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 27 2022 3 28 08 183-188 |
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10.1007/s10151-022-02685-y doi (DE-627)SPR049238043 (SPR)s10151-022-02685-y-e DE-627 ger DE-627 rakwb eng Ishizaki, Tetsuo verfasserin (orcid)0000-0001-8375-3849 aut Predictive modelling for high-risk stage II colon cancer using auto-artificial intelligence 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Nature Switzerland AG 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Background Recently, stratification of high-risk stage II colon cancer (CC) and the need for adjuvant chemotherapy have been the focus of attention. The aim of this retrospective study was to define high-risk factors for recurrent stage II CC using Prediction One auto-artificial intelligence (AI) software and develop a new predictive model for high-risk stage II CC. Methods The study included 259 consecutive pathological stage II CC patients undergoing curative resection at our institution between January 2000 and December 2016. Prediction One software with five-fold cross-validation was used to create a predictive model and receiver operating characteristic (ROC) curve. Predictive accuracy of AI was evaluated using the area under the ROC curve (AUC). We also evaluated the importance of variables (IOV) using a method based on permutation feature importance (IOV > 0.01 defined high-risk factors) to evaluate disease-free survival (DFS). Results The median observation period was 6.1 (range = 0.3–15.8) years. Thirty-seven patients had recurrence (14.3%); the AUC of the AI model was 0.775. Preoperative carcinoembryonic antigen > 5.0 ng/mL (IOV = 0.047), venous invasion (IOV = 0.014), and obstruction (IOV = 0.012) were high-risk factors contributing to cancer recurrence. Patients with 2–3 high-risk factors had lower 5-year DFS than those with 0–1 factor (87.4% vs 62.7%, p < 0.001). Conclusions We developed a new predictive model that could predict recurrent high-risk stage II CC with high probability using auto-AI Prediction One software. Patients with ≥ 2 of the aforementioned factors are considered to have high risks for recurrent stage II CC and may benefit from adjuvant chemotherapy. Predictive modelling (dpeaa)DE-He213 Stage II colon cancer (dpeaa)DE-He213 High-risk (dpeaa)DE-He213 Auto-artificial intelligence (dpeaa)DE-He213 Prediction (dpeaa)DE-He213 Adjuvant chemotherapy (dpeaa)DE-He213 Mazaki, Junichi aut Enomoto, Masanobu aut Udo, Ryutaro aut Tago, Tomoya aut Kasahara, Kenta aut Nagakawa, Yuichi aut Enthalten in Techniques in coloproctology Milano : Springer Italia, 1999 27(2022), 3 vom: 28. Aug., Seite 183-188 (DE-627)312410735 (DE-600)2011444-8 1128-045X nnns volume:27 year:2022 number:3 day:28 month:08 pages:183-188 https://dx.doi.org/10.1007/s10151-022-02685-y 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_65 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_152 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_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 27 2022 3 28 08 183-188 |
| allfieldsSound |
10.1007/s10151-022-02685-y doi (DE-627)SPR049238043 (SPR)s10151-022-02685-y-e DE-627 ger DE-627 rakwb eng Ishizaki, Tetsuo verfasserin (orcid)0000-0001-8375-3849 aut Predictive modelling for high-risk stage II colon cancer using auto-artificial intelligence 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Nature Switzerland AG 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Background Recently, stratification of high-risk stage II colon cancer (CC) and the need for adjuvant chemotherapy have been the focus of attention. The aim of this retrospective study was to define high-risk factors for recurrent stage II CC using Prediction One auto-artificial intelligence (AI) software and develop a new predictive model for high-risk stage II CC. Methods The study included 259 consecutive pathological stage II CC patients undergoing curative resection at our institution between January 2000 and December 2016. Prediction One software with five-fold cross-validation was used to create a predictive model and receiver operating characteristic (ROC) curve. Predictive accuracy of AI was evaluated using the area under the ROC curve (AUC). We also evaluated the importance of variables (IOV) using a method based on permutation feature importance (IOV > 0.01 defined high-risk factors) to evaluate disease-free survival (DFS). Results The median observation period was 6.1 (range = 0.3–15.8) years. Thirty-seven patients had recurrence (14.3%); the AUC of the AI model was 0.775. Preoperative carcinoembryonic antigen > 5.0 ng/mL (IOV = 0.047), venous invasion (IOV = 0.014), and obstruction (IOV = 0.012) were high-risk factors contributing to cancer recurrence. Patients with 2–3 high-risk factors had lower 5-year DFS than those with 0–1 factor (87.4% vs 62.7%, p < 0.001). Conclusions We developed a new predictive model that could predict recurrent high-risk stage II CC with high probability using auto-AI Prediction One software. Patients with ≥ 2 of the aforementioned factors are considered to have high risks for recurrent stage II CC and may benefit from adjuvant chemotherapy. Predictive modelling (dpeaa)DE-He213 Stage II colon cancer (dpeaa)DE-He213 High-risk (dpeaa)DE-He213 Auto-artificial intelligence (dpeaa)DE-He213 Prediction (dpeaa)DE-He213 Adjuvant chemotherapy (dpeaa)DE-He213 Mazaki, Junichi aut Enomoto, Masanobu aut Udo, Ryutaro aut Tago, Tomoya aut Kasahara, Kenta aut Nagakawa, Yuichi aut Enthalten in Techniques in coloproctology Milano : Springer Italia, 1999 27(2022), 3 vom: 28. Aug., Seite 183-188 (DE-627)312410735 (DE-600)2011444-8 1128-045X nnns volume:27 year:2022 number:3 day:28 month:08 pages:183-188 https://dx.doi.org/10.1007/s10151-022-02685-y 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_65 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_152 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_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 27 2022 3 28 08 183-188 |
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Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Background Recently, stratification of high-risk stage II colon cancer (CC) and the need for adjuvant chemotherapy have been the focus of attention. The aim of this retrospective study was to define high-risk factors for recurrent stage II CC using Prediction One auto-artificial intelligence (AI) software and develop a new predictive model for high-risk stage II CC. Methods The study included 259 consecutive pathological stage II CC patients undergoing curative resection at our institution between January 2000 and December 2016. Prediction One software with five-fold cross-validation was used to create a predictive model and receiver operating characteristic (ROC) curve. Predictive accuracy of AI was evaluated using the area under the ROC curve (AUC). We also evaluated the importance of variables (IOV) using a method based on permutation feature importance (IOV > 0.01 defined high-risk factors) to evaluate disease-free survival (DFS). Results The median observation period was 6.1 (range = 0.3–15.8) years. Thirty-seven patients had recurrence (14.3%); the AUC of the AI model was 0.775. Preoperative carcinoembryonic antigen > 5.0 ng/mL (IOV = 0.047), venous invasion (IOV = 0.014), and obstruction (IOV = 0.012) were high-risk factors contributing to cancer recurrence. Patients with 2–3 high-risk factors had lower 5-year DFS than those with 0–1 factor (87.4% vs 62.7%, p < 0.001). Conclusions We developed a new predictive model that could predict recurrent high-risk stage II CC with high probability using auto-AI Prediction One software. 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Ishizaki, Tetsuo |
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Ishizaki, Tetsuo misc Predictive modelling misc Stage II colon cancer misc High-risk misc Auto-artificial intelligence misc Prediction misc Adjuvant chemotherapy Predictive modelling for high-risk stage II colon cancer using auto-artificial intelligence |
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Predictive modelling for high-risk stage II colon cancer using auto-artificial intelligence Predictive modelling (dpeaa)DE-He213 Stage II colon cancer (dpeaa)DE-He213 High-risk (dpeaa)DE-He213 Auto-artificial intelligence (dpeaa)DE-He213 Prediction (dpeaa)DE-He213 Adjuvant chemotherapy (dpeaa)DE-He213 |
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predictive modelling for high-risk stage ii colon cancer using auto-artificial intelligence |
| title_auth |
Predictive modelling for high-risk stage II colon cancer using auto-artificial intelligence |
| abstract |
Background Recently, stratification of high-risk stage II colon cancer (CC) and the need for adjuvant chemotherapy have been the focus of attention. The aim of this retrospective study was to define high-risk factors for recurrent stage II CC using Prediction One auto-artificial intelligence (AI) software and develop a new predictive model for high-risk stage II CC. Methods The study included 259 consecutive pathological stage II CC patients undergoing curative resection at our institution between January 2000 and December 2016. Prediction One software with five-fold cross-validation was used to create a predictive model and receiver operating characteristic (ROC) curve. Predictive accuracy of AI was evaluated using the area under the ROC curve (AUC). We also evaluated the importance of variables (IOV) using a method based on permutation feature importance (IOV > 0.01 defined high-risk factors) to evaluate disease-free survival (DFS). Results The median observation period was 6.1 (range = 0.3–15.8) years. Thirty-seven patients had recurrence (14.3%); the AUC of the AI model was 0.775. Preoperative carcinoembryonic antigen > 5.0 ng/mL (IOV = 0.047), venous invasion (IOV = 0.014), and obstruction (IOV = 0.012) were high-risk factors contributing to cancer recurrence. Patients with 2–3 high-risk factors had lower 5-year DFS than those with 0–1 factor (87.4% vs 62.7%, p < 0.001). Conclusions We developed a new predictive model that could predict recurrent high-risk stage II CC with high probability using auto-AI Prediction One software. Patients with ≥ 2 of the aforementioned factors are considered to have high risks for recurrent stage II CC and may benefit from adjuvant chemotherapy. © Springer Nature Switzerland AG 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Background Recently, stratification of high-risk stage II colon cancer (CC) and the need for adjuvant chemotherapy have been the focus of attention. The aim of this retrospective study was to define high-risk factors for recurrent stage II CC using Prediction One auto-artificial intelligence (AI) software and develop a new predictive model for high-risk stage II CC. Methods The study included 259 consecutive pathological stage II CC patients undergoing curative resection at our institution between January 2000 and December 2016. Prediction One software with five-fold cross-validation was used to create a predictive model and receiver operating characteristic (ROC) curve. Predictive accuracy of AI was evaluated using the area under the ROC curve (AUC). We also evaluated the importance of variables (IOV) using a method based on permutation feature importance (IOV > 0.01 defined high-risk factors) to evaluate disease-free survival (DFS). Results The median observation period was 6.1 (range = 0.3–15.8) years. Thirty-seven patients had recurrence (14.3%); the AUC of the AI model was 0.775. Preoperative carcinoembryonic antigen > 5.0 ng/mL (IOV = 0.047), venous invasion (IOV = 0.014), and obstruction (IOV = 0.012) were high-risk factors contributing to cancer recurrence. Patients with 2–3 high-risk factors had lower 5-year DFS than those with 0–1 factor (87.4% vs 62.7%, p < 0.001). Conclusions We developed a new predictive model that could predict recurrent high-risk stage II CC with high probability using auto-AI Prediction One software. Patients with ≥ 2 of the aforementioned factors are considered to have high risks for recurrent stage II CC and may benefit from adjuvant chemotherapy. © Springer Nature Switzerland AG 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Background Recently, stratification of high-risk stage II colon cancer (CC) and the need for adjuvant chemotherapy have been the focus of attention. The aim of this retrospective study was to define high-risk factors for recurrent stage II CC using Prediction One auto-artificial intelligence (AI) software and develop a new predictive model for high-risk stage II CC. Methods The study included 259 consecutive pathological stage II CC patients undergoing curative resection at our institution between January 2000 and December 2016. Prediction One software with five-fold cross-validation was used to create a predictive model and receiver operating characteristic (ROC) curve. Predictive accuracy of AI was evaluated using the area under the ROC curve (AUC). We also evaluated the importance of variables (IOV) using a method based on permutation feature importance (IOV > 0.01 defined high-risk factors) to evaluate disease-free survival (DFS). Results The median observation period was 6.1 (range = 0.3–15.8) years. Thirty-seven patients had recurrence (14.3%); the AUC of the AI model was 0.775. Preoperative carcinoembryonic antigen > 5.0 ng/mL (IOV = 0.047), venous invasion (IOV = 0.014), and obstruction (IOV = 0.012) were high-risk factors contributing to cancer recurrence. Patients with 2–3 high-risk factors had lower 5-year DFS than those with 0–1 factor (87.4% vs 62.7%, p < 0.001). Conclusions We developed a new predictive model that could predict recurrent high-risk stage II CC with high probability using auto-AI Prediction One software. Patients with ≥ 2 of the aforementioned factors are considered to have high risks for recurrent stage II CC and may benefit from adjuvant chemotherapy. © Springer Nature Switzerland AG 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Predictive modelling for high-risk stage II colon cancer using auto-artificial intelligence |
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Mazaki, Junichi Enomoto, Masanobu Udo, Ryutaro Tago, Tomoya Kasahara, Kenta Nagakawa, Yuichi |
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| score |
7.401045 |