Breast Cancer Clinical Trial
— MIKAPOCoOfficial title:
Improving the Quality of Radiotherapy by Multi-Institution Knowledge-Based Planning Optimization Models
Verified date | March 2024 |
Source | IRCCS Ospedale San Raffaele |
Contact | n/a |
Is FDA regulated | No |
Health authority | |
Study type | Observational |
Investigators central hypothesis is that it is possible to create libraries of "consistent" Knowledge-Based plan-models derived from large Institutional experiences. These libraries can be used to guide automated RT planning and serve as tools to assist centers for plan quality assurance (QA) and plan prediction. Quantifying Inter-institute variability of RT planning and building libraries of interchangeable and validated multi-Institutional KB plan prediction models is expected to impact on the quality of planning at the national level. The project has the potential of facilitating the introduction of AI approaches in plan optimization, thus reducing intra and inter-Institute planning variability. Improving plan quality is expected to translate into better outcome after RT in terms of local control and, even more, of side effects and Quality of life. Positive impact is also expected in patient selection for advanced techniques, in plan audit and plan optimization in clinical trials, in technology comparison and cost-benefit analyses as well as in the RT educational field.
Status | Enrolling by invitation |
Enrollment | 1000 |
Est. completion date | October 28, 2025 |
Est. primary completion date | October 28, 2022 |
Accepts healthy volunteers | No |
Gender | All |
Age group | N/A and older |
Eligibility | Inclusion Criteria: - real life consecutive (or randomly chosen) plan data of patients treated for prostate cancer during the last 10 years; - real life consecutive (or randomly chosen) plan data of patients treated for breast cancer during the last 10 years; - real life consecutive (or randomly chosen) plan data of patients treated for selected SBRT situations (spine and prostate, according to RTOG 0631 and 0938 schemes respectively) during the last 10 years. Exclusion Criteria: - |
Country | Name | City | State |
---|---|---|---|
Italy | IRCCS Ospedale San Raffaele | Milano |
Lead Sponsor | Collaborator |
---|---|
IRCCS Ospedale San Raffaele |
Italy,
Castriconi R, Esposito PG, Tudda A, Mangili P, Broggi S, Fodor A, Deantoni CL, Longobardi B, Pasetti M, Perna L, Del Vecchio A, Di Muzio NG, Fiorino C. Replacing Manual Planning of Whole Breast Irradiation With Knowledge-Based Automatic Optimization by Virtual Tangential-Fields Arc Therapy. Front Oncol. 2021 Aug 24;11:712423. doi: 10.3389/fonc.2021.712423. eCollection 2021. — View Citation
Esposito PG, Castriconi R, Mangili P, Broggi S, Fodor A, Pasetti M, Tudda A, Di Muzio NG, Del Vecchio A, Fiorino C. Knowledge-based automatic plan optimization for left-sided whole breast tomotherapy. Phys Imaging Radiat Oncol. 2022 Jun 23;23:54-59. doi: 10.1016/j.phro.2022.06.009. eCollection 2022 Jul. — View Citation
Monticelli D, Castriconi R, Tudda A, Fodor A, Deantoni C, Gisella Di Muzio N, Mangili P, Del Vecchio A, Fiorino C, Broggi S. Knowledge-based plan optimization for prostate SBRT delivered with CyberKnife according to RTOG0938 protocol. Phys Med. 2023 Jun;110:102606. doi: 10.1016/j.ejmp.2023.102606. Epub 2023 May 15. — View Citation
Tudda A, Castriconi R, Benecchi G, Cagni E, Cicchetti A, Dusi F, Esposito PG, Guernieri M, Ianiro A, Landoni V, Mazzilli A, Moretti E, Oliviero C, Placidi L, Rambaldi Guidasci G, Rancati T, Scaggion A, Trojani V, Fiorino C. Knowledge-based multi-institution plan prediction of whole breast irradiation with tangential fields. Radiother Oncol. 2022 Oct;175:10-16. doi: 10.1016/j.radonc.2022.07.012. Epub 2022 Jul 19. — View Citation
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | model interchangeability | interchangeability will be assessed by considering: a) the fraction of patients identified as "anatomy outlier" (in terms of out of the geometric features (GF) boundary of each single model) once the model coming from Institute X is applied to patients of Institute Y (modX-Y) and vice-versa (modY-X); b) the relative differences in DVH predictions between modX-Y and modY-X, including and not including the previously recognized "GF outlier" patients. Based on these results and on their clinical interpretation, sub-groups of KB-models with "high" interchangeability will be tentatively identified and the relationships between GF and interchangeability quantified. | 3 years |
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