A Study to Develop Molecular Integrated Predictive Models of Breast Radio-toxicity (Precise-RTox)
- Conditions
- Breast Cancer
- Registration Number
- NCT06114589
- Lead Sponsor
- Centro di Riferimento Oncologico - Aviano
- 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.
Recruitment & Eligibility
- Status
- RECRUITING
- Sex
- Female
- Target Recruitment
- 420
- 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.
- 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.
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Generation of a predictive model for actinic fibrosis. 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 Outcome Measures
Name Time Method Generation of a predictive model for aesthetic outcome up to 2 years after start of treatment Specificity of a model combining different variables to predict aesthetic outcome defined as fair/poor, according to Harvard score
Generation of a predictive model for acute skin toxicity up to 2 years after start of treatment Specificity of a model combining different variables to predict acute skin toxicity defined according to CTCAE scale v4.0 as dermatitis grade ≥2 or ulceration of the skin of grade ≥2
Generation of a predictive model for late skin toxicity up to 2 years after start of treatment Specificity of a model combining different variables to predict late skin toxicity defined according to CTCAE scale v4.0 as grade 2 telangiectasia or grade 2 hyperpigmentation
Generation of a predictive model for acute pain up to 2 years after start of treatment Specificity of a model combining different variables to predict acute pain of grade ≥2 defined according to CTCAE scale v4.0
Generation of a predictive model for fatigue up to 2 years after start of treatment Specificity of a model combining different variables to predict fatigue of grade ≥2 defined according to CTCAE scale v4.0
Generation of a predictive model for hypothyroidism up to 2 years after start of treatment Specificity of a model combining different variables to predict hypothyroidism of grade ≥2 defined according to CTCAE v4.0
Comparison between toxicity risk in treatment plans using protons or photons up to 2 years after start of treatment Difference in frequency of high risk toxicity between treatment plans using protons or photons
Generation of a predictive model for chronic pain up to 2 years after start of treatment Specificity of a model combining different variables to predict chronic pain of grade ≥2 defined according to CTCAE scale v4.0
Generation of a predictive model for cardiotoxicity up to 2 years after start of treatment Specificity of a model combining different variables to predict grade ≥2 cardiovascular events defined according to CTCAE v4.0
Generation of a predictive model for lymphedema up to 2 years after start of treatment Specificity of a model combining different variables to predict ipsilateral limb lymphedema of grade ≥2 defined according to CTCAE v4.0
Generation of a predictive model for contra-lateral breast cancer up to 2 years after start of treatment Specificity of a model combining different variables to predict secondary neoplasia to the contra-lateral breast according to CTCAE v4.0
Trial Locations
- Locations (1)
Centro di Riferimento Oncologico (CRO) di Aviano - IRCCS
🇮🇹Aviano, Pordenone, Italy