Predictive modeling for determination of microscopic residual disease at primary cytoreduction: An NRG Oncology/Gynecologic Oncology Group 182 Study
Objective: Microscopic residual disease following complete cytoreduction (R0) is associated with a significant survival benefit for patients with advanced epithelial ovarian cancer (EOC). Our objective was to develop a prediction model for R0 to support surgeons in their clinical care decisions.Meth...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Horowitz, Neil S. [verfasserIn] Larry Maxwell, G. [verfasserIn] Miller, Austin [verfasserIn] Hamilton, Chad A. [verfasserIn] Rungruang, Bunja [verfasserIn] Rodriguez, Noah [verfasserIn] Richard, Scott D. [verfasserIn] Krivak, Thomas C. [verfasserIn] Fowler, Jeffrey M. [verfasserIn] Mutch, David G. [verfasserIn] Van Le, Linda [verfasserIn] Lee, Roger B. [verfasserIn] Argenta, Peter [verfasserIn] Bender, David [verfasserIn] Tewari, Krishnansu S. [verfasserIn] Gershenson, David [verfasserIn] Java, James J. [verfasserIn] Bookman, Michael A. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Gynecologic oncology - Orlando, Fla. : Academic Press, 1972, 148 |
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| Übergeordnetes Werk: |
volume:148 |
| DOI / URN: |
10.1016/j.ygyno.2017.10.011 |
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| Katalog-ID: |
ELV00070783X |
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| 024 | 7 | |a 10.1016/j.ygyno.2017.10.011 |2 doi | |
| 035 | |a (DE-627)ELV00070783X | ||
| 035 | |a (ELSEVIER)S0090-8258(17)31371-9 | ||
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| 100 | 1 | |a Horowitz, Neil S. |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Predictive modeling for determination of microscopic residual disease at primary cytoreduction: An NRG Oncology/Gynecologic Oncology Group 182 Study |
| 264 | 1 | |c 2017 | |
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 520 | |a Objective: Microscopic residual disease following complete cytoreduction (R0) is associated with a significant survival benefit for patients with advanced epithelial ovarian cancer (EOC). Our objective was to develop a prediction model for R0 to support surgeons in their clinical care decisions.Methods: Demographic, pathologic, surgical, and CA125 data were collected from GOG 182 records. Patients enrolled prior to September 1, 2003 were used for the training model while those enrolled after constituted the validation data set. Univariate analysis was performed to identify significant predictors of R0 and these variables were subsequently analyzed using multivariable regression. The regression model was reduced using backward selection and predictive accuracy was quantified using area under the receiver operating characteristic area under the curve (AUC) in both the training and the validation data sets.Results: Of the 3882 patients enrolled in GOG 182, 1480 had complete clinical data available for the analysis. The training data set consisted of 1007 patients (234 with R0) while the validation set was comprised of 473 patients (122 with R0). The reduced multivariable regression model demonstrated several variables predictive of R0 at cytoreduction: Disease Score (DS) (p <0.001), stage (p =0.009), CA125 (p <0.001), ascites (p <0.001), and stage-age interaction (p =0.01). Applying the prediction model to the validation data resulted in an AUC of 0.73 (0.67 to 0.78, 95% CI). Inclusion of DS enhanced the model performance to an AUC of 0.83 (0.79 to 0.88, 95% CI).Conclusions: We developed and validated a prediction model for R0 that offers improved performance over previously reported models for prediction of residual disease. The performance of the prediction model suggests additional factors (i.e. imaging, molecular profiling, etc.) should be explored in the future for a more clinically actionable tool. | ||
| 650 | 4 | |a Ovarian cancer | |
| 650 | 4 | |a Microscopic residual | |
| 700 | 1 | |a Larry Maxwell, G. |e verfasserin |4 aut | |
| 700 | 1 | |a Miller, Austin |e verfasserin |4 aut | |
| 700 | 1 | |a Hamilton, Chad A. |e verfasserin |4 aut | |
| 700 | 1 | |a Rungruang, Bunja |e verfasserin |4 aut | |
| 700 | 1 | |a Rodriguez, Noah |e verfasserin |4 aut | |
| 700 | 1 | |a Richard, Scott D. |e verfasserin |4 aut | |
| 700 | 1 | |a Krivak, Thomas C. |e verfasserin |4 aut | |
| 700 | 1 | |a Fowler, Jeffrey M. |e verfasserin |4 aut | |
| 700 | 1 | |a Mutch, David G. |e verfasserin |4 aut | |
| 700 | 1 | |a Van Le, Linda |e verfasserin |4 aut | |
| 700 | 1 | |a Lee, Roger B. |e verfasserin |4 aut | |
| 700 | 1 | |a Argenta, Peter |e verfasserin |4 aut | |
| 700 | 1 | |a Bender, David |e verfasserin |4 aut | |
| 700 | 1 | |a Tewari, Krishnansu S. |e verfasserin |4 aut | |
| 700 | 1 | |a Gershenson, David |e verfasserin |4 aut | |
| 700 | 1 | |a Java, James J. |e verfasserin |4 aut | |
| 700 | 1 | |a Bookman, Michael A. |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Gynecologic oncology |d Orlando, Fla. : Academic Press, 1972 |g 148 |h Online-Ressource |w (DE-627)266881351 |w (DE-600)1467974-7 |w (DE-576)104193735 |x 1095-6859 |7 nnns |
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2017 |
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2017 |
| allfields |
10.1016/j.ygyno.2017.10.011 doi (DE-627)ELV00070783X (ELSEVIER)S0090-8258(17)31371-9 DE-627 ger DE-627 rda eng 610 DE-600 44.81 bkl 44.92 bkl Horowitz, Neil S. verfasserin aut Predictive modeling for determination of microscopic residual disease at primary cytoreduction: An NRG Oncology/Gynecologic Oncology Group 182 Study 2017 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objective: Microscopic residual disease following complete cytoreduction (R0) is associated with a significant survival benefit for patients with advanced epithelial ovarian cancer (EOC). Our objective was to develop a prediction model for R0 to support surgeons in their clinical care decisions.Methods: Demographic, pathologic, surgical, and CA125 data were collected from GOG 182 records. Patients enrolled prior to September 1, 2003 were used for the training model while those enrolled after constituted the validation data set. Univariate analysis was performed to identify significant predictors of R0 and these variables were subsequently analyzed using multivariable regression. The regression model was reduced using backward selection and predictive accuracy was quantified using area under the receiver operating characteristic area under the curve (AUC) in both the training and the validation data sets.Results: Of the 3882 patients enrolled in GOG 182, 1480 had complete clinical data available for the analysis. The training data set consisted of 1007 patients (234 with R0) while the validation set was comprised of 473 patients (122 with R0). The reduced multivariable regression model demonstrated several variables predictive of R0 at cytoreduction: Disease Score (DS) (p <0.001), stage (p =0.009), CA125 (p <0.001), ascites (p <0.001), and stage-age interaction (p =0.01). Applying the prediction model to the validation data resulted in an AUC of 0.73 (0.67 to 0.78, 95% CI). Inclusion of DS enhanced the model performance to an AUC of 0.83 (0.79 to 0.88, 95% CI).Conclusions: We developed and validated a prediction model for R0 that offers improved performance over previously reported models for prediction of residual disease. The performance of the prediction model suggests additional factors (i.e. imaging, molecular profiling, etc.) should be explored in the future for a more clinically actionable tool. Ovarian cancer Microscopic residual Larry Maxwell, G. verfasserin aut Miller, Austin verfasserin aut Hamilton, Chad A. verfasserin aut Rungruang, Bunja verfasserin aut Rodriguez, Noah verfasserin aut Richard, Scott D. verfasserin aut Krivak, Thomas C. verfasserin aut Fowler, Jeffrey M. verfasserin aut Mutch, David G. verfasserin aut Van Le, Linda verfasserin aut Lee, Roger B. verfasserin aut Argenta, Peter verfasserin aut Bender, David verfasserin aut Tewari, Krishnansu S. verfasserin aut Gershenson, David verfasserin aut Java, James J. verfasserin aut Bookman, Michael A. verfasserin aut Enthalten in Gynecologic oncology Orlando, Fla. : Academic Press, 1972 148 Online-Ressource (DE-627)266881351 (DE-600)1467974-7 (DE-576)104193735 1095-6859 nnns volume:148 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 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_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2098 GBV_ILN_2106 GBV_ILN_2108 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_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 44.81 Onkologie 44.92 Gynäkologie AR 148 |
| spelling |
10.1016/j.ygyno.2017.10.011 doi (DE-627)ELV00070783X (ELSEVIER)S0090-8258(17)31371-9 DE-627 ger DE-627 rda eng 610 DE-600 44.81 bkl 44.92 bkl Horowitz, Neil S. verfasserin aut Predictive modeling for determination of microscopic residual disease at primary cytoreduction: An NRG Oncology/Gynecologic Oncology Group 182 Study 2017 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objective: Microscopic residual disease following complete cytoreduction (R0) is associated with a significant survival benefit for patients with advanced epithelial ovarian cancer (EOC). Our objective was to develop a prediction model for R0 to support surgeons in their clinical care decisions.Methods: Demographic, pathologic, surgical, and CA125 data were collected from GOG 182 records. Patients enrolled prior to September 1, 2003 were used for the training model while those enrolled after constituted the validation data set. Univariate analysis was performed to identify significant predictors of R0 and these variables were subsequently analyzed using multivariable regression. The regression model was reduced using backward selection and predictive accuracy was quantified using area under the receiver operating characteristic area under the curve (AUC) in both the training and the validation data sets.Results: Of the 3882 patients enrolled in GOG 182, 1480 had complete clinical data available for the analysis. The training data set consisted of 1007 patients (234 with R0) while the validation set was comprised of 473 patients (122 with R0). The reduced multivariable regression model demonstrated several variables predictive of R0 at cytoreduction: Disease Score (DS) (p <0.001), stage (p =0.009), CA125 (p <0.001), ascites (p <0.001), and stage-age interaction (p =0.01). Applying the prediction model to the validation data resulted in an AUC of 0.73 (0.67 to 0.78, 95% CI). Inclusion of DS enhanced the model performance to an AUC of 0.83 (0.79 to 0.88, 95% CI).Conclusions: We developed and validated a prediction model for R0 that offers improved performance over previously reported models for prediction of residual disease. The performance of the prediction model suggests additional factors (i.e. imaging, molecular profiling, etc.) should be explored in the future for a more clinically actionable tool. Ovarian cancer Microscopic residual Larry Maxwell, G. verfasserin aut Miller, Austin verfasserin aut Hamilton, Chad A. verfasserin aut Rungruang, Bunja verfasserin aut Rodriguez, Noah verfasserin aut Richard, Scott D. verfasserin aut Krivak, Thomas C. verfasserin aut Fowler, Jeffrey M. verfasserin aut Mutch, David G. verfasserin aut Van Le, Linda verfasserin aut Lee, Roger B. verfasserin aut Argenta, Peter verfasserin aut Bender, David verfasserin aut Tewari, Krishnansu S. verfasserin aut Gershenson, David verfasserin aut Java, James J. verfasserin aut Bookman, Michael A. verfasserin aut Enthalten in Gynecologic oncology Orlando, Fla. : Academic Press, 1972 148 Online-Ressource (DE-627)266881351 (DE-600)1467974-7 (DE-576)104193735 1095-6859 nnns volume:148 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 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_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2098 GBV_ILN_2106 GBV_ILN_2108 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_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 44.81 Onkologie 44.92 Gynäkologie AR 148 |
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10.1016/j.ygyno.2017.10.011 doi (DE-627)ELV00070783X (ELSEVIER)S0090-8258(17)31371-9 DE-627 ger DE-627 rda eng 610 DE-600 44.81 bkl 44.92 bkl Horowitz, Neil S. verfasserin aut Predictive modeling for determination of microscopic residual disease at primary cytoreduction: An NRG Oncology/Gynecologic Oncology Group 182 Study 2017 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objective: Microscopic residual disease following complete cytoreduction (R0) is associated with a significant survival benefit for patients with advanced epithelial ovarian cancer (EOC). Our objective was to develop a prediction model for R0 to support surgeons in their clinical care decisions.Methods: Demographic, pathologic, surgical, and CA125 data were collected from GOG 182 records. Patients enrolled prior to September 1, 2003 were used for the training model while those enrolled after constituted the validation data set. Univariate analysis was performed to identify significant predictors of R0 and these variables were subsequently analyzed using multivariable regression. The regression model was reduced using backward selection and predictive accuracy was quantified using area under the receiver operating characteristic area under the curve (AUC) in both the training and the validation data sets.Results: Of the 3882 patients enrolled in GOG 182, 1480 had complete clinical data available for the analysis. The training data set consisted of 1007 patients (234 with R0) while the validation set was comprised of 473 patients (122 with R0). The reduced multivariable regression model demonstrated several variables predictive of R0 at cytoreduction: Disease Score (DS) (p <0.001), stage (p =0.009), CA125 (p <0.001), ascites (p <0.001), and stage-age interaction (p =0.01). Applying the prediction model to the validation data resulted in an AUC of 0.73 (0.67 to 0.78, 95% CI). Inclusion of DS enhanced the model performance to an AUC of 0.83 (0.79 to 0.88, 95% CI).Conclusions: We developed and validated a prediction model for R0 that offers improved performance over previously reported models for prediction of residual disease. The performance of the prediction model suggests additional factors (i.e. imaging, molecular profiling, etc.) should be explored in the future for a more clinically actionable tool. Ovarian cancer Microscopic residual Larry Maxwell, G. verfasserin aut Miller, Austin verfasserin aut Hamilton, Chad A. verfasserin aut Rungruang, Bunja verfasserin aut Rodriguez, Noah verfasserin aut Richard, Scott D. verfasserin aut Krivak, Thomas C. verfasserin aut Fowler, Jeffrey M. verfasserin aut Mutch, David G. verfasserin aut Van Le, Linda verfasserin aut Lee, Roger B. verfasserin aut Argenta, Peter verfasserin aut Bender, David verfasserin aut Tewari, Krishnansu S. verfasserin aut Gershenson, David verfasserin aut Java, James J. verfasserin aut Bookman, Michael A. verfasserin aut Enthalten in Gynecologic oncology Orlando, Fla. : Academic Press, 1972 148 Online-Ressource (DE-627)266881351 (DE-600)1467974-7 (DE-576)104193735 1095-6859 nnns volume:148 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 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_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2098 GBV_ILN_2106 GBV_ILN_2108 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_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 44.81 Onkologie 44.92 Gynäkologie AR 148 |
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10.1016/j.ygyno.2017.10.011 doi (DE-627)ELV00070783X (ELSEVIER)S0090-8258(17)31371-9 DE-627 ger DE-627 rda eng 610 DE-600 44.81 bkl 44.92 bkl Horowitz, Neil S. verfasserin aut Predictive modeling for determination of microscopic residual disease at primary cytoreduction: An NRG Oncology/Gynecologic Oncology Group 182 Study 2017 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objective: Microscopic residual disease following complete cytoreduction (R0) is associated with a significant survival benefit for patients with advanced epithelial ovarian cancer (EOC). Our objective was to develop a prediction model for R0 to support surgeons in their clinical care decisions.Methods: Demographic, pathologic, surgical, and CA125 data were collected from GOG 182 records. Patients enrolled prior to September 1, 2003 were used for the training model while those enrolled after constituted the validation data set. Univariate analysis was performed to identify significant predictors of R0 and these variables were subsequently analyzed using multivariable regression. The regression model was reduced using backward selection and predictive accuracy was quantified using area under the receiver operating characteristic area under the curve (AUC) in both the training and the validation data sets.Results: Of the 3882 patients enrolled in GOG 182, 1480 had complete clinical data available for the analysis. The training data set consisted of 1007 patients (234 with R0) while the validation set was comprised of 473 patients (122 with R0). The reduced multivariable regression model demonstrated several variables predictive of R0 at cytoreduction: Disease Score (DS) (p <0.001), stage (p =0.009), CA125 (p <0.001), ascites (p <0.001), and stage-age interaction (p =0.01). Applying the prediction model to the validation data resulted in an AUC of 0.73 (0.67 to 0.78, 95% CI). Inclusion of DS enhanced the model performance to an AUC of 0.83 (0.79 to 0.88, 95% CI).Conclusions: We developed and validated a prediction model for R0 that offers improved performance over previously reported models for prediction of residual disease. The performance of the prediction model suggests additional factors (i.e. imaging, molecular profiling, etc.) should be explored in the future for a more clinically actionable tool. Ovarian cancer Microscopic residual Larry Maxwell, G. verfasserin aut Miller, Austin verfasserin aut Hamilton, Chad A. verfasserin aut Rungruang, Bunja verfasserin aut Rodriguez, Noah verfasserin aut Richard, Scott D. verfasserin aut Krivak, Thomas C. verfasserin aut Fowler, Jeffrey M. verfasserin aut Mutch, David G. verfasserin aut Van Le, Linda verfasserin aut Lee, Roger B. verfasserin aut Argenta, Peter verfasserin aut Bender, David verfasserin aut Tewari, Krishnansu S. verfasserin aut Gershenson, David verfasserin aut Java, James J. verfasserin aut Bookman, Michael A. verfasserin aut Enthalten in Gynecologic oncology Orlando, Fla. : Academic Press, 1972 148 Online-Ressource (DE-627)266881351 (DE-600)1467974-7 (DE-576)104193735 1095-6859 nnns volume:148 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 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_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2098 GBV_ILN_2106 GBV_ILN_2108 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_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 44.81 Onkologie 44.92 Gynäkologie AR 148 |
| allfieldsSound |
10.1016/j.ygyno.2017.10.011 doi (DE-627)ELV00070783X (ELSEVIER)S0090-8258(17)31371-9 DE-627 ger DE-627 rda eng 610 DE-600 44.81 bkl 44.92 bkl Horowitz, Neil S. verfasserin aut Predictive modeling for determination of microscopic residual disease at primary cytoreduction: An NRG Oncology/Gynecologic Oncology Group 182 Study 2017 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Objective: Microscopic residual disease following complete cytoreduction (R0) is associated with a significant survival benefit for patients with advanced epithelial ovarian cancer (EOC). Our objective was to develop a prediction model for R0 to support surgeons in their clinical care decisions.Methods: Demographic, pathologic, surgical, and CA125 data were collected from GOG 182 records. Patients enrolled prior to September 1, 2003 were used for the training model while those enrolled after constituted the validation data set. Univariate analysis was performed to identify significant predictors of R0 and these variables were subsequently analyzed using multivariable regression. The regression model was reduced using backward selection and predictive accuracy was quantified using area under the receiver operating characteristic area under the curve (AUC) in both the training and the validation data sets.Results: Of the 3882 patients enrolled in GOG 182, 1480 had complete clinical data available for the analysis. The training data set consisted of 1007 patients (234 with R0) while the validation set was comprised of 473 patients (122 with R0). The reduced multivariable regression model demonstrated several variables predictive of R0 at cytoreduction: Disease Score (DS) (p <0.001), stage (p =0.009), CA125 (p <0.001), ascites (p <0.001), and stage-age interaction (p =0.01). Applying the prediction model to the validation data resulted in an AUC of 0.73 (0.67 to 0.78, 95% CI). Inclusion of DS enhanced the model performance to an AUC of 0.83 (0.79 to 0.88, 95% CI).Conclusions: We developed and validated a prediction model for R0 that offers improved performance over previously reported models for prediction of residual disease. The performance of the prediction model suggests additional factors (i.e. imaging, molecular profiling, etc.) should be explored in the future for a more clinically actionable tool. Ovarian cancer Microscopic residual Larry Maxwell, G. verfasserin aut Miller, Austin verfasserin aut Hamilton, Chad A. verfasserin aut Rungruang, Bunja verfasserin aut Rodriguez, Noah verfasserin aut Richard, Scott D. verfasserin aut Krivak, Thomas C. verfasserin aut Fowler, Jeffrey M. verfasserin aut Mutch, David G. verfasserin aut Van Le, Linda verfasserin aut Lee, Roger B. verfasserin aut Argenta, Peter verfasserin aut Bender, David verfasserin aut Tewari, Krishnansu S. verfasserin aut Gershenson, David verfasserin aut Java, James J. verfasserin aut Bookman, Michael A. verfasserin aut Enthalten in Gynecologic oncology Orlando, Fla. : Academic Press, 1972 148 Online-Ressource (DE-627)266881351 (DE-600)1467974-7 (DE-576)104193735 1095-6859 nnns volume:148 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 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_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2098 GBV_ILN_2106 GBV_ILN_2108 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_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 44.81 Onkologie 44.92 Gynäkologie AR 148 |
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Enthalten in Gynecologic oncology 148 volume:148 |
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Ovarian cancer Microscopic residual |
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Gynecologic oncology |
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Horowitz, Neil S. @@aut@@ Larry Maxwell, G. @@aut@@ Miller, Austin @@aut@@ Hamilton, Chad A. @@aut@@ Rungruang, Bunja @@aut@@ Rodriguez, Noah @@aut@@ Richard, Scott D. @@aut@@ Krivak, Thomas C. @@aut@@ Fowler, Jeffrey M. @@aut@@ Mutch, David G. @@aut@@ Van Le, Linda @@aut@@ Lee, Roger B. @@aut@@ Argenta, Peter @@aut@@ Bender, David @@aut@@ Tewari, Krishnansu S. @@aut@@ Gershenson, David @@aut@@ Java, James J. @@aut@@ Bookman, Michael A. @@aut@@ |
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2017-01-01T00:00:00Z |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV00070783X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230524140740.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230427s2017 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.ygyno.2017.10.011</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV00070783X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0090-8258(17)31371-9</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">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.81</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.92</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Horowitz, Neil S.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Predictive modeling for determination of microscopic residual disease at primary cytoreduction: An NRG Oncology/Gynecologic Oncology Group 182 Study</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2017</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</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">Objective: Microscopic residual disease following complete cytoreduction (R0) is associated with a significant survival benefit for patients with advanced epithelial ovarian cancer (EOC). Our objective was to develop a prediction model for R0 to support surgeons in their clinical care decisions.Methods: Demographic, pathologic, surgical, and CA125 data were collected from GOG 182 records. Patients enrolled prior to September 1, 2003 were used for the training model while those enrolled after constituted the validation data set. Univariate analysis was performed to identify significant predictors of R0 and these variables were subsequently analyzed using multivariable regression. The regression model was reduced using backward selection and predictive accuracy was quantified using area under the receiver operating characteristic area under the curve (AUC) in both the training and the validation data sets.Results: Of the 3882 patients enrolled in GOG 182, 1480 had complete clinical data available for the analysis. The training data set consisted of 1007 patients (234 with R0) while the validation set was comprised of 473 patients (122 with R0). The reduced multivariable regression model demonstrated several variables predictive of R0 at cytoreduction: Disease Score (DS) (p <0.001), stage (p =0.009), CA125 (p <0.001), ascites (p <0.001), and stage-age interaction (p =0.01). Applying the prediction model to the validation data resulted in an AUC of 0.73 (0.67 to 0.78, 95% CI). Inclusion of DS enhanced the model performance to an AUC of 0.83 (0.79 to 0.88, 95% CI).Conclusions: We developed and validated a prediction model for R0 that offers improved performance over previously reported models for prediction of residual disease. 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Horowitz, Neil S. |
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Horowitz, Neil S. ddc 610 bkl 44.81 bkl 44.92 misc Ovarian cancer misc Microscopic residual Predictive modeling for determination of microscopic residual disease at primary cytoreduction: An NRG Oncology/Gynecologic Oncology Group 182 Study |
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610 DE-600 44.81 bkl 44.92 bkl Predictive modeling for determination of microscopic residual disease at primary cytoreduction: An NRG Oncology/Gynecologic Oncology Group 182 Study Ovarian cancer Microscopic residual |
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ddc 610 bkl 44.81 bkl 44.92 misc Ovarian cancer misc Microscopic residual |
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Predictive modeling for determination of microscopic residual disease at primary cytoreduction: An NRG Oncology/Gynecologic Oncology Group 182 Study |
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Predictive modeling for determination of microscopic residual disease at primary cytoreduction: An NRG Oncology/Gynecologic Oncology Group 182 Study |
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Gynecologic oncology |
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Horowitz, Neil S. Larry Maxwell, G. Miller, Austin Hamilton, Chad A. Rungruang, Bunja Rodriguez, Noah Richard, Scott D. Krivak, Thomas C. Fowler, Jeffrey M. Mutch, David G. Van Le, Linda Lee, Roger B. Argenta, Peter Bender, David Tewari, Krishnansu S. Gershenson, David Java, James J. Bookman, Michael A. |
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predictive modeling for determination of microscopic residual disease at primary cytoreduction: an nrg oncology/gynecologic oncology group 182 study |
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Predictive modeling for determination of microscopic residual disease at primary cytoreduction: An NRG Oncology/Gynecologic Oncology Group 182 Study |
| abstract |
Objective: Microscopic residual disease following complete cytoreduction (R0) is associated with a significant survival benefit for patients with advanced epithelial ovarian cancer (EOC). Our objective was to develop a prediction model for R0 to support surgeons in their clinical care decisions.Methods: Demographic, pathologic, surgical, and CA125 data were collected from GOG 182 records. Patients enrolled prior to September 1, 2003 were used for the training model while those enrolled after constituted the validation data set. Univariate analysis was performed to identify significant predictors of R0 and these variables were subsequently analyzed using multivariable regression. The regression model was reduced using backward selection and predictive accuracy was quantified using area under the receiver operating characteristic area under the curve (AUC) in both the training and the validation data sets.Results: Of the 3882 patients enrolled in GOG 182, 1480 had complete clinical data available for the analysis. The training data set consisted of 1007 patients (234 with R0) while the validation set was comprised of 473 patients (122 with R0). The reduced multivariable regression model demonstrated several variables predictive of R0 at cytoreduction: Disease Score (DS) (p <0.001), stage (p =0.009), CA125 (p <0.001), ascites (p <0.001), and stage-age interaction (p =0.01). Applying the prediction model to the validation data resulted in an AUC of 0.73 (0.67 to 0.78, 95% CI). Inclusion of DS enhanced the model performance to an AUC of 0.83 (0.79 to 0.88, 95% CI).Conclusions: We developed and validated a prediction model for R0 that offers improved performance over previously reported models for prediction of residual disease. The performance of the prediction model suggests additional factors (i.e. imaging, molecular profiling, etc.) should be explored in the future for a more clinically actionable tool. |
| abstractGer |
Objective: Microscopic residual disease following complete cytoreduction (R0) is associated with a significant survival benefit for patients with advanced epithelial ovarian cancer (EOC). Our objective was to develop a prediction model for R0 to support surgeons in their clinical care decisions.Methods: Demographic, pathologic, surgical, and CA125 data were collected from GOG 182 records. Patients enrolled prior to September 1, 2003 were used for the training model while those enrolled after constituted the validation data set. Univariate analysis was performed to identify significant predictors of R0 and these variables were subsequently analyzed using multivariable regression. The regression model was reduced using backward selection and predictive accuracy was quantified using area under the receiver operating characteristic area under the curve (AUC) in both the training and the validation data sets.Results: Of the 3882 patients enrolled in GOG 182, 1480 had complete clinical data available for the analysis. The training data set consisted of 1007 patients (234 with R0) while the validation set was comprised of 473 patients (122 with R0). The reduced multivariable regression model demonstrated several variables predictive of R0 at cytoreduction: Disease Score (DS) (p <0.001), stage (p =0.009), CA125 (p <0.001), ascites (p <0.001), and stage-age interaction (p =0.01). Applying the prediction model to the validation data resulted in an AUC of 0.73 (0.67 to 0.78, 95% CI). Inclusion of DS enhanced the model performance to an AUC of 0.83 (0.79 to 0.88, 95% CI).Conclusions: We developed and validated a prediction model for R0 that offers improved performance over previously reported models for prediction of residual disease. The performance of the prediction model suggests additional factors (i.e. imaging, molecular profiling, etc.) should be explored in the future for a more clinically actionable tool. |
| abstract_unstemmed |
Objective: Microscopic residual disease following complete cytoreduction (R0) is associated with a significant survival benefit for patients with advanced epithelial ovarian cancer (EOC). Our objective was to develop a prediction model for R0 to support surgeons in their clinical care decisions.Methods: Demographic, pathologic, surgical, and CA125 data were collected from GOG 182 records. Patients enrolled prior to September 1, 2003 were used for the training model while those enrolled after constituted the validation data set. Univariate analysis was performed to identify significant predictors of R0 and these variables were subsequently analyzed using multivariable regression. The regression model was reduced using backward selection and predictive accuracy was quantified using area under the receiver operating characteristic area under the curve (AUC) in both the training and the validation data sets.Results: Of the 3882 patients enrolled in GOG 182, 1480 had complete clinical data available for the analysis. The training data set consisted of 1007 patients (234 with R0) while the validation set was comprised of 473 patients (122 with R0). The reduced multivariable regression model demonstrated several variables predictive of R0 at cytoreduction: Disease Score (DS) (p <0.001), stage (p =0.009), CA125 (p <0.001), ascites (p <0.001), and stage-age interaction (p =0.01). Applying the prediction model to the validation data resulted in an AUC of 0.73 (0.67 to 0.78, 95% CI). Inclusion of DS enhanced the model performance to an AUC of 0.83 (0.79 to 0.88, 95% CI).Conclusions: We developed and validated a prediction model for R0 that offers improved performance over previously reported models for prediction of residual disease. The performance of the prediction model suggests additional factors (i.e. imaging, molecular profiling, etc.) should be explored in the future for a more clinically actionable tool. |
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| title_short |
Predictive modeling for determination of microscopic residual disease at primary cytoreduction: An NRG Oncology/Gynecologic Oncology Group 182 Study |
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true |
| author2 |
Larry Maxwell, G. Miller, Austin Hamilton, Chad A. Rungruang, Bunja Rodriguez, Noah Richard, Scott D. Krivak, Thomas C. Fowler, Jeffrey M. Mutch, David G. Van Le, Linda Lee, Roger B. Argenta, Peter Bender, David Tewari, Krishnansu S. Gershenson, David Java, James J. Bookman, Michael A. |
| author2Str |
Larry Maxwell, G. Miller, Austin Hamilton, Chad A. Rungruang, Bunja Rodriguez, Noah Richard, Scott D. Krivak, Thomas C. Fowler, Jeffrey M. Mutch, David G. Van Le, Linda Lee, Roger B. Argenta, Peter Bender, David Tewari, Krishnansu S. Gershenson, David Java, James J. Bookman, Michael A. |
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| doi_str |
10.1016/j.ygyno.2017.10.011 |
| up_date |
2024-07-06T18:53:53.495Z |
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1803856939317198848 |
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