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.
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;
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;
Women with distant non-metastatic breast cancer candidated to radiotherapy after conservative surgery
Cohort
Prospective
Not listed
Generation of a predictive model for actinic fibrosis.
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 .
Generation of a predictive model for acute skin toxicity
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 late skin toxicity
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
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
Sensitivity of a model combining different variables to predict chronic pain grade ≥2 defined according to CTCAE scale v4.0
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