An Observational Study to Develop Molecular Integrated Predictive Models of Breast Radio-toxicity (Precise-RTox)
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 420
- 试验地点
- 1
- 主要终点
- Generation of a predictive model for actinic fibrosis.
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- Female
- 接受健康志愿者
- 否
入选标准
- •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.
排除标准
- •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.
结局指标
主要结局
Generation of a predictive model for actinic fibrosis.
时间窗: 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 .
次要结局
- 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)