Breast CancerINFLAMMATENCT06447532
Use of Machine Learning Techniques for Serial Assessment of Systemic Inflammatory Markers in Breast Cancer Patients
An observational study of Breast Cancer, run by Federal University of São Paulo at 13 sites in 8 countries.
Trial summary
Breast cancer is the most common cancer in women globally, with 2.3 million new cases diagnosed in 2020. Hormone receptor positive (HR+), human epidermal growth factor receptor 2 negative (HER2-) breast cancer is the most prevalent subtype, comprising 69% of all breast cancers in the USA. Read more about this study
- Who is it for?
- Ages 18 to 75 years, women only
- Conditions the study is for
- Breast Cancer
- What is being tested?
- Surgery (Mastectomy or quadrantectomy)
- Type of treatment
- Surgery or procedure
- How many people
- 4,500 participants
- Where
- 13 sitesin 8 countries
- Study dates
- 1 Aug 2024to Feb 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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 to 75 years
- Women only
- No healthy volunteers
- Adult, Older adult
Study population: All the women involved in our study are patients who are diagnosed breast cancer pathologically and treated with surgery or neoadjuvant chemotherapy from January 1st 2013 to December 31st 2018.
Who may be able to join
- Women patients with age between 18 and 75 years old;
- Invasive breast carcinoma patients diagnosed by pathology ;
- Patients diagnosed between 1 January 2013 and 31 December 2018;
- Have a complete blood count performed before the surgical intervention (mastectomy or conservative breast surgery) or neoadjuvant chemotherapy;
Who cannot join
- Presence of hematological disorders;
- Bilateral breast cancer;
- Male;
- Karnofsky Performance Status Score < 70';
- Inflammatory breast cancer and in situ carcinoma;
- Pregnancy or breastfeeding;
- Evidence of local or distant recurrence.
Trial locations and contacts
13 sites in 8 countries. Contact the study team or a site near you to ask about taking part.
13 sites in 8 countries
Brazil5 sites
Idam Oliveira Junior
Barretos, São Paulo
César Cabello
Campinas, São Paulo
Tomás Reinert
Porto Alegre, Rio Grande do Sul
Daniel Guimaraes Tiezzi
Ribeirão Preto, São Paulo
Rosekeila Simoes Nomeline
Uberaba, Minas Gerais
About this study
Breast cancer is the most common cancer in women globally, with 2.3 million new cases diagnosed in 2020. Hormone receptor positive (HR+), human epidermal growth factor receptor 2 negative (HER2-) breast cancer is the most prevalent subtype, comprising 69% of all breast cancers in the USA. Within the tumor immune microenvironment, a higher intensity of myeloid cell infiltration and low levels of lymphocyte infiltration have been associated with worse outcomes. Markers in peripheral blood have emerged as predictive biomarkers that can be easily obtained non-invasively and at low cost. Experiments have confirmed the relative components of these tests (such as the immune cells) directly or indirectly participated in tumour occurrence, development, and immune escape, underscoring the potential use of laboratory tests as tumour biomarkers
Read the detailed description
In breast cancer, increased neutrophil levels and decreased lymphocyte levels in peripheral blood are associated with worse overall survival (OS). In HR+, HER2- metastatic breast cancers, low pretreatment NLR and high pretreatment absolute lymphocyte count (ALC) were related with better progression-free survival (PFS) and OS. The development of predictive models, based on machine learning (ML) algorithms it has been used in prognostication and assist in the diagnosis of different types of cancer.
Although regular laboratory tests have potential to be breast cancer biomarkers, a single test is yet to provide adequate sensitivity or specificity. Artificial intelligence (AI) could help with integrating data from multiple tests to aid diagnosis. Technical improvements such as data storage capacity, computing power, and better algorithms mean that ML can process clinically meaningful information from laboratory test data. Models' generalisability and stability still need to be confirmed, in view of limitations such as the absence of various pathological types, small cohorts, and lack of external validation. Therefore, a competitive model is also essential to achieve more accurate stratification of patients with breast cancer. The purpose of this retrospective multicentre study is to systematically evaluate the ability of laboratory tests to predict breast cancer, and develop a robust and generalisable model to assist in identifying patients with breast cancer.
- Conditions
- Breast Cancer
- Being tested
- Surgery (Mastectomy or quadrantectomy)
- Keywords
- breast cancer, machine learning, prognosis, inflammation
- Medical subject headings
- Breast Neoplasms, Inflammation
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Surgery (Mastectomy or quadrantectomy)Procedure or surgery
Also called Neoadjuvant chemotherapy
Surgery (mastectomy or quadrantectomy); Neoadjuvant chemotherapy
| Study group | Role | What they receive |
|---|---|---|
| Group I: Breast cancer | All the participants involved in our study are women who are diagnosed breast cancer and treated with surgery or neoadjuvant chemotherapy from January 1st 2013 to December 31st 2018. Receives: Procedure: Surgery (Mastectomy or quadrantectomy) |
What is being measured
The primary outcome is the main question the study is designed to answer.
Overall survival
Overall survival
Measured: From the date of diagnosis to the date of death, assessed up to 120 months
Secondary outcomes (1)
Disease free survival
Disease-free survival
Measured: From the date of diagnosis to the date of first progression (local recurrence of tumor or distant metastasis), assessed up to 60 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted7 Jun 2024
- Study start1 Aug 2024
- Primary completion31 Dec 2024
- Study completionFeb 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)Papers the sponsor has linked to this study.
- 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
- Retrospective
- Enrolment
- 4,500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Federal University of São Paulo
- Sponsor type
- University, hospital or other
- Collaborators
- Kansai Medical University, University of Sao Paulo, Kyoto University, Barretos Cancer Hospital, Women's College Hospital, Emory University, University of Campinas, Brazil, Centro de Educación Medica e Investigaciones Clínicas Norberto Quirno, Instituto Nacional de Cancer, Brazil, Universidade Federal do Triangulo Mineiro, Instituto de Cardiología y Medicina Vascular Hospital Zambrano-Hellion Tec Salud, Hospital Vall d'Hebron, Mansoura University, Seoul National University
- Responsible party
- Principal investigator, Afonso Celso Pinto Nazario, Federal University of São Paulo
- Investigators
- Afonso C Nazario, PhD, Principal investigator, University Federal of Sao Paulo
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06447532
- Sponsor's protocol ID
- University of Sao Paulo
- Acronym
- INFLAMMATE
- Status last verified
- Jun 2024
- First submitted
- 26 Apr 2024
- Last updated
- 12 Mar 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021 May;71(3):209-249. doi: 10.3322/caac.21660. Epub 2021 Feb 4.
PubMed 33538338Faria SS, Giannarelli D, Cordeiro de Lima VC, Anwar SL, Casadei C, De Giorgi U, Madonna G, Ascierto PA, Mendoza Lopez RV, Chammas R, Capone M. Development of a Prognostic Model for Early Breast Cancer Integrating Neutrophil to Lymphocyte Ratio and Clinical-Pathological Characteristics. Oncologist. 2024 Apr 4;29(4):e447-e454. doi: 10.1093/oncolo/oyad303.
PubMed 37971409Choi E, Bahadori MT, Schuetz A, Stewart WF, Sun J. Doctor AI: Predicting Clinical Events via Recurrent Neural Networks. JMLR Workshop Conf Proc. 2016 Aug;56:301-318. Epub 2016 Dec 10.
PubMed 28286600
Study at a glance
From the registry record, last updated by the sponsor on 12 Mar 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Use of Machine Learning Techniques for Serial Assessment of Systemic Inflammatory Markers in Breast Cancer Patients
- ClinicalTrials.gov ID
- NCT06447532
- Acronym / sponsor ID
- INFLAMMATE / University of Sao Paulo
- Conditions
- Breast Cancer
- Keywords
- breast cancer, machine learning, prognosis, inflammation
- Study type
- Observational
- Recruitment status
- Enrolling by invitation
- Enrolment
- 4,500 participants (estimated)
- Lead sponsor
- Federal University of São PauloUniversity, hospital or other
- Collaborators
- Kansai Medical University, University of Sao Paulo, Kyoto University, Barretos Cancer Hospital, Women's College Hospital, Emory University, University of Campinas, Brazil, Centro de Educación Medica e Investigaciones Clínicas Norberto Quirno, Instituto Nacional de Cancer, Brazil, Universidade Federal do Triangulo Mineiro, Instituto de Cardiología y Medicina Vascular Hospital Zambrano-Hellion Tec Salud, Hospital Vall d'Hebron, Mansoura University, Seoul National University
- Study start
- 1 Aug 2024 (actual)
- Primary completion
- 31 Dec 2024 (actual)
- Study completion
- Feb 2027 (estimated)
- First posted
- 7 Jun 2024 (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 cancer is the most common cancer in women globally, with 2.3 million new cases diagnosed in 2020. Hormone receptor positive (HR+), human epidermal growth factor receptor 2 negative (HER2-) breast cancer is the most prevalent subtype, comprising 69% of all breast cancers in the USA. Within the tumor immune microenvironment, a higher intensity of myeloid cell infiltration and low levels of lymphocyte infiltration have been associated with worse outcomes. Markers in peripheral blood have emerged as predictive biomarkers that can be easily obtained non-invasively and at low cost. Experiments have confirmed the relative components of these tests (such as the immune cells) directly or indirectly participated in tumour occurrence, development, and immune escape, underscoring the potential use of laboratory tests as tumour biomarkers
Detailed description
In breast cancer, increased neutrophil levels and decreased lymphocyte levels in peripheral blood are associated with worse overall survival (OS). In HR+, HER2- metastatic breast cancers, low pretreatment NLR and high pretreatment absolute lymphocyte count (ALC) were related with better progression-free survival (PFS) and OS. The development of predictive models, based on machine learning (ML) algorithms it has been used in prognostication and assist in the diagnosis of different types of cancer.
Although regular laboratory tests have potential to be breast cancer biomarkers, a single test is yet to provide adequate sensitivity or specificity. Artificial intelligence (AI) could help with integrating data from multiple tests to aid diagnosis. Technical improvements such as data storage capacity, computing power, and better algorithms mean that ML can process clinically meaningful information from laboratory test data. Models' generalisability and stability still need to be confirmed, in view of limitations such as the absence of various pathological types, small cohorts, and lack of external validation. Therefore, a competitive model is also essential to achieve more accurate stratification of patients with breast cancer. The purpose of this retrospective multicentre study is to systematically evaluate the ability of laboratory tests to predict breast cancer, and develop a robust and generalisable model to assist in identifying patients with breast cancer.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Group I: Breast cancer | – | Procedure: Surgery (Mastectomy or quadrantectomy)All the participants involved in our study are women who are diagnosed breast cancer and treated with surgery or neoadjuvant chemotherapy from January 1st 2013 to December 31st 2018. |
Who may qualify
- Ages
- 18 to 75 years
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- All the women involved in our study are patients who are diagnosed breast cancer pathologically and treated with surgery or neoadjuvant chemotherapy from January 1st 2013 to December 31st 2018.
Key inclusion criteria (4 listed)
- Women patients with age between 18 and 75 years old;
- Invasive breast carcinoma patients diagnosed by pathology ;
- Patients diagnosed between 1 January 2013 and 31 December 2018;
- Have a complete blood count performed before the surgical intervention (mastectomy or conservative breast surgery) or neoadjuvant chemotherapy;
Key exclusion criteria (7 listed)
- Presence of hematological disorders;
- Bilateral breast cancer;
- Male;
- Karnofsky Performance Status Score < 70';
- Inflammatory breast cancer and in situ carcinoma;
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Surgery (Mastectomy or quadrantectomy)Also: Neoadjuvant chemotherapySurgery (mastectomy or quadrantectomy); Neoadjuvant chemotherapy | Procedure or surgery | Group I: Breast cancer |
Primary outcome
Overall survival
Time frame: From the date of diagnosis to the date of death, assessed up to 120 months
Overall survival
1 secondary outcome
Disease free survival
Time frame: From the date of diagnosis to the date of first progression (local recurrence of tumor or distant metastasis), assessed up to 60 months
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 12 Mar 2025.
- Background: PubMed 33538338Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021 May;71(3):209-249. doi: 10.3322/caac.21660. Epub 2021 Feb 4.
- Background: PubMed 37971409Faria SS, Giannarelli D, Cordeiro de Lima VC, Anwar SL, Casadei C, De Giorgi U, Madonna G, Ascierto PA, Mendoza Lopez RV, Chammas R, Capone M. Development of a Prognostic Model for Early Breast Cancer Integrating Neutrophil to Lymphocyte Ratio and Clinical-Pathological Characteristics. Oncologist. 2024 Apr 4;29(4):e447-e454. doi: 10.1093/oncolo/oyad303.
- Background: PubMed 28286600Choi E, Bahadori MT, Schuetz A, Stewart WF, Sun J. Doctor AI: Predicting Clinical Events via Recurrent Neural Networks. JMLR Workshop Conf Proc. 2016 Aug;56:301-318. Epub 2016 Dec 10.
- Individual participant dataSharing planned: No
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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.
Afonso C Nazario, PhD principal investigator
University Federal of Sao Paulo
13 sites in 8 countries: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Breast Cancer, run by Federal University of São Paulo at 13 sites in 8 countries.
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 4 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: enrolling by invitation, as of the sponsor's last update on 12 Mar 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 13 sites in Brazil, Japan, Argentina, Canada, Egypt and 3 more countries. 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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Overall survival (time frame: From the date of diagnosis to the date of death, assessed up to 120 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06447532, which also lists 3 linked publications.
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 12 Mar 2025.