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)

Breast Cancer Clinical Trial

— MIKAPOCo  

Official title:

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

Clinical Trial Details — Status: Enrolling by invitation

Administrative data

• NCT number: NCT06317948
• Other study ID #: IG23150
• Status: Enrolling by invitation
• Start date: October 28, 2022
• Est. completion date: October 28, 2025

Study information

• Verified date: March 2024
• Source: IRCCS Ospedale San Raffaele
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• 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: -

Related Conditions & MeSH terms

• Breast Cancer
• Prostate Cancer
• Prostatic Neoplasms

Intervention

Other:
• treatment 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

Locations

Country	Name	City	State
Italy	IRCCS Ospedale San Raffaele	Milano

Sponsors (1)

Lead Sponsor	Collaborator
IRCCS Ospedale San Raffaele

Country where clinical trial is conducted

Italy, 

References & Publications (4)

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

Outcome

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