Breast CancerNCT05767424
Prospective Observational Study for Breast Microcalcifications' Classification With Artificial Intelligence Techniques
An observational study of Breast Cancer and Microcalcification, run by Istituti Clinici Scientifici Maugeri SpA at 1 site in Italy.
Trial summary
Breast microcalcifications are a common mammographic finding. Microcalcifications are considered suspicious signs of breast cancer and a breast biopsy is required, however, cancer is diagnosed in only a few patients. Read more about this study
- Who is it for?
- Ages 18 to 88 years, women only
- Conditions the study is for
- Breast Cancer, Microcalcification
- How many people
- 1,426 participants
- Where
- 1 siteItaly
- Study dates
- 22 Jul 2022to 25 Jul 2028
- 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 88 years
- Women only
- No healthy volunteers
- Adult, Older adult
Study population: Breast patological patients who experience microcalcification lesion
Who may be able to join
- Female subjects;
- Age between 18 and 88 years;
- Detection of microcalcifications on clinical and screening mammography with or without indication for histological assessment by biopsy;
- Subjects who agree to participate in the study by signing and dating the Informed Consent form
Who cannot join
- Personal history of breast cancer
Trial locations and contacts
1 site in 1 country.
About this study
Breast microcalcifications are a common mammographic finding. Microcalcifications are considered suspicious signs of breast cancer and a breast biopsy is required, however, cancer is diagnosed in only a few patients. Reducing unnecessary biopsies and rapid characterization of breast microcalcifications are unmet clinical needs. This study intends to implement a classification method for breast microcalcifications (as begnin or malign) with Artificial Intelligence techniques on mammographic images, evaluating the diagnostic performance (accuracy) of this approach. Another aim is the development of a diagnostic tool able to determining in-situ the biomolecular characteristics of microcalcifications. Raman spectroscopy (RS) is a highly specific method from the biomolecular point of view and it is able to explore molecular composition of a given sample through its direct irradiation (through laser light) and the simultaneous acquisition of emission signals. RS information could be combined togheter with imaging features to implement an AI model for the combined classification of breast microcalcifications
Read the detailed description
Breast microcalcifications are currently classified using the BI-RADS radiological scale. In case of suspicious microcalcifications (B3), it is recommended to perform a biopsy assessment for histopathological evaluation. However, about 70-80% of performed biopsies shows benign histology that does not require surgical treatment. Core biopsies are invasive procedures with a biological, psychological (patient discomfort), organizational and economic (for the Health Care System) costs. Therefore, accuracy's improvement in radiological classification of microcalcifications is essential. Recently, various approaches have been reported in the literature to detect and classify microcalcification as benign or suspicious in digital mammograms. Analysis methods based on the use of deep learning (DL) have also emerged as promising for processing mammography images. Convolutional neural networks (CNNs) are currently the state of the art for image classification in many application fields in the field of computer vision. This study intends to implement a classification method for breast microcalcifications (as benign or malign) with Artificial Intelligence (AI) techniques on mammographic images, evaluating the diagnostic performance (accuracy) of this approach. The evaluation will be conducted with reference to the standard radiological approach (BI-RADS classification).
Together with the application of AI systems to mammographic imaging, a further current clinical need is the development of a diagnostic tool able to determining in-situ the biomolecular characteristics of microcalcifications, accurately discriminating their nature without take tissue, fixation and embedding of the sample in paraffin, and without highly specialized evaluation by the pathologist. Raman spectroscopy (RS) is a highly specific method from the biomolecular point of view and, at the same time, it is compatible with in-vivo measurements. It consists in a biophotonic approach able to explore molecular composition of a given sample through its direct irradiation (through laser light) and the simultaneous acquisition of emission signals. RS information could be combined togheter with imaging features to implement an AI model for the combined classification of breast microcalcifications
- Conditions
- Breast Cancer, Microcalcification
- Keywords
- Breast microcalcification, Breast cancer, Artificial intelligence
- Medical subject headings
- Breast Neoplasms, Calcinosis
What is being measured
The primary outcome is the main question the study is designed to answer.
Artificial Intellicence method for classification
Classification method of breast microcalcifications with Artificial Intelligence techniques on mammography images
Measured: 36 months
Secondary outcomes (2)
Radiological features extraction
Identification of the typical characteristics extracted from the Artificial Intelligence systems
Measured: 36 months
Artificial Intellicence method for combined classification
Evaluation of the diagnostic performance of a model that combines radiological characteristics and characteristics deriving from Raman spectroscopic analysis
Measured: 36 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted14 Mar 2023
- Study start22 Jul 2022
- Primary completion25 Jul 2025
- Study completion25 Jul 2028 (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
- 1,426 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Istituti Clinici Scientifici Maugeri SpA
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Fabio Corsi, Istituti Clinici Scientifici Maugeri SpA
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05767424
- Sponsor's protocol ID
- 2669
- Status last verified
- Jul 2026
- First submitted
- 2 Mar 2023
- Last updated
- 26 Aug 2026
- 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 26 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prospective Observational Study for Breast Microcalcifications' Classification With Artificial Intelligence Techniques
- ClinicalTrials.gov ID
- NCT05767424
- Acronym / sponsor ID
- 2669
- Conditions
- Breast Cancer, Microcalcification
- Keywords
- Breast microcalcification, Breast cancer, Artificial intelligence
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 1,426 participants (estimated)
- Lead sponsor
- Istituti Clinici Scientifici Maugeri SpAUniversity, hospital or other
- Study start
- 22 Jul 2022 (actual)
- Primary completion
- 25 Jul 2025 (actual)
- Study completion
- 25 Jul 2028 (estimated)
- First posted
- 14 Mar 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 microcalcifications are a common mammographic finding. Microcalcifications are considered suspicious signs of breast cancer and a breast biopsy is required, however, cancer is diagnosed in only a few patients. Reducing unnecessary biopsies and rapid characterization of breast microcalcifications are unmet clinical needs. This study intends to implement a classification method for breast microcalcifications (as begnin or malign) with Artificial Intelligence techniques on mammographic images, evaluating the diagnostic performance (accuracy) of this approach. Another aim is the development of a diagnostic tool able to determining in-situ the biomolecular characteristics of microcalcifications. Raman spectroscopy (RS) is a highly specific method from the biomolecular point of view and it is able to explore molecular composition of a given sample through its direct irradiation (through laser light) and the simultaneous acquisition of emission signals. RS information could be combined togheter with imaging features to implement an AI model for the combined classification of breast microcalcifications
Detailed description
Breast microcalcifications are currently classified using the BI-RADS radiological scale. In case of suspicious microcalcifications (B3), it is recommended to perform a biopsy assessment for histopathological evaluation. However, about 70-80% of performed biopsies shows benign histology that does not require surgical treatment. Core biopsies are invasive procedures with a biological, psychological (patient discomfort), organizational and economic (for the Health Care System) costs. Therefore, accuracy's improvement in radiological classification of microcalcifications is essential. Recently, various approaches have been reported in the literature to detect and classify microcalcification as benign or suspicious in digital mammograms. Analysis methods based on the use of deep learning (DL) have also emerged as promising for processing mammography images. Convolutional neural networks (CNNs) are currently the state of the art for image classification in many application fields in the field of computer vision. This study intends to implement a classification method for breast microcalcifications (as benign or malign) with Artificial Intelligence (AI) techniques on mammographic images, evaluating the diagnostic performance (accuracy) of this approach. The evaluation will be conducted with reference to the standard radiological approach (BI-RADS classification).
Together with the application of AI systems to mammographic imaging, a further current clinical need is the development of a diagnostic tool able to determining in-situ the biomolecular characteristics of microcalcifications, accurately discriminating their nature without take tissue, fixation and embedding of the sample in paraffin, and without highly specialized evaluation by the pathologist. Raman spectroscopy (RS) is a highly specific method from the biomolecular point of view and, at the same time, it is compatible with in-vivo measurements. It consists in a biophotonic approach able to explore molecular composition of a given sample through its direct irradiation (through laser light) and the simultaneous acquisition of emission signals. RS information could be combined togheter with imaging features to implement an AI model for the combined classification of breast microcalcifications
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 to 88 years
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Breast patological patients who experience microcalcification lesion
Key inclusion criteria (4 listed)
- Female subjects;
- Age between 18 and 88 years;
- Detection of microcalcifications on clinical and screening mammography with or without indication for histological assessment by biopsy;
- Subjects who agree to participate in the study by signing and dating the Informed Consent form
Key exclusion criteria (1 listed)
- Personal history of breast cancer
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
Artificial Intellicence method for classification
Time frame: 36 months
Classification method of breast microcalcifications with Artificial Intelligence techniques on mammography images
2 secondary outcomes
Radiological features extraction
Time frame: 36 months
Artificial Intellicence method for combined classification
Time frame: 36 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 26 Aug 2026.
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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.
The record lists no contact. Use the official record to reach the sponsor.
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 Microcalcification, run by Istituti Clinici Scientifici Maugeri SpA 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 4 inclusion and 1 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: under way, not looking for participants, as of the sponsor's last update on 26 Aug 2026. 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?
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?
Artificial Intellicence method for classification (time frame: 36 months). 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 26 Aug 2026.