The role of a knowledge based dose–volume histogram predictive model in the optimisation of intensity-modulated proton plans for hepatocellular carcinoma patients
Purpose To investigate the performance of a knowledge-based RapidPlan, for optimisation of intensity-modulated proton therapy (IMPT) plans applied to hepatocellular cancer (HCC) patients. Methods A cohort of 65 patients was retrospectively selected: 50 were used to “train” the model, while the remai...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Cozzi, Luca [verfasserIn] Vanderstraeten, Reynald [verfasserIn] Fogliata, Antonella [verfasserIn] Chang, Feng-Ling [verfasserIn] Wang, Po-Ming [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
Intensity-modulated proton therapy |
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| Übergeordnetes Werk: |
Enthalten in: Strahlentherapie und Onkologie - Berlin : Springer Medizin, 1997, 197(2020), 4 vom: 16. Juli, Seite 332-342 |
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| Übergeordnetes Werk: |
volume:197 ; year:2020 ; number:4 ; day:16 ; month:07 ; pages:332-342 |
| Links: |
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| DOI / URN: |
10.1007/s00066-020-01664-2 |
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| Katalog-ID: |
SPR043590144 |
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| 245 | 1 | 4 | |a The role of a knowledge based dose–volume histogram predictive model in the optimisation of intensity-modulated proton plans for hepatocellular carcinoma patients |
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| 520 | |a Purpose To investigate the performance of a knowledge-based RapidPlan, for optimisation of intensity-modulated proton therapy (IMPT) plans applied to hepatocellular cancer (HCC) patients. Methods A cohort of 65 patients was retrospectively selected: 50 were used to “train” the model, while the remaining 15 provided independent validation. The performance of the RapidPlan model was benchmarked against manual optimisation and was also compared to volumetric modulated arc therapy (RapidArc) photon plans. A subanalysis appraised the performance of the RapidPlan model applied to patients with lesions ≤300 $ cm^{3} $ or larger. Quantitative assessment was based on several metrics derived from the constraints of the NRG-GI003 clinical trial. Results There was an equivalence between manual plans and RapidPlan-optimised IMPT plans, which outperformed the RapidArc plans. The planning dose–volume objectives were met on average for all structures except for $ D_{0.5} $ cm3 ≤30 Gy in the bowels. Limiting the results to the class-solution proton plans (all values in Gy), the data for manual plans vs RapidPlan-based IMPT plans, respectively, showed the following: $ D_{99%} $ to the target of 47.5 ± 1.4 vs 47.2 ± 1.2; for organs at risk, the mean dose to the healthy liver was 6.7 ± 3.6 vs 6.7 ± 3.7; the mean dose to the kidneys was 0.2 ± 0.5 vs 0.1 ± 0.2; $ D_{0.5} $ cm3 for the bowels was 33.4 ± 16.4 vs 30.2 ± 16.0; for the stomach was 17.9 ± 19.9 vs 14.9 ± 18.8; for the oesophagus was 17.9 ± 15.1 vs 14.9 ± 13.9; for the spinal cord was 0.5 ± 1.6 vs 0.2 ± 0.7. The model performed similarly for cases with small or large lesions. Conclusion A knowledge-based RapidPlan model was trained and validated for IMPT. The results demonstrate that RapidPlan can be trained adequately for IMPT in HCC. The quality of the RapidPlan-based plans is at least equivalent compared to what is achievable with manual planning. RapidPlan also confirmed the potential to optimise the quality of the proton therapy results, thus reducing the impact of operator planning skills on patient results. | ||
| 650 | 4 | |a Intensity-modulated proton therapy |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Volumetric modulated arc therapy |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a RapidArc |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Hepatocellular cancer |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Machine learning |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Vanderstraeten, Reynald |e verfasserin |4 aut | |
| 700 | 1 | |a Fogliata, Antonella |e verfasserin |4 aut | |
| 700 | 1 | |a Chang, Feng-Ling |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Po-Ming |e verfasserin |4 aut | |
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10.1007/s00066-020-01664-2 doi (DE-627)SPR043590144 (DE-599)SPRs00066-020-01664-2-e (SPR)s00066-020-01664-2-e DE-627 ger DE-627 rakwb eng 610 ASE 610 ASE 44.81 bkl 44.64 bkl Cozzi, Luca verfasserin aut The role of a knowledge based dose–volume histogram predictive model in the optimisation of intensity-modulated proton plans for hepatocellular carcinoma patients 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose To investigate the performance of a knowledge-based RapidPlan, for optimisation of intensity-modulated proton therapy (IMPT) plans applied to hepatocellular cancer (HCC) patients. Methods A cohort of 65 patients was retrospectively selected: 50 were used to “train” the model, while the remaining 15 provided independent validation. The performance of the RapidPlan model was benchmarked against manual optimisation and was also compared to volumetric modulated arc therapy (RapidArc) photon plans. A subanalysis appraised the performance of the RapidPlan model applied to patients with lesions ≤300 $ cm^{3} $ or larger. Quantitative assessment was based on several metrics derived from the constraints of the NRG-GI003 clinical trial. Results There was an equivalence between manual plans and RapidPlan-optimised IMPT plans, which outperformed the RapidArc plans. The planning dose–volume objectives were met on average for all structures except for $ D_{0.5} $ cm3 ≤30 Gy in the bowels. Limiting the results to the class-solution proton plans (all values in Gy), the data for manual plans vs RapidPlan-based IMPT plans, respectively, showed the following: $ D_{99%} $ to the target of 47.5 ± 1.4 vs 47.2 ± 1.2; for organs at risk, the mean dose to the healthy liver was 6.7 ± 3.6 vs 6.7 ± 3.7; the mean dose to the kidneys was 0.2 ± 0.5 vs 0.1 ± 0.2; $ D_{0.5} $ cm3 for the bowels was 33.4 ± 16.4 vs 30.2 ± 16.0; for the stomach was 17.9 ± 19.9 vs 14.9 ± 18.8; for the oesophagus was 17.9 ± 15.1 vs 14.9 ± 13.9; for the spinal cord was 0.5 ± 1.6 vs 0.2 ± 0.7. The model performed similarly for cases with small or large lesions. Conclusion A knowledge-based RapidPlan model was trained and validated for IMPT. The results demonstrate that RapidPlan can be trained adequately for IMPT in HCC. The quality of the RapidPlan-based plans is at least equivalent compared to what is achievable with manual planning. RapidPlan also confirmed the potential to optimise the quality of the proton therapy results, thus reducing the impact of operator planning skills on patient results. Intensity-modulated proton therapy (dpeaa)DE-He213 Volumetric modulated arc therapy (dpeaa)DE-He213 RapidArc (dpeaa)DE-He213 Hepatocellular cancer (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Vanderstraeten, Reynald verfasserin aut Fogliata, Antonella verfasserin aut Chang, Feng-Ling verfasserin aut Wang, Po-Ming verfasserin aut Enthalten in Strahlentherapie und Onkologie Berlin : Springer Medizin, 1997 197(2020), 4 vom: 16. Juli, Seite 332-342 (DE-627)312407866 (DE-600)2003907-4 1439-099X nnns volume:197 year:2020 number:4 day:16 month:07 pages:332-342 https://dx.doi.org/10.1007/s00066-020-01664-2 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_4277 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 44.81 ASE 44.64 ASE AR 197 2020 4 16 07 332-342 |
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10.1007/s00066-020-01664-2 doi (DE-627)SPR043590144 (DE-599)SPRs00066-020-01664-2-e (SPR)s00066-020-01664-2-e DE-627 ger DE-627 rakwb eng 610 ASE 610 ASE 44.81 bkl 44.64 bkl Cozzi, Luca verfasserin aut The role of a knowledge based dose–volume histogram predictive model in the optimisation of intensity-modulated proton plans for hepatocellular carcinoma patients 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose To investigate the performance of a knowledge-based RapidPlan, for optimisation of intensity-modulated proton therapy (IMPT) plans applied to hepatocellular cancer (HCC) patients. Methods A cohort of 65 patients was retrospectively selected: 50 were used to “train” the model, while the remaining 15 provided independent validation. The performance of the RapidPlan model was benchmarked against manual optimisation and was also compared to volumetric modulated arc therapy (RapidArc) photon plans. A subanalysis appraised the performance of the RapidPlan model applied to patients with lesions ≤300 $ cm^{3} $ or larger. Quantitative assessment was based on several metrics derived from the constraints of the NRG-GI003 clinical trial. Results There was an equivalence between manual plans and RapidPlan-optimised IMPT plans, which outperformed the RapidArc plans. The planning dose–volume objectives were met on average for all structures except for $ D_{0.5} $ cm3 ≤30 Gy in the bowels. Limiting the results to the class-solution proton plans (all values in Gy), the data for manual plans vs RapidPlan-based IMPT plans, respectively, showed the following: $ D_{99%} $ to the target of 47.5 ± 1.4 vs 47.2 ± 1.2; for organs at risk, the mean dose to the healthy liver was 6.7 ± 3.6 vs 6.7 ± 3.7; the mean dose to the kidneys was 0.2 ± 0.5 vs 0.1 ± 0.2; $ D_{0.5} $ cm3 for the bowels was 33.4 ± 16.4 vs 30.2 ± 16.0; for the stomach was 17.9 ± 19.9 vs 14.9 ± 18.8; for the oesophagus was 17.9 ± 15.1 vs 14.9 ± 13.9; for the spinal cord was 0.5 ± 1.6 vs 0.2 ± 0.7. The model performed similarly for cases with small or large lesions. Conclusion A knowledge-based RapidPlan model was trained and validated for IMPT. The results demonstrate that RapidPlan can be trained adequately for IMPT in HCC. The quality of the RapidPlan-based plans is at least equivalent compared to what is achievable with manual planning. RapidPlan also confirmed the potential to optimise the quality of the proton therapy results, thus reducing the impact of operator planning skills on patient results. Intensity-modulated proton therapy (dpeaa)DE-He213 Volumetric modulated arc therapy (dpeaa)DE-He213 RapidArc (dpeaa)DE-He213 Hepatocellular cancer (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Vanderstraeten, Reynald verfasserin aut Fogliata, Antonella verfasserin aut Chang, Feng-Ling verfasserin aut Wang, Po-Ming verfasserin aut Enthalten in Strahlentherapie und Onkologie Berlin : Springer Medizin, 1997 197(2020), 4 vom: 16. Juli, Seite 332-342 (DE-627)312407866 (DE-600)2003907-4 1439-099X nnns volume:197 year:2020 number:4 day:16 month:07 pages:332-342 https://dx.doi.org/10.1007/s00066-020-01664-2 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_4277 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 44.81 ASE 44.64 ASE AR 197 2020 4 16 07 332-342 |
| allfields_unstemmed |
10.1007/s00066-020-01664-2 doi (DE-627)SPR043590144 (DE-599)SPRs00066-020-01664-2-e (SPR)s00066-020-01664-2-e DE-627 ger DE-627 rakwb eng 610 ASE 610 ASE 44.81 bkl 44.64 bkl Cozzi, Luca verfasserin aut The role of a knowledge based dose–volume histogram predictive model in the optimisation of intensity-modulated proton plans for hepatocellular carcinoma patients 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose To investigate the performance of a knowledge-based RapidPlan, for optimisation of intensity-modulated proton therapy (IMPT) plans applied to hepatocellular cancer (HCC) patients. Methods A cohort of 65 patients was retrospectively selected: 50 were used to “train” the model, while the remaining 15 provided independent validation. The performance of the RapidPlan model was benchmarked against manual optimisation and was also compared to volumetric modulated arc therapy (RapidArc) photon plans. A subanalysis appraised the performance of the RapidPlan model applied to patients with lesions ≤300 $ cm^{3} $ or larger. Quantitative assessment was based on several metrics derived from the constraints of the NRG-GI003 clinical trial. Results There was an equivalence between manual plans and RapidPlan-optimised IMPT plans, which outperformed the RapidArc plans. The planning dose–volume objectives were met on average for all structures except for $ D_{0.5} $ cm3 ≤30 Gy in the bowels. Limiting the results to the class-solution proton plans (all values in Gy), the data for manual plans vs RapidPlan-based IMPT plans, respectively, showed the following: $ D_{99%} $ to the target of 47.5 ± 1.4 vs 47.2 ± 1.2; for organs at risk, the mean dose to the healthy liver was 6.7 ± 3.6 vs 6.7 ± 3.7; the mean dose to the kidneys was 0.2 ± 0.5 vs 0.1 ± 0.2; $ D_{0.5} $ cm3 for the bowels was 33.4 ± 16.4 vs 30.2 ± 16.0; for the stomach was 17.9 ± 19.9 vs 14.9 ± 18.8; for the oesophagus was 17.9 ± 15.1 vs 14.9 ± 13.9; for the spinal cord was 0.5 ± 1.6 vs 0.2 ± 0.7. The model performed similarly for cases with small or large lesions. Conclusion A knowledge-based RapidPlan model was trained and validated for IMPT. The results demonstrate that RapidPlan can be trained adequately for IMPT in HCC. The quality of the RapidPlan-based plans is at least equivalent compared to what is achievable with manual planning. RapidPlan also confirmed the potential to optimise the quality of the proton therapy results, thus reducing the impact of operator planning skills on patient results. Intensity-modulated proton therapy (dpeaa)DE-He213 Volumetric modulated arc therapy (dpeaa)DE-He213 RapidArc (dpeaa)DE-He213 Hepatocellular cancer (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Vanderstraeten, Reynald verfasserin aut Fogliata, Antonella verfasserin aut Chang, Feng-Ling verfasserin aut Wang, Po-Ming verfasserin aut Enthalten in Strahlentherapie und Onkologie Berlin : Springer Medizin, 1997 197(2020), 4 vom: 16. Juli, Seite 332-342 (DE-627)312407866 (DE-600)2003907-4 1439-099X nnns volume:197 year:2020 number:4 day:16 month:07 pages:332-342 https://dx.doi.org/10.1007/s00066-020-01664-2 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_4277 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 44.81 ASE 44.64 ASE AR 197 2020 4 16 07 332-342 |
| allfieldsGer |
10.1007/s00066-020-01664-2 doi (DE-627)SPR043590144 (DE-599)SPRs00066-020-01664-2-e (SPR)s00066-020-01664-2-e DE-627 ger DE-627 rakwb eng 610 ASE 610 ASE 44.81 bkl 44.64 bkl Cozzi, Luca verfasserin aut The role of a knowledge based dose–volume histogram predictive model in the optimisation of intensity-modulated proton plans for hepatocellular carcinoma patients 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose To investigate the performance of a knowledge-based RapidPlan, for optimisation of intensity-modulated proton therapy (IMPT) plans applied to hepatocellular cancer (HCC) patients. Methods A cohort of 65 patients was retrospectively selected: 50 were used to “train” the model, while the remaining 15 provided independent validation. The performance of the RapidPlan model was benchmarked against manual optimisation and was also compared to volumetric modulated arc therapy (RapidArc) photon plans. A subanalysis appraised the performance of the RapidPlan model applied to patients with lesions ≤300 $ cm^{3} $ or larger. Quantitative assessment was based on several metrics derived from the constraints of the NRG-GI003 clinical trial. Results There was an equivalence between manual plans and RapidPlan-optimised IMPT plans, which outperformed the RapidArc plans. The planning dose–volume objectives were met on average for all structures except for $ D_{0.5} $ cm3 ≤30 Gy in the bowels. Limiting the results to the class-solution proton plans (all values in Gy), the data for manual plans vs RapidPlan-based IMPT plans, respectively, showed the following: $ D_{99%} $ to the target of 47.5 ± 1.4 vs 47.2 ± 1.2; for organs at risk, the mean dose to the healthy liver was 6.7 ± 3.6 vs 6.7 ± 3.7; the mean dose to the kidneys was 0.2 ± 0.5 vs 0.1 ± 0.2; $ D_{0.5} $ cm3 for the bowels was 33.4 ± 16.4 vs 30.2 ± 16.0; for the stomach was 17.9 ± 19.9 vs 14.9 ± 18.8; for the oesophagus was 17.9 ± 15.1 vs 14.9 ± 13.9; for the spinal cord was 0.5 ± 1.6 vs 0.2 ± 0.7. The model performed similarly for cases with small or large lesions. Conclusion A knowledge-based RapidPlan model was trained and validated for IMPT. The results demonstrate that RapidPlan can be trained adequately for IMPT in HCC. The quality of the RapidPlan-based plans is at least equivalent compared to what is achievable with manual planning. RapidPlan also confirmed the potential to optimise the quality of the proton therapy results, thus reducing the impact of operator planning skills on patient results. Intensity-modulated proton therapy (dpeaa)DE-He213 Volumetric modulated arc therapy (dpeaa)DE-He213 RapidArc (dpeaa)DE-He213 Hepatocellular cancer (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Vanderstraeten, Reynald verfasserin aut Fogliata, Antonella verfasserin aut Chang, Feng-Ling verfasserin aut Wang, Po-Ming verfasserin aut Enthalten in Strahlentherapie und Onkologie Berlin : Springer Medizin, 1997 197(2020), 4 vom: 16. Juli, Seite 332-342 (DE-627)312407866 (DE-600)2003907-4 1439-099X nnns volume:197 year:2020 number:4 day:16 month:07 pages:332-342 https://dx.doi.org/10.1007/s00066-020-01664-2 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_4277 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 44.81 ASE 44.64 ASE AR 197 2020 4 16 07 332-342 |
| allfieldsSound |
10.1007/s00066-020-01664-2 doi (DE-627)SPR043590144 (DE-599)SPRs00066-020-01664-2-e (SPR)s00066-020-01664-2-e DE-627 ger DE-627 rakwb eng 610 ASE 610 ASE 44.81 bkl 44.64 bkl Cozzi, Luca verfasserin aut The role of a knowledge based dose–volume histogram predictive model in the optimisation of intensity-modulated proton plans for hepatocellular carcinoma patients 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose To investigate the performance of a knowledge-based RapidPlan, for optimisation of intensity-modulated proton therapy (IMPT) plans applied to hepatocellular cancer (HCC) patients. Methods A cohort of 65 patients was retrospectively selected: 50 were used to “train” the model, while the remaining 15 provided independent validation. The performance of the RapidPlan model was benchmarked against manual optimisation and was also compared to volumetric modulated arc therapy (RapidArc) photon plans. A subanalysis appraised the performance of the RapidPlan model applied to patients with lesions ≤300 $ cm^{3} $ or larger. Quantitative assessment was based on several metrics derived from the constraints of the NRG-GI003 clinical trial. Results There was an equivalence between manual plans and RapidPlan-optimised IMPT plans, which outperformed the RapidArc plans. The planning dose–volume objectives were met on average for all structures except for $ D_{0.5} $ cm3 ≤30 Gy in the bowels. Limiting the results to the class-solution proton plans (all values in Gy), the data for manual plans vs RapidPlan-based IMPT plans, respectively, showed the following: $ D_{99%} $ to the target of 47.5 ± 1.4 vs 47.2 ± 1.2; for organs at risk, the mean dose to the healthy liver was 6.7 ± 3.6 vs 6.7 ± 3.7; the mean dose to the kidneys was 0.2 ± 0.5 vs 0.1 ± 0.2; $ D_{0.5} $ cm3 for the bowels was 33.4 ± 16.4 vs 30.2 ± 16.0; for the stomach was 17.9 ± 19.9 vs 14.9 ± 18.8; for the oesophagus was 17.9 ± 15.1 vs 14.9 ± 13.9; for the spinal cord was 0.5 ± 1.6 vs 0.2 ± 0.7. The model performed similarly for cases with small or large lesions. Conclusion A knowledge-based RapidPlan model was trained and validated for IMPT. The results demonstrate that RapidPlan can be trained adequately for IMPT in HCC. The quality of the RapidPlan-based plans is at least equivalent compared to what is achievable with manual planning. RapidPlan also confirmed the potential to optimise the quality of the proton therapy results, thus reducing the impact of operator planning skills on patient results. Intensity-modulated proton therapy (dpeaa)DE-He213 Volumetric modulated arc therapy (dpeaa)DE-He213 RapidArc (dpeaa)DE-He213 Hepatocellular cancer (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Vanderstraeten, Reynald verfasserin aut Fogliata, Antonella verfasserin aut Chang, Feng-Ling verfasserin aut Wang, Po-Ming verfasserin aut Enthalten in Strahlentherapie und Onkologie Berlin : Springer Medizin, 1997 197(2020), 4 vom: 16. Juli, Seite 332-342 (DE-627)312407866 (DE-600)2003907-4 1439-099X nnns volume:197 year:2020 number:4 day:16 month:07 pages:332-342 https://dx.doi.org/10.1007/s00066-020-01664-2 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_4277 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 44.81 ASE 44.64 ASE AR 197 2020 4 16 07 332-342 |
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Enthalten in Strahlentherapie und Onkologie 197(2020), 4 vom: 16. Juli, Seite 332-342 volume:197 year:2020 number:4 day:16 month:07 pages:332-342 |
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Intensity-modulated proton therapy Volumetric modulated arc therapy RapidArc Hepatocellular cancer Machine learning |
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Strahlentherapie und Onkologie |
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Cozzi, Luca @@aut@@ Vanderstraeten, Reynald @@aut@@ Fogliata, Antonella @@aut@@ Chang, Feng-Ling @@aut@@ Wang, Po-Ming @@aut@@ |
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2020-07-16T00:00:00Z |
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Methods A cohort of 65 patients was retrospectively selected: 50 were used to “train” the model, while the remaining 15 provided independent validation. The performance of the RapidPlan model was benchmarked against manual optimisation and was also compared to volumetric modulated arc therapy (RapidArc) photon plans. A subanalysis appraised the performance of the RapidPlan model applied to patients with lesions ≤300 $ cm^{3} $ or larger. Quantitative assessment was based on several metrics derived from the constraints of the NRG-GI003 clinical trial. Results There was an equivalence between manual plans and RapidPlan-optimised IMPT plans, which outperformed the RapidArc plans. The planning dose–volume objectives were met on average for all structures except for $ D_{0.5} $ cm3 ≤30 Gy in the bowels. Limiting the results to the class-solution proton plans (all values in Gy), the data for manual plans vs RapidPlan-based IMPT plans, respectively, showed the following: $ D_{99%} $ to the target of 47.5 ± 1.4 vs 47.2 ± 1.2; for organs at risk, the mean dose to the healthy liver was 6.7 ± 3.6 vs 6.7 ± 3.7; the mean dose to the kidneys was 0.2 ± 0.5 vs 0.1 ± 0.2; $ D_{0.5} $ cm3 for the bowels was 33.4 ± 16.4 vs 30.2 ± 16.0; for the stomach was 17.9 ± 19.9 vs 14.9 ± 18.8; for the oesophagus was 17.9 ± 15.1 vs 14.9 ± 13.9; for the spinal cord was 0.5 ± 1.6 vs 0.2 ± 0.7. The model performed similarly for cases with small or large lesions. Conclusion A knowledge-based RapidPlan model was trained and validated for IMPT. The results demonstrate that RapidPlan can be trained adequately for IMPT in HCC. The quality of the RapidPlan-based plans is at least equivalent compared to what is achievable with manual planning. 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| author |
Cozzi, Luca |
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Cozzi, Luca ddc 610 bkl 44.81 bkl 44.64 misc Intensity-modulated proton therapy misc Volumetric modulated arc therapy misc RapidArc misc Hepatocellular cancer misc Machine learning The role of a knowledge based dose–volume histogram predictive model in the optimisation of intensity-modulated proton plans for hepatocellular carcinoma patients |
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610 ASE 44.81 bkl 44.64 bkl The role of a knowledge based dose–volume histogram predictive model in the optimisation of intensity-modulated proton plans for hepatocellular carcinoma patients Intensity-modulated proton therapy (dpeaa)DE-He213 Volumetric modulated arc therapy (dpeaa)DE-He213 RapidArc (dpeaa)DE-He213 Hepatocellular cancer (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 |
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ddc 610 bkl 44.81 bkl 44.64 misc Intensity-modulated proton therapy misc Volumetric modulated arc therapy misc RapidArc misc Hepatocellular cancer misc Machine learning |
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ddc 610 bkl 44.81 bkl 44.64 misc Intensity-modulated proton therapy misc Volumetric modulated arc therapy misc RapidArc misc Hepatocellular cancer misc Machine learning |
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ddc 610 bkl 44.81 bkl 44.64 misc Intensity-modulated proton therapy misc Volumetric modulated arc therapy misc RapidArc misc Hepatocellular cancer misc Machine learning |
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The role of a knowledge based dose–volume histogram predictive model in the optimisation of intensity-modulated proton plans for hepatocellular carcinoma patients |
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The role of a knowledge based dose–volume histogram predictive model in the optimisation of intensity-modulated proton plans for hepatocellular carcinoma patients |
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Cozzi, Luca |
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Strahlentherapie und Onkologie |
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Cozzi, Luca Vanderstraeten, Reynald Fogliata, Antonella Chang, Feng-Ling Wang, Po-Ming |
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Cozzi, Luca |
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role of a knowledge based dose–volume histogram predictive model in the optimisation of intensity-modulated proton plans for hepatocellular carcinoma patients |
| title_auth |
The role of a knowledge based dose–volume histogram predictive model in the optimisation of intensity-modulated proton plans for hepatocellular carcinoma patients |
| abstract |
Purpose To investigate the performance of a knowledge-based RapidPlan, for optimisation of intensity-modulated proton therapy (IMPT) plans applied to hepatocellular cancer (HCC) patients. Methods A cohort of 65 patients was retrospectively selected: 50 were used to “train” the model, while the remaining 15 provided independent validation. The performance of the RapidPlan model was benchmarked against manual optimisation and was also compared to volumetric modulated arc therapy (RapidArc) photon plans. A subanalysis appraised the performance of the RapidPlan model applied to patients with lesions ≤300 $ cm^{3} $ or larger. Quantitative assessment was based on several metrics derived from the constraints of the NRG-GI003 clinical trial. Results There was an equivalence between manual plans and RapidPlan-optimised IMPT plans, which outperformed the RapidArc plans. The planning dose–volume objectives were met on average for all structures except for $ D_{0.5} $ cm3 ≤30 Gy in the bowels. Limiting the results to the class-solution proton plans (all values in Gy), the data for manual plans vs RapidPlan-based IMPT plans, respectively, showed the following: $ D_{99%} $ to the target of 47.5 ± 1.4 vs 47.2 ± 1.2; for organs at risk, the mean dose to the healthy liver was 6.7 ± 3.6 vs 6.7 ± 3.7; the mean dose to the kidneys was 0.2 ± 0.5 vs 0.1 ± 0.2; $ D_{0.5} $ cm3 for the bowels was 33.4 ± 16.4 vs 30.2 ± 16.0; for the stomach was 17.9 ± 19.9 vs 14.9 ± 18.8; for the oesophagus was 17.9 ± 15.1 vs 14.9 ± 13.9; for the spinal cord was 0.5 ± 1.6 vs 0.2 ± 0.7. The model performed similarly for cases with small or large lesions. Conclusion A knowledge-based RapidPlan model was trained and validated for IMPT. The results demonstrate that RapidPlan can be trained adequately for IMPT in HCC. The quality of the RapidPlan-based plans is at least equivalent compared to what is achievable with manual planning. RapidPlan also confirmed the potential to optimise the quality of the proton therapy results, thus reducing the impact of operator planning skills on patient results. |
| abstractGer |
Purpose To investigate the performance of a knowledge-based RapidPlan, for optimisation of intensity-modulated proton therapy (IMPT) plans applied to hepatocellular cancer (HCC) patients. Methods A cohort of 65 patients was retrospectively selected: 50 were used to “train” the model, while the remaining 15 provided independent validation. The performance of the RapidPlan model was benchmarked against manual optimisation and was also compared to volumetric modulated arc therapy (RapidArc) photon plans. A subanalysis appraised the performance of the RapidPlan model applied to patients with lesions ≤300 $ cm^{3} $ or larger. Quantitative assessment was based on several metrics derived from the constraints of the NRG-GI003 clinical trial. Results There was an equivalence between manual plans and RapidPlan-optimised IMPT plans, which outperformed the RapidArc plans. The planning dose–volume objectives were met on average for all structures except for $ D_{0.5} $ cm3 ≤30 Gy in the bowels. Limiting the results to the class-solution proton plans (all values in Gy), the data for manual plans vs RapidPlan-based IMPT plans, respectively, showed the following: $ D_{99%} $ to the target of 47.5 ± 1.4 vs 47.2 ± 1.2; for organs at risk, the mean dose to the healthy liver was 6.7 ± 3.6 vs 6.7 ± 3.7; the mean dose to the kidneys was 0.2 ± 0.5 vs 0.1 ± 0.2; $ D_{0.5} $ cm3 for the bowels was 33.4 ± 16.4 vs 30.2 ± 16.0; for the stomach was 17.9 ± 19.9 vs 14.9 ± 18.8; for the oesophagus was 17.9 ± 15.1 vs 14.9 ± 13.9; for the spinal cord was 0.5 ± 1.6 vs 0.2 ± 0.7. The model performed similarly for cases with small or large lesions. Conclusion A knowledge-based RapidPlan model was trained and validated for IMPT. The results demonstrate that RapidPlan can be trained adequately for IMPT in HCC. The quality of the RapidPlan-based plans is at least equivalent compared to what is achievable with manual planning. RapidPlan also confirmed the potential to optimise the quality of the proton therapy results, thus reducing the impact of operator planning skills on patient results. |
| abstract_unstemmed |
Purpose To investigate the performance of a knowledge-based RapidPlan, for optimisation of intensity-modulated proton therapy (IMPT) plans applied to hepatocellular cancer (HCC) patients. Methods A cohort of 65 patients was retrospectively selected: 50 were used to “train” the model, while the remaining 15 provided independent validation. The performance of the RapidPlan model was benchmarked against manual optimisation and was also compared to volumetric modulated arc therapy (RapidArc) photon plans. A subanalysis appraised the performance of the RapidPlan model applied to patients with lesions ≤300 $ cm^{3} $ or larger. Quantitative assessment was based on several metrics derived from the constraints of the NRG-GI003 clinical trial. Results There was an equivalence between manual plans and RapidPlan-optimised IMPT plans, which outperformed the RapidArc plans. The planning dose–volume objectives were met on average for all structures except for $ D_{0.5} $ cm3 ≤30 Gy in the bowels. Limiting the results to the class-solution proton plans (all values in Gy), the data for manual plans vs RapidPlan-based IMPT plans, respectively, showed the following: $ D_{99%} $ to the target of 47.5 ± 1.4 vs 47.2 ± 1.2; for organs at risk, the mean dose to the healthy liver was 6.7 ± 3.6 vs 6.7 ± 3.7; the mean dose to the kidneys was 0.2 ± 0.5 vs 0.1 ± 0.2; $ D_{0.5} $ cm3 for the bowels was 33.4 ± 16.4 vs 30.2 ± 16.0; for the stomach was 17.9 ± 19.9 vs 14.9 ± 18.8; for the oesophagus was 17.9 ± 15.1 vs 14.9 ± 13.9; for the spinal cord was 0.5 ± 1.6 vs 0.2 ± 0.7. The model performed similarly for cases with small or large lesions. Conclusion A knowledge-based RapidPlan model was trained and validated for IMPT. The results demonstrate that RapidPlan can be trained adequately for IMPT in HCC. The quality of the RapidPlan-based plans is at least equivalent compared to what is achievable with manual planning. RapidPlan also confirmed the potential to optimise the quality of the proton therapy results, thus reducing the impact of operator planning skills on patient results. |
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| title_short |
The role of a knowledge based dose–volume histogram predictive model in the optimisation of intensity-modulated proton plans for hepatocellular carcinoma patients |
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https://dx.doi.org/10.1007/s00066-020-01664-2 |
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Vanderstraeten, Reynald Fogliata, Antonella Chang, Feng-Ling Wang, Po-Ming |
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Vanderstraeten, Reynald Fogliata, Antonella Chang, Feng-Ling Wang, Po-Ming |
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312407866 |
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| doi_str |
10.1007/s00066-020-01664-2 |
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2024-07-03T19:38:17.883Z |
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| score |
7.402915 |