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)

NCT06317948 | Enrolling By Invitation

• 1 other identifier: IG23150
• Study Type: observational
• Target: 1,000
• Locations: 1 country
• Sites: 1

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.

Conditions

Breast Cancer; Prostate Cancer

Outcome Measures

Primary Outcomes (1)

• 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

Interventions

treatment plan comparison OTHER

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

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• 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).

You may qualify if:

• 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.

Sponsors & Collaborators

• IRCCS Ospedale San Raffaele: lead

Study Sites (1)

IRCCS Ospedale San Raffaele

Milan, 20133, Italy

Location

Related Publications (5)

• 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.

  PMID: 35814259 BACKGROUND

• 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.

  PMID: 35868603 BACKGROUND

• 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.

  PMID: 37196603 BACKGROUND

• 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.

  PMID: 34504790 BACKGROUND

• Placidi L, Griffin P, Castriconi R, Tudda A, Benecchi G, Burns M, Cagni E, Markham C, Landoni V, Moretti E, Oliviero C, Guidasci GR, Meffe G, Rancati T, Scaggion A, McGoldrick K, Panettieri V, Fiorino C. An International Inter-Consortium Validation of Knowledge-Based Plan Prediction Modeling for Whole Breast Radiotherapy Treatment. Cancers (Basel). 2025 Nov 5;17(21):3576. doi: 10.3390/cancers17213576.

  PMID: 41228368 DERIVED

MeSH Terms

Conditions

Breast Neoplasms; Prostatic Neoplasms

Condition Hierarchy (Ancestors)

Neoplasms by Site; Neoplasms; Breast Diseases; Skin Diseases; Skin and Connective Tissue Diseases; Genital Neoplasms, Male; Urogenital Neoplasms; Genital Diseases, Male; Genital Diseases; Urogenital Diseases; Prostatic Diseases; Male Urogenital Diseases

Study Officials

• Claudio Fiorino, Msc

  IRCCS Ospedale San Raffaele

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: March 12, 2024
• First Posted: March 19, 2024
• Study Start: October 28, 2022
• Primary Completion: October 28, 2022
• Study Completion: October 28, 2025
• Last Updated: March 20, 2024

Record last verified: 2024-03

Data Sharing

• IPD Sharing: Will not share

Locations

IT (1)