Breast CancerNCT06001528
Serum and Tissue Metabolite-based Prediction of Sentinel Lymph Node Metastasis in Breast Cancer
An observational study of Breast Cancer and Lymph Node Metastasis, run by Shantou Central Hospital at 1 site in China.
Trial summary
Breast cancer is a malignant tumor with the highest morbidity and mortality among women worldwide. Accurate staging of axillary lymph nodes is critical for metastatic assessment and decisions regarding treatment modalities in breast cancer patient. Read more about this study
- Who is it for?
- Ages 18 years and older, women only
- Conditions the study is for
- Breast Cancer, Lymph Node Metastasis
- How many people
- 2,400 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2021to 31 Aug 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
- No healthy volunteers
- Adult, Older adult
Study population: Retrospective cohort: The study retrospectively collected data from 724 patients with early breast cancer. Prospective cohort: We expected the accuracy of our predictive model reached 96%, and given an estimated dropout rate of 10%. We needed to include at least 586 breast cancer in the prospective cohort. Therefore, we plan to prospectively enroll serum samples from 586 breast cancer patients to detect the abundance of metabolites and collect the radiological and pathological information from these patients for the following analysis.
Who may be able to join
- Pathological diagnosis of breast cancer
- No preoperative therapy including chemotherapy or endocrine therapy
- No distant metastasis
- Underwent mastectomy or breast-conserving surgery with sentinel lymph node biopsy
- Agreed to provide preoperative peripheral blood samples
- Had access to imaging, pathological and follow-up data for preoperative and postoperative evaluation of the disease
Who cannot join
- Neoadjuvant therapy
- Presence of distant metastasis at time of diagnosis
- Primary malignancies other than breast cancer
- Bilateral breast cancer or previous contralateral breast cancer
- Undergo modified radical surgery for breast cancer without sentinel lymph node biopsy
- Incomplete pathological data and follow-up data
- Pregnancy and other conditions determined by the investigator to be ineligible for inclusion in the study
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Shantou Central Hospital
Shantou, Guangdong
Xiaorong Lin, Dr., 13790891600, clarelynn_lin@163.com
About this study
Breast cancer is a malignant tumor with the highest morbidity and mortality among women worldwide. Accurate staging of axillary lymph nodes is critical for metastatic assessment and decisions regarding treatment modalities in breast cancer patient. Among patients who underwent sentinel lymph node biopsy, about 70 % of the patients had negative pathological results and in other words, these 70 % of the patients received unnecessary surgery. At present, imaging and pathological diagnosis is the main measure of lymph node metastasis in breast cancer. However, limitations remained. Artificial intelligence, including deep learning and machine learning algorithms, has emerged as a possible technique, which can make a more accuracy prediction through machine-based collection, learning and processing of previous information, especially in radiology and pathology-based diagnosis. With the intensification of the concept of precision medicine and the development of non-invasive technology, the investigators intend to use the artificial intelligence technology to develop a serum and tissue-based predictive model for sentinel lymph node metastasis diagnosis combined with imaging and pathological information, providing specific, efficient and non-invasive biological indicators for the monitoring and early intervention of lymph node metastasis in patient with breast cancer. Therefore, the investigators retrospectively include serum samples from early breast cancer patients undergoing sentinel lymph node biopsy, including a discovery cohort and a modeling cohort. Metabolites were detected and screened in the discovery cohort and then as the target metabolites for targeted detection in the modeling cohort. Combined with preoperative imaging and pathological information, a prediction model of breast cancer sentinel lymph node metastasis based on serum metabolites would be established. Subsequently, multi-center breast cancer patients will prospectively be included to verify the accuracy and stability of the model.
- Conditions
- Breast Cancer, Lymph Node Metastasis
- Keywords
- breast cancer, sentinel lymph node metastasis, metabolic reprogramming, artificial intelligence
- Medical subject headings
- Breast Neoplasms, Lymphatic Metastasis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Discovering cohort | Discovering cohort was used for the discovery and screening of metabolic differences. Two groups were included-SLN+ group and SLN- group, meaning the breast cancer patients with/without sentinel lymph node metastasis respectively. Abundance and distribution of serum and tissue metabolites in this cohort of patients would be observed. | |
| Modeling cohort | Modeling cohort refer to the cohort of patients included for targeted metabolites detection. Two groups were included-SLN+ group and SLN- group. Abundance and distribution of targeted metabolites in this cohort of patients would be detected, and a predictive model would be established using the data of this cohort. | |
| Validation cohort | Validation cohort means a cohort of patients included to validate the prediction model established in the modeling stage. Patients of validation cohort will be enrolled from several different hospitals. Also, it included SLN+ group and SLN- group. Abundance and distribution of targeted metabolites in this cohort of patients would be detected, and the accuracy and stability of prediction model will be verified in this cohort. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Metabolic difference detection
Serum metabolites difference between breast cancer patients with and without sentinel lymph node metastasis would be analyzed, and potential biological indicators found.
Measured: From January 01, 2021 to December 31, 2021
Predictive model establishment
Combined with preoperative imaging and pathological information, a predictive model of sentinel lymph node metastasis in breast cancer would be established based on the metabolic difference.
Measured: From January 01, 2022 to December 31, 2022
Predictive model validation
Verify the stability and accuracy of our model in larger cohorts and promote clinical translation.
Measured: From January 01, 2023 to December 31, 2023
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted21 Aug 2023
- Study start1 Jan 2021
- Primary completion31 Dec 2023 (estimated)
- Study completion31 Aug 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (8)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
- Other
- Enrolment
- 2,400 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Shantou Central Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Zhejiang Cancer Hospital, Sichuan Cancer Hospital and Research Institute, Shenshan Medical Center of Sun Yat-sen Memorial Hospital, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
- Responsible party
- Sponsor
- Investigators
- Xiaorong Lin, Dr., Study director, Shantou Central HospitalHai Hu, Pro., Principal investigator, Zhejiang Cancer HospitalZhiyong Wu, Dr., Principal investigator, Shantou Central Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06001528
- Sponsor's protocol ID
- XR Lin
- Status last verified
- Sep 2023
- First submitted
- 14 Aug 2023
- Last updated
- 28 Sep 2023
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- 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 33538338Xu Y, Su GH, Ma D, Xiao Y, Shao ZM, Jiang YZ. Technological advances in cancer immunity: from immunogenomics to single-cell analysis and artificial intelligence. Signal Transduct Target Ther. 2021 Aug 20;6(1):312. doi: 10.1038/s41392-021-00729-7.
PubMed 34417437Zhou H, Zhu L, Song J, Wang G, Li P, Li W, Luo P, Sun X, Wu J, Liu Y, Zhu S, Zhang Y. Liquid biopsy at the frontier of detection, prognosis and progression monitoring in colorectal cancer. Mol Cancer. 2022 Mar 25;21(1):86. doi: 10.1186/s12943-022-01556-2.
PubMed 35337361Richard V, Davey MG, Annuk H, Miller N, Kerin MJ. The double agents in liquid biopsy: promoter and informant biomarkers of early metastases in breast cancer. Mol Cancer. 2022 Apr 4;21(1):95. doi: 10.1186/s12943-022-01506-y.
PubMed 35379239Chayakulkheeree J, Pungrassami D, Prueksadee J. Performance of breast magnetic resonance imaging in axillary nodal staging in newly diagnosed breast cancer patients. Pol J Radiol. 2019 Oct 18;84:e413-e418. doi: 10.5114/pjr.2019.89690. eCollection 2019.
PubMed 31969959Alimirzaie S, Bagherzadeh M, Akbari MR. Liquid biopsy in breast cancer: A comprehensive review. Clin Genet. 2019 Jun;95(6):643-660. doi: 10.1111/cge.13514. Epub 2019 Feb 27.
PubMed 30671931Isaksen JL, Baumert M, Hermans ANL, Maleckar M, Linz D. Artificial intelligence for the detection, prediction, and management of atrial fibrillation. Herzschrittmacherther Elektrophysiol. 2022 Mar;33(1):34-41. doi: 10.1007/s00399-022-00839-x. Epub 2022 Feb 11.
PubMed 35147766Bi WL, Hosny A, Schabath MB, Giger ML, Birkbak NJ, Mehrtash A, Allison T, Arnaout O, Abbosh C, Dunn IF, Mak RH, Tamimi RM, Tempany CM, Swanton C, Hoffmann U, Schwartz LH, Gillies RJ, Huang RY, Aerts HJWL. Artificial intelligence in cancer imaging: Clinical challenges and applications. CA Cancer J Clin. 2019 Mar;69(2):127-157. doi: 10.3322/caac.21552. Epub 2019 Feb 5.
PubMed 30720861
Study at a glance
From the registry record, last updated by the sponsor on 28 Sep 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Serum and Tissue Metabolite-based Prediction of Sentinel Lymph Node Metastasis in Breast Cancer
- ClinicalTrials.gov ID
- NCT06001528
- Acronym / sponsor ID
- XR Lin
- Conditions
- Breast Cancer, Lymph Node Metastasis
- Keywords
- breast cancer, sentinel lymph node metastasis, metabolic reprogramming, artificial intelligence
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 2,400 participants (estimated)
- Lead sponsor
- Shantou Central HospitalUniversity, hospital or other
- Collaborators
- Zhejiang Cancer Hospital, Sichuan Cancer Hospital and Research Institute, Shenshan Medical Center of Sun Yat-sen Memorial Hospital, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
- Study start
- 1 Jan 2021 (actual)
- Primary completion
- 31 Dec 2023 (estimated)
- Study completion
- 31 Aug 2026 (estimated)
- First posted
- 21 Aug 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 cancer is a malignant tumor with the highest morbidity and mortality among women worldwide. Accurate staging of axillary lymph nodes is critical for metastatic assessment and decisions regarding treatment modalities in breast cancer patient. Among patients who underwent sentinel lymph node biopsy, about 70 % of the patients had negative pathological results and in other words, these 70 % of the patients received unnecessary surgery. At present, imaging and pathological diagnosis is the main measure of lymph node metastasis in breast cancer. However, limitations remained. Artificial intelligence, including deep learning and machine learning algorithms, has emerged as a possible technique, which can make a more accuracy prediction through machine-based collection, learning and processing of previous information, especially in radiology and pathology-based diagnosis. With the intensification of the concept of precision medicine and the development of non-invasive technology, the investigators intend to use the artificial intelligence technology to develop a serum and tissue-based predictive model for sentinel lymph node metastasis diagnosis combined with imaging and pathological information, providing specific, efficient and non-invasive biological indicators for the monitoring and early intervention of lymph node metastasis in patient with breast cancer. Therefore, the investigators retrospectively include serum samples from early breast cancer patients undergoing sentinel lymph node biopsy, including a discovery cohort and a modeling cohort. Metabolites were detected and screened in the discovery cohort and then as the target metabolites for targeted detection in the modeling cohort. Combined with preoperative imaging and pathological information, a prediction model of breast cancer sentinel lymph node metastasis based on serum metabolites would be established. Subsequently, multi-center breast cancer patients will prospectively be included to verify the accuracy and stability of the model.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Discovering cohort | – | –Discovering cohort was used for the discovery and screening of metabolic differences. Two groups were included-SLN+ group and SLN- group, meaning the breast cancer patients with/without sentinel lymph node metastasis respectively. Abundance and distribution of serum and tissue metabolites in this cohort of patients would be observed. |
| Modeling cohort | – | –Modeling cohort refer to the cohort of patients included for targeted metabolites detection. Two groups were included-SLN+ group and SLN- group. Abundance and distribution of targeted metabolites in this cohort of patients would be detected, and a predictive model would be established using the data of this cohort. |
| Validation cohort | – | –Validation cohort means a cohort of patients included to validate the prediction model established in the modeling stage. Patients of validation cohort will be enrolled from several different hospitals. Also, it included SLN+ group and SLN- group. Abundance and distribution of targeted metabolites in this cohort of patients would be detected, and the accuracy and stability of prediction model will be verified in this cohort. |
Who may qualify
- Ages
- 18 years and older
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Retrospective cohort: The study retrospectively collected data from 724 patients with early breast cancer. Prospective cohort: We expected the accuracy of our predictive model reached 96%, and given an estimated dropout rate of 10%. We needed to include at least 586 breast cancer in the prospective cohort. Therefore, we plan to prospectively enroll serum samples from 586 breast cancer patients to detect the abundance of metabolites and collect the radiological and pathological information from these patients for the following analysis.
Key inclusion criteria (6 listed)
- Pathological diagnosis of breast cancer
- No preoperative therapy including chemotherapy or endocrine therapy
- No distant metastasis
- Underwent mastectomy or breast-conserving surgery with sentinel lymph node biopsy
- Agreed to provide preoperative peripheral blood samples
Key exclusion criteria (7 listed)
- Neoadjuvant therapy
- Presence of distant metastasis at time of diagnosis
- Primary malignancies other than breast cancer
- Bilateral breast cancer or previous contralateral breast cancer
- Undergo modified radical surgery for breast cancer without sentinel lymph node biopsy
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
Metabolic difference detection
Time frame: From January 01, 2021 to December 31, 2021
Serum metabolites difference between breast cancer patients with and without sentinel lymph node metastasis would be analyzed, and potential biological indicators found.
Predictive model establishment
Time frame: From January 01, 2022 to December 31, 2022
Combined with preoperative imaging and pathological information, a predictive model of sentinel lymph node metastasis in breast cancer would be established based on the metabolic difference.
Predictive model validation
Time frame: From January 01, 2023 to December 31, 2023
Verify the stability and accuracy of our model in larger cohorts and promote clinical translation.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 28 Sep 2023.
- 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 34417437Xu Y, Su GH, Ma D, Xiao Y, Shao ZM, Jiang YZ. Technological advances in cancer immunity: from immunogenomics to single-cell analysis and artificial intelligence. Signal Transduct Target Ther. 2021 Aug 20;6(1):312. doi: 10.1038/s41392-021-00729-7.
- Background: PubMed 35337361Zhou H, Zhu L, Song J, Wang G, Li P, Li W, Luo P, Sun X, Wu J, Liu Y, Zhu S, Zhang Y. Liquid biopsy at the frontier of detection, prognosis and progression monitoring in colorectal cancer. Mol Cancer. 2022 Mar 25;21(1):86. doi: 10.1186/s12943-022-01556-2.
- Background: PubMed 35379239Richard V, Davey MG, Annuk H, Miller N, Kerin MJ. The double agents in liquid biopsy: promoter and informant biomarkers of early metastases in breast cancer. Mol Cancer. 2022 Apr 4;21(1):95. doi: 10.1186/s12943-022-01506-y.
- Background: PubMed 31969959Chayakulkheeree J, Pungrassami D, Prueksadee J. Performance of breast magnetic resonance imaging in axillary nodal staging in newly diagnosed breast cancer patients. Pol J Radiol. 2019 Oct 18;84:e413-e418. doi: 10.5114/pjr.2019.89690. eCollection 2019.
- Background: PubMed 30671931Alimirzaie S, Bagherzadeh M, Akbari MR. Liquid biopsy in breast cancer: A comprehensive review. Clin Genet. 2019 Jun;95(6):643-660. doi: 10.1111/cge.13514. Epub 2019 Feb 27.
- Background: PubMed 35147766Isaksen JL, Baumert M, Hermans ANL, Maleckar M, Linz D. Artificial intelligence for the detection, prediction, and management of atrial fibrillation. Herzschrittmacherther Elektrophysiol. 2022 Mar;33(1):34-41. doi: 10.1007/s00399-022-00839-x. Epub 2022 Feb 11.
- Background: PubMed 30720861Bi WL, Hosny A, Schabath MB, Giger ML, Birkbak NJ, Mehrtash A, Allison T, Arnaout O, Abbosh C, Dunn IF, Mak RH, Tamimi RM, Tempany CM, Swanton C, Hoffmann U, Schwartz LH, Gillies RJ, Huang RY, Aerts HJWL. Artificial intelligence in cancer imaging: Clinical challenges and applications. CA Cancer J Clin. 2019 Mar;69(2):127-157. doi: 10.3322/caac.21552. Epub 2019 Feb 5.
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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. This study is not recruiting at the moment.
Xiaorong Lin, Dr. study director
Shantou Central Hospital
Hai Hu, Pro. principal investigator
Zhejiang Cancer Hospital
Zhiyong Wu, Dr. principal investigator
Shantou Central Hospital
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 and Lymph Node Metastasis, run by Shantou Central Hospital at 1 site in China.
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 6 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 28 Sep 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 China. 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 Xiaorong Lin, Dr. (13790891600, clarelynn_lin@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Metabolic difference detection (time frame: From January 01, 2021 to December 31, 2021). The study lists 3 primary outcomes in all. 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 NCT06001528, which also lists 8 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 28 Sep 2023.