Clinical trial NCT06317948: 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)
Official title: Improving the Quality of Radiotherapy by Multi-Institution Knowledge-Based Planning Optimization Models
Study type: OBSERVATIONAL
Overall status: ENROLLING_BY_INVITATION
Conditions: Breast Cancer
Prostate Cancer
Lead sponsor: IRCCS Ospedale San Raffaele
Sponsor class: OTHER
First posted: 2024-03-19
Enrollment: 1000
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: prostate cancer, breast cancer and for selected SBRT situations (spine and prostate, according to RTOG 0631 and 0938 schemes respectively).

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.

Eligibility criteria: 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:

-

Primary outcomes:
measure: model interchangeability
timeFrame: 3 years
Secondary outcomes:
Other outcomes:
Locations:
city: Milano
country: Italy
facility: IRCCS Ospedale San Raffaele
Study officials:
affiliation: IRCCS Ospedale San Raffaele
name: Claudio Fiorino, Msc
role: PRINCIPAL_INVESTIGATOR
References:
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.
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.
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.
citation: 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.