An Observational Study to Develop Molecular Integrated Predictive Models of Breast Radio-toxicity (Precise-RTox)
Trial Snapshot
- Phase
- Not Applicable
- Status
- Recruiting
- Enrollment
- 420
- Locations
- 1
- Primary Endpoint
- Generation of a predictive model for actinic fibrosis.
Study Overview
Brief Summary
Breast radiation treatment is burdened by acute and chronic toxicities, in most cases mild. However, considering the excellent life expectancy of patients with breast cancer, maintaining a low toxicity profile is of primary importance in order to guarantee a satisfactory quality of life. The definition of the molecular and genetic variables related to radiotoxicity and their integration into predictive molecular signatures may allow the risk of toxicity to be individualized. This would provide the clinician with a useful tool in order to personalize the radiation treatment, thus being able to choose the best technique or schedule for each patient.
Detailed Description
Breast radiation treatment is burdened by acute and chronic toxicities, in most cases mild. However, considering the excellent life expectancy of patients with breast cancer, maintaining a low toxicity profile is of primary importance in order to guarantee a satisfactory quality of life. Currently there are numerous predictive models of toxicity (Normal Tissue Complication Probability, NTCP) which are based on dosimetric and sometimes also clinical data. To date, they do not include individual genetic variability. However, it is believed that inter-individual variability may be responsible for up to 40% of actinic toxicity. Multiparametric models that consider genetics, dose and clinical aspects probably better reflect the complexity of radiotoxicity than models that rely on a single parameter and it is possible to integrate such parameters using a machine learning approach. The definition of the molecular and genetic variables related to radiotoxicity and their integration into predictive molecular signatures would therefore allow the risk to be individualized. This would provide the clinician with a useful tool in order to personalize the radiation treatment, thus being able to choose the best technique or schedule for each patient.
Study Design
- Study Type
- Observational
- Observational Model
- Cohort
- Time Perspective
- Prospective
Eligibility Criteria
- Ages
- 18 Years to — (Adult, Older Adult)
- Sex
- Female
- Accepts Healthy Volunteers
- No
Inclusion Criteria
- •Age ≥18 years;
- •Ability to express appropriate informed consent to treatment;
- •Distant nonmetastatic breast cancer;
- •Histology: infiltrating NST(no special type)/lobular carcinoma or ductal carcinoma in situ;
- •Stage: pTis; pT1-3 pN1-3 M0;
- •Hormone receptors, HER-2 status: Any;
- •Breast-conserving surgery. Both the sentinel lymph node biopsy and axillary lymphadenectomy. Negative surgical margins.
- •Candidates for postoperative radiation treatment.
Exclusion Criteria
- •Refusal of radiotherapy treatment (i.e., absence of signed informed consent);
- •Previous radiation therapy at the same site;
- •Concomitant chemotherapy with anthracyclines or taxanes;
- •Inability to maintain treatment position;
- •Partial breast radiotherapy (PBI);
- •Male breast cancer;
- •Mastectomy surgery.
Outcomes
Primary Outcomes
Generation of a predictive model for actinic fibrosis.
Time Frame: up to 2 years after start of treatment
Identification of a predictive model of actinic fibrosis in the breast, with sensitivity of at least 75% and specificity of 90%. Fibrosis is defined as grade ≥2 (CTCAE v 4.0) or skin induration as grade ≥2 defined according to CTCAE v 4.0 .
Secondary Outcomes
- Generation of a predictive model for aesthetic outcome(up to 2 years after start of treatment)
- Generation of a predictive model for acute skin toxicity(up to 2 years after start of treatment)
- Generation of a predictive model for late skin toxicity(up to 2 years after start of treatment)
- Generation of a predictive model for acute pain(up to 2 years after start of treatment)
- Generation of a predictive model for fatigue(up to 2 years after start of treatment)
- Generation of a predictive model for hypothyroidism(up to 2 years after start of treatment)
- Comparison between toxicity risk in treatment plans using protons or photons(up to 2 years after start of treatment)
- Generation of a predictive model for chronic pain(up to 2 years after start of treatment)
- Generation of a predictive model for cardiotoxicity(up to 2 years after start of treatment)
- Generation of a predictive model for lymphedema(up to 2 years after start of treatment)
- Generation of a predictive model for contra-lateral breast cancer(up to 2 years after start of treatment)