PURPOSE/ OBJECTIVES: The purpose of this study is to dually evaluate the effectiveness of PlanIQ in predicting the viability and outcome of dosimetric planning in cases of complex re-irradiation as well as generating an equivalent plan through Pinnacle integration. The study also postulates that a possible strength of PlanIQ lies in mitigating pre-optimization uncertainties tied directly to dose overlap regions where re-irradiation is necessary. METHODS: A retrospective patient selection (n = 20) included a diverse range of re-irradiation cases to be planned using Pinnacle auto-planning with PlanIQ integration. A consistent planning template was developed and applied across all cases. Direct plan comparisons of manual plans against feasibility-produced plans were performed by physician(s) with dosimetry recording relevant proximal OAR and planning timeline data. RESULTS AND DISCUSSION: All re-irradiation cases were successfully predicted to be achievable per PlanIQ analyses with three cases (3/20) necessitating 95% target coverage conditions, previously exhibited in the manually planned counterparts, and determined acceptable under institutional standards. At the same time, PlanIQ consistently produced plans of equal or greater quality to the previously manually planned re-irradiation across all (20/20) trials (P = 0.05). Proximal OAR exhibited similar to slightly improved maximum point doses from feasibility-based planning with the largest advantages gained found within the subset of cranial and spine overlap cases, where improvements upward of 10.9% were observed. Mean doses to proximal tissues were found to be a statistically significant (P < 0.05) 5.0% improvement across the entire study. Documented planning times were markedly less than or equal to the time contributed to manual planning across all cases. CONCLUSION: Initial findings indicate that PlanIQ effectively provides the user clear feasibility feedback capable of facilitating decision-making on whether re-irradiation dose objectives and prescription dose coverage are possible at the onset of treatment planning thus eliminating possible trial and error associated with some manual planning. Introducing model-based prediction tools into planning of complex re-irradiation cases yielded positive outcomes on the final treatment plans.
PURPOSE/ OBJECTIVES: The purpose of this study is to dually evaluate the effectiveness of PlanIQ in predicting the viability and outcome of dosimetric planning in cases of complex re-irradiation as well as generating an equivalent plan through Pinnacle integration. The study also postulates that a possible strength of PlanIQ lies in mitigating pre-optimization uncertainties tied directly to dose overlap regions where re-irradiation is necessary. METHODS: A retrospective patient selection (n = 20) included a diverse range of re-irradiation cases to be planned using Pinnacle auto-planning with PlanIQ integration. A consistent planning template was developed and applied across all cases. Direct plan comparisons of manual plans against feasibility-produced plans were performed by physician(s) with dosimetry recording relevant proximal OAR and planning timeline data. RESULTS AND DISCUSSION: All re-irradiation cases were successfully predicted to be achievable per PlanIQ analyses with three cases (3/20) necessitating 95% target coverage conditions, previously exhibited in the manually planned counterparts, and determined acceptable under institutional standards. At the same time, PlanIQ consistently produced plans of equal or greater quality to the previously manually planned re-irradiation across all (20/20) trials (P = 0.05). Proximal OAR exhibited similar to slightly improved maximum point doses from feasibility-based planning with the largest advantages gained found within the subset of cranial and spine overlap cases, where improvements upward of 10.9% were observed. Mean doses to proximal tissues were found to be a statistically significant (P < 0.05) 5.0% improvement across the entire study. Documented planning times were markedly less than or equal to the time contributed to manual planning across all cases. CONCLUSION: Initial findings indicate that PlanIQ effectively provides the user clear feasibility feedback capable of facilitating decision-making on whether re-irradiation dose objectives and prescription dose coverage are possible at the onset of treatment planning thus eliminating possible trial and error associated with some manual planning. Introducing model-based prediction tools into planning of complex re-irradiation cases yielded positive outcomes on the final treatment plans.
Authors: Marco Esposito; Giulia Maggi; Carmelo Marino; Laura Bottalico; Elisabetta Cagni; Claudia Carbonini; Michelina Casale; Stefania Clemente; Valentina D'Alesio; David Fedele; Francesca Romana Giglioli; Valeria Landoni; Anna Martinotti; Roberta Nigro; Lidia Strigari; Elena Villaggi; Pietro Mancosu Journal: Phys Med Date: 2015-10-20 Impact factor: 2.685
Authors: Lawrence B Marks; Ellen D Yorke; Andrew Jackson; Randall K Ten Haken; Louis S Constine; Avraham Eisbruch; Søren M Bentzen; Jiho Nam; Joseph O Deasy Journal: Int J Radiat Oncol Biol Phys Date: 2010-03-01 Impact factor: 7.038
Authors: Saeed Ahmed; Benjamin Nelms; Dawn Gintz; Jimmy Caudell; Geoffrey Zhang; Eduardo G Moros; Vladimir Feygelman Journal: Med Phys Date: 2017-08-31 Impact factor: 4.071
Authors: Shuo Wang; Dandan Zheng; Chi Zhang; Rongtao Ma; Nathan R Bennion; Yu Lei; Xiaofeng Zhu; Charles A Enke; Sumin Zhou Journal: Med Dosim Date: 2017 Spring Impact factor: 1.482