Breast Cancer(Precise-RTox)NCT06114589
A Study to Develop Molecular Integrated Predictive Models of Breast Radio-toxicity (Precise-RTox)
An observational study of Breast Cancer, run by Centro di Riferimento Oncologico - Aviano at 1 site in Italy.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older, women only
- Conditions the study is for
- Breast Cancer
- How many people
- 420 participants
- Where
- 1 siteItaly
- Study dates
- 25 Aug 2022to 30 Jun 2026
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- Women only
- Adult, Older adult
Study population: Women with distant non-metastatic breast cancer candidated to radiotherapy after conservative surgery
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
Italy1 site, 1 recruiting
- Recruiting
Centro di Riferimento Oncologico (CRO) di Aviano - IRCCS
Aviano, Pordenone, 33081
Lorenzo Vinante, MD, +390434659855, lorenzo.vinante@cro.it
Lorenzo Vinante, MD
About this study
Official title: An Observational Study to Develop Molecular Integrated Predictive Models of Breast Radio-toxicity (Precise-RTox)
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.
Read the 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.
- Conditions
- Breast Cancer
- Medical subject headings
- Breast Neoplasms
What is being measured
The primary outcome is the main question the study is designed to answer.
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 .
Measured: up to 2 years after start of treatment
Secondary outcomes (23)
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
Measured: up to 2 years after start of treatment
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
Measured: up to 2 years after start of treatment
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
Measured: up to 2 years after start of treatment
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
Measured: up to 2 years after start of treatment
Generation of a predictive model for fatigue
Sensitivity of a model combining different variables to predict fatigue of grade ≥2 defined according to CTCAE scale v4.0
Measured: up to 2 years after start of treatment
Generation of a predictive model for lymphedema
Sensitivity of a model combining different variables to predict ipsilateral limb lymphedema of grade ≥2 defined according to CTCAE v4.0
Measured: up to 2 years after start of treatment
Generation of a predictive model for hypothyroidism
Sensitivity of a model combining different variables to predict hypothyroidism of grade ≥2 defined according to CTCAE v4.0
Measured: up to 2 years after start of treatment
Generation of a predictive model for contra-lateral breast cancer
Sensitivity of a model combining different variables to predict secondary neoplasia to the contra-lateral breast according to CTCAE v4.0
Measured: up to 2 years after start of treatment
Generation of a predictive model for cardiotoxicity
Sensitivity of a model combining different variables to predict cardiotoxicity defined as reduction at echocardiography of Global Longitudinal Strain (GLS) ≥10% compared to baseline
Measured: up to 2 years after start of treatment
Generation of a predictive model for cardiotoxicity
Sensitivity of a model combining different variables to predict grade ≥2 cardiovascular events defined according to CTCAE v4.0
Measured: up to 2 years after start of treatment
Generation of a predictive model for aesthetic outcome
Sensitivity of a model combining different variables to predict aesthetic outcome defined as fair/poor, according to Harvard score
Measured: up to 2 years after start of treatment
Generation of a predictive model for acute skin toxicity
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
Measured: up to 2 years after start of treatment
Generation of a predictive model for late skin toxicity
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
Measured: up to 2 years after start of treatment
Generation of a predictive model for acute pain
Specificity of a model combining different variables to predict acute pain of grade ≥2 defined according to CTCAE scale v4.0
Measured: up to 2 years after start of treatment
Generation of a predictive model for chronic pain
Specificity of a model combining different variables to predict chronic pain of grade ≥2 defined according to CTCAE scale v4.0
Measured: up to 2 years after start of treatment
Generation of a predictive model for fatigue
Specificity of a model combining different variables to predict fatigue of grade ≥2 defined according to CTCAE scale v4.0
Measured: up to 2 years after start of treatment
Generation of a predictive model for lymphedema
Specificity of a model combining different variables to predict ipsilateral limb lymphedema of grade ≥2 defined according to CTCAE v4.0
Measured: up to 2 years after start of treatment
Generation of a predictive model for hypothyroidism
Specificity of a model combining different variables to predict hypothyroidism of grade ≥2 defined according to CTCAE v4.0
Measured: up to 2 years after start of treatment
Generation of a predictive model for contra-lateral breast cancer
Specificity of a model combining different variables to predict secondary neoplasia to the contra-lateral breast according to CTCAE v4.0
Measured: up to 2 years after start of treatment
Generation of a predictive model for cardiotoxicity
Specificity of a model combining different variables to predict cardiotoxicity defined as reduction at echocardiography of Global Longitudinal Strain (GLS) ≥10% compared to baseline
Measured: up to 2 years after start of treatment
Generation of a predictive model for cardiotoxicity
Specificity of a model combining different variables to predict grade ≥2 cardiovascular events defined according to CTCAE v4.0
Measured: up to 2 years after start of treatment
Generation of a predictive model for aesthetic outcome
Specificity of a model combining different variables to predict aesthetic outcome defined as fair/poor, according to Harvard score
Measured: up to 2 years after start of treatment
Comparison between toxicity risk in treatment plans using protons or photons
Difference in frequency of high risk toxicity between treatment plans using protons or photons
Measured: up to 2 years after start of treatment
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted2 Nov 2023
- Study start25 Aug 2022
- Primary completion30 Jun 2026 (estimated)
- Study completion30 Jun 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Breast CancerStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 420 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Centro di Riferimento Oncologico - Aviano
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Lorenzo Vinante, MD, Principal investigator, Centro di Riferimento Oncologico di Aviano (CRO) - IRCCSBarbara Belletti, PhD, Principal investigator, Centro di Riferimento Oncologico di Aviano (CRO) - IRCCS
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06114589
- Sponsor's protocol ID
- CRO-2022-29
- Acronym
- (Precise-RTox)
- Status last verified
- Nov 2023
- First submitted
- 27 Oct 2023
- Last updated
- 7 Nov 2023
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 7 Nov 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- An Observational Study to Develop Molecular Integrated Predictive Models of Breast Radio-toxicity (Precise-RTox)
- ClinicalTrials.gov ID
- NCT06114589
- Acronym / sponsor ID
- (Precise-RTox) / CRO-2022-29
- Conditions
- Breast Cancer
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 420 participants (estimated)
- Lead sponsor
- Centro di Riferimento Oncologico - AvianoUniversity, hospital or other
- Study start
- 25 Aug 2022 (actual)
- Primary completion
- 30 Jun 2026 (estimated)
- Study completion
- 30 Jun 2026 (estimated)
- First posted
- 2 Nov 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- Female only
- Age groups
- Adult, Older adult
- Study population
- Women with distant non-metastatic breast cancer candidated to radiotherapy after conservative surgery
Key inclusion criteria (8 listed)
- 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;
Key exclusion criteria (7 listed)
- 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);
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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 .
23 secondary outcomes
Generation of a predictive model for acute skin toxicity
Time frame: up to 2 years after start of treatment
Generation of a predictive model for late skin toxicity
Time frame: up to 2 years after start of treatment
Generation of a predictive model for acute pain
Time frame: up to 2 years after start of treatment
Generation of a predictive model for chronic pain
Time frame: up to 2 years after start of treatment
Generation of a predictive model for fatigue
Time frame: up to 2 years after start of treatment
Generation of a predictive model for lymphedema
Time frame: up to 2 years after start of treatment
Generation of a predictive model for hypothyroidism
Time frame: up to 2 years after start of treatment
Generation of a predictive model for contra-lateral breast cancer
Time frame: up to 2 years after start of treatment
Generation of a predictive model for cardiotoxicity
Time frame: up to 2 years after start of treatment
Generation of a predictive model for cardiotoxicity
Time frame: up to 2 years after start of treatment
Generation of a predictive model for aesthetic outcome
Time frame: up to 2 years after start of treatment
Generation of a predictive model for acute skin toxicity
Time frame: up to 2 years after start of treatment
Generation of a predictive model for late skin toxicity
Time frame: up to 2 years after start of treatment
Generation of a predictive model for acute pain
Time frame: up to 2 years after start of treatment
Generation of a predictive model for chronic pain
Time frame: up to 2 years after start of treatment
Generation of a predictive model for fatigue
Time frame: up to 2 years after start of treatment
Generation of a predictive model for lymphedema
Time frame: up to 2 years after start of treatment
Generation of a predictive model for hypothyroidism
Time frame: up to 2 years after start of treatment
Generation of a predictive model for contra-lateral breast cancer
Time frame: up to 2 years after start of treatment
Generation of a predictive model for cardiotoxicity
Time frame: up to 2 years after start of treatment
Generation of a predictive model for cardiotoxicity
Time frame: up to 2 years after start of treatment
Generation of a predictive model for aesthetic outcome
Time frame: up to 2 years after start of treatment
Comparison between toxicity risk in treatment plans using protons or photons
Time frame: up to 2 years after start of treatment
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 7 Nov 2023.
Related trials for Breast Cancer
Active, not recruiting
Copper Cu 64-DOTA-Trastuzumab PET in Predicting Response to Treatment With Ado-Trastuzumab Emtansine in Patients With Metastatic HER2 Positive Breast Cancer
Bone Metastases, HER2-positive Breast Cancer
Active, not recruitingObservational study
A ReaL World Study of DS-8201
Breast Cancer Metastatic, Treatment Efficacy
RecruitingPhase 3
A Clinical Study of Patritumab Deruxtecan to Treat Breast Cancer (MK-1022-016)
Breast Neoplasms
RecruitingObservational study
Characterization of Extracellular Vesicles in Breast Cancer Patients
Breast Cancer
Not yet recruitingPhase 4
Evaluation of the Efficacy and Acceptability of a Phytotherapy Combining Devil's Claw, Blackcurrant and Meadowsweet for the Management of Aromatase Inhibitor-induced Arthralgia in Patients Treated for Localized Breast Cancer in the Adjuvant Setting
Hormone Therapy-induced Arthralgia, Phytotherapy
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Lorenzo Vinante, MD principal investigator
Centro di Riferimento Oncologico di Aviano (CRO) - IRCCS
Barbara Belletti, PhD principal investigator
Centro di Riferimento Oncologico di Aviano (CRO) - IRCCS
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Breast Cancer, run by Centro di Riferimento Oncologico - Aviano at 1 site in Italy.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 8 inclusion and 7 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 7 Nov 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Italy. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Lorenzo Vinante, MD (+390434659855, lorenzo.vinante@cro.it), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Generation of a predictive model for actinic fibrosis. (time frame: up to 2 years after start of treatment). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 7 Nov 2023.