Prospective Observational Study for Breast Microcalcifications' Classification With Artificial Intelligence Techniques

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

Study Overview

• Status: Recruiting
• Conditions: Breast Cancer; Microcalcification

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 Type: Observational
• Enrollment (Estimated): 1426

Contacts and Locations

Study Locations

• Italy
  • Lombardia
    • Pavia, Lombardia, Italy, 27100
      • Recruiting
      • Istituti Clinici Scientifici Maugeri SpA
      • Contact: Fabio Corsi, Professor; Phone Number: 0382592272; Email: fabio.corsi@icsmaugeri.it
      • Contact: Sara Albasini, MsC; Phone Number: 3497378405; Email: sara.albasini@icsmaugeri.it

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 88 years (Adult, Older Adult)
• Accepts Healthy Volunteers: No

• Sampling Method: Probability Sample

Study Population

Breast patological patients who experience microcalcification lesion

Description

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

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Artificial Intellicence method for classification	Classification method of breast microcalcifications with Artificial Intelligence techniques on mammography images	36 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Radiological features extraction	Identification of the typical characteristics extracted from the Artificial Intelligence systems	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	36 months

Collaborators and Investigators

• Sponsor: Istituti Clinici Scientifici Maugeri SpA

Study record dates

Study Major Dates

• Study Start (Actual): July 22, 2022
• Primary Completion (Estimated): July 25, 2025
• Study Completion (Estimated): July 25, 2025

Study Registration Dates

• First Submitted: March 2, 2023
• First Submitted That Met QC Criteria: March 2, 2023
• First Posted (Actual): March 14, 2023

Study Record Updates

• Last Update Posted (Actual): March 7, 2024
• Last Update Submitted That Met QC Criteria: March 6, 2024
• Last Verified: March 1, 2024

More Information

Terms related to this study

• Keywords: Breast cancer; Artificial intelligence; Breast microcalcification
• Additional Relevant MeSH Terms: Metabolic Diseases; Calcium Metabolism Disorders; Calcinosis
• Other Study ID Numbers: 2669 (CTEP)

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No