Clinical trial · Observational
Prospective Observational Study for Breast Microcalcifications' Classification With Artificial Intelligence Techniques
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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
Conditions
Conditions (2)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Breast Cancer | Malignant Breast Neoplasm | CURATED_EXACT | 0.92 |
| Microcalcification | — | UNRESOLVED | — |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (1)
- measure
- Artificial Intellicence method for classification
- timeFrame
- 36 months
- description
- Classification method of breast microcalcifications with Artificial Intelligence techniques on mammography images
Secondary outcomes (2)
- measure
- Radiological features extraction
- timeFrame
- 36 months
- description
- Identification of the typical characteristics extracted from the Artificial Intelligence systems
- measure
- Artificial Intellicence method for combined classification
- timeFrame
- 36 months
- description
- Evaluation of the diagnostic performance of a model that combines radiological characteristics and characteristics deriving from Raman spectroscopic analysis
Eligibility
Eligibility (as posted)
- Sex
- Female
- Minimum age
- 18 Years
- Maximum age
- 88 Years
Show eligibility criteria text
Inclusion Criteria: * 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 Exclusion Criteria: * Personal history of breast cancer
References
Publications (0)
Data not yet available