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Improving the Quality of Radiotherapy by Multi-Institution Knowledge-Based Planning Optimization Models (Acronym: MIKAPOCo, Multi-Institutional Knowledge-based Approach in Plan Optimization for the Community)

Improving the Quality of Radiotherapy by Multi-Institution Knowledge-Based Planning Optimization Models

Status
Enrolling by invitation
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT06317948
Acronym
MIKAPOCo
Enrollment
1000
Registered
2024-03-19
Start date
2022-10-28
Completion date
2025-10-28
Last updated
2024-03-20

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Breast Cancer, Prostate Cancer

Brief summary

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.

Detailed description

Major aims 1. To create libraries of consistently generated KB models for patients treated with RT for breast and prostate cancer and for selected stereotactic-body RT (SBRT) applications based on the experience of many Italian Institutions; to quantify planning inter-institute variability in homogeneous classes of patients. 2. To group models based on their characteristics and interchangeability. To assess groups of highly interchangeable models to be considered for multi-institutional dose-volume histogram (DVH) prediction purposes.

Interventions

OTHERtreatment plan comparison

In order to assess inter-Institute variability of DVH prediction of the various models, for the different situations and the different OARs, DVH and dose statistics (min, mean, median, max and SD of the dose received by each OAR) predicted on the patients owning to the different centers by the different models will be compared

Sponsors

IRCCS Ospedale San Raffaele
Lead SponsorOTHER

Study design

Observational model
COHORT
Time perspective
RETROSPECTIVE

Eligibility

Sex/Gender
ALL
Healthy volunteers
No

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

\-

Design outcomes

Primary

MeasureTime frameDescription
model interchangeability3 yearsinterchangeability 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.

Countries

Italy

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026