Breast Cancer
Conditions
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.
Interventions
None listed
Sponsors
Centro di Riferimento Oncologico - Aviano
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
FEMALE
Age
18 Years to No maximum
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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
| Measure | Time frame | Description |
|---|---|---|
| Generation of a predictive model for late skin toxicity | up to 2 years after start of treatment | Sensitivity 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 | Sensitivity 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 chronic pain | up to 2 years after start of treatment | Sensitivity of a model combining different variables to predict chronic pain grade ≥2 defined according to CTCAE scale v4.0 |
| Generation of a predictive model for fatigue | up to 2 years after start of treatment | Sensitivity 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 lymphedema | up to 2 years after start of treatment | Sensitivity 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 acute skin toxicity | up to 2 years after start of treatment | Sensitivity 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 contra-lateral breast cancer | up to 2 years after start of treatment | Sensitivity of a model combining different variables to predict secondary neoplasia to the contra-lateral breast according to CTCAE v4.0 |
| Generation of a predictive model for cardiotoxicity | up to 2 years after start of treatment | Sensitivity of a model combining different variables to predict cardiotoxicity defined as reduction at echocardiography of Global Longitudinal Strain (GLS) ≥10% compared to baseline |
| Generation of a predictive model for aesthetic outcome | up to 2 years after start of treatment | Sensitivity of a model combining different variables to predict aesthetic outcome defined as fair/poor, according to Harvard score |
| 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 hypothyroidism | up to 2 years after start of treatment | Sensitivity of a model combining different variables to predict hypothyroidism of grade ≥2 defined according to CTCAE v4.0 |
Countries
Italy
Contacts
Primary ContactLorenzo Vinante, MD
Outcome results
None listed