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) (MIKAPOCo)

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

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.

Study Overview

• Status: Enrolling by invitation
• Conditions: Breast Cancer; Prostate Cancer
• Intervention / Treatment: Other: treatment plan comparison

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.

• Study Type: Observational
• Enrollment (Estimated): 1000

Contacts and Locations

Study Locations

• Italy
  • Milano, Italy, 20133
    • IRCCS Ospedale San Raffaele

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No

• 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).

Description

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:

-

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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

Collaborators and Investigators

• Sponsor: IRCCS Ospedale San Raffaele
• Investigators: Principal Investigator: Claudio Fiorino, Msc, IRCCS Ospedale San Raffaele

Publications and helpful links

General Publications

• 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.
• 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.
• 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.
• 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.

Study record dates

Study Major Dates

• Study Start (Actual): October 28, 2022
• Primary Completion (Actual): October 28, 2022
• Study Completion (Estimated): October 28, 2025

Study Registration Dates

• First Submitted: March 12, 2024
• First Submitted That Met QC Criteria: March 12, 2024
• First Posted (Actual): March 19, 2024

Study Record Updates

• Last Update Posted (Actual): March 20, 2024
• Last Update Submitted That Met QC Criteria: March 19, 2024
• Last Verified: March 1, 2024

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Neoplasms; Urogenital Neoplasms; Neoplasms by Site; Genital Neoplasms, Male; Prostatic Diseases; Urogenital Diseases; Male Urogenital Diseases; Genital Diseases, Male; Genital Diseases; Prostatic Neoplasms
• Other Study ID Numbers: IG23150

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No