C50
Conditions
Interventions
Group 1: A retrospective evaluation will be conducted on patients who were radio-oncologically treated and followed up at the Heidelberg University Hospital (Heidelberg site) for breast cancer. Patien
no additional study-related examinations or interventions will take place.
The evaluation will be primarily descriptive, with an intended sample size of approximately 100-200 patients. The correspondi
Sponsors
Universitätsklinikum Heidelberg
Eligibility
Sex/Gender
Female
Inclusion criteria
Inclusion criteria: atients diagnosed with breast cancer who received radiotherapy at the University Hospital Heidelberg (UKHD) in the Department of Radiooncology and Radiation Therapy between 01/2010 and 09/2025.
Exclusion criteria
Exclusion criteria: Patients with incomplete treatment or outcome data, those who did not undergo radiotherapy, or those with missing documentation of radiation planning will be excluded.
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Retrospective evaluation of the effectiveness (local control) and safety (toxicity) of radiation for breast cancers and their metastases in terms of patient-oriented individual therapy optimization. This should examine both the differences between various radiation strategies and planning procedures, as well as their impact on target volume coverage and sparing of critical organs. The main objective of this retrospective analysis is to identify patient- and tumor-specific factors (e.g., bilateral breast cancers) that influence and can predict the suitability for the application of innovative radiation techniques (e.g., DIBH technique) in the radiation therapy of breast cancer. | — |
Secondary
| Measure | Time frame |
|---|---|
| Additionally, clinical endpoints such as progression-free or relapse-free survival, local, locoregional, and systemic tumor control, as well as therapy-associated side effects and late toxicities, will be evaluated, and prognostic factors will be assessed. In particular, the impact of a scar boost technique in locally advanced, mastectomized patients on the oncological outcome will be examined. Moreover, analyses of imaging characteristics will be conducted to assess tumor size response and dynamics, as well as influencing factors for better estimation of therapy responders and non-responders. Various machine learning algorithms will be employed to develop predictive models. The aim is to identify complex relationships between patient characteristics, imaging and radiation parameters, and clinical endpoints. | — |
Countries
Germany
Contacts
Public ContactEva Meixner
Universitätsklinikum Heidelberg
Outcome results
None listed