Study on Female Patients' Mammographic Texture Features

NCT06469606 | Not Yet Recruiting DMC

• 1 other identifier: R23077
• Study Type: interventional
• Target: 200
• Locations: 0 countries
• Sites: N/A

Brief Summary

Mammography is the most common method for breast imaging, and it provides information for model building and analysis. Radiomics applied to mammography has the potential to revolutionize clinical decision-making by providing valuable insights into risk assessment and disease detection. Despite this, the influence of imaging parameters and clinical and biological factors on radiological texture features remains poorly understood. There is a pressing need to overcome the obstacle of system-inherent effects on mammographic images to facilitate the translation of radiological texture features into routine clinical practice by enabling reliable and robust AI-based or AI-aided decision-making. Furthermore, understanding the relationship between imaging parameters, textural features, and clinical and biological information supports the clinical use of AI. The objective of this study is to evaluate AI methods for clinical practice and to study how it relates to clinical factors and biological features.

Conditions

Breast Cancer; Artificial Intelligence; Mammography

Outcome Measures

Primary Outcomes (1)

• Mammographic texture features

  Aim: to evaluate how imaging parameters affect the mammographic texture features

  Through study completion, an average of 5 year

Secondary Outcomes (1)

• Biological features

  through study completion, an average of 10 year

Study Arms (2)

Participant diagnosed with breast cancer

EXPERIMENTAL

Participants diagnosed with breast cancer who will undergo a mastectomy operation

Device: AI tool

Participant not diagnosed with breast cancer

EXPERIMENTAL

Participants who will undergo a mastectomy operation for a non-breast cancer related clinical indication

Device: AI tool

Interventions

AI tool DEVICE

Both the arms will undergo the use of "AI tool" developed in the group. The tool will be trained to detect outcomes.

Participant diagnosed with breast cancer; Participant not diagnosed with breast cancer

Eligibility Criteria

• Age: 18 Years+
• Sex: female
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Candidate is a biological female aged 18 years or above;
• Candidate is willing and able to give informed consent and gives their written consent for the participation in the study;
• There is a clinical indication for a uni- or bilateral mastectomy

You may not qualify if:

• Candidate lacks the capacity to provide informed consent;
• Candidate has breast implants

Sponsors & Collaborators

• Tampere University Hospital: lead
• Tampere University: collaborator
• Kuopio University Hospital: collaborator
• University of Eastern Finland: collaborator
• University of Turku: collaborator
• University of Oulu: collaborator

MeSH Terms

Conditions

Breast Neoplasms

Condition Hierarchy (Ancestors)

Neoplasms by Site; Neoplasms; Breast Diseases; Skin Diseases; Skin and Connective Tissue Diseases

Central Study Contacts

Otso Arponen, MD, PhD

CONTACT

+3583311611; otso.arponen@tuni.fi

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NON RANDOMIZED
• Masking: SINGLE
• Who Masked: PARTICIPANT
• Purpose: DIAGNOSTIC
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: May 30, 2024
• First Posted: June 21, 2024
• Study Start: June 17, 2024
• Primary Completion (Estimated): December 15, 2035
• Study Completion (Estimated): December 15, 2038
• Last Updated: June 21, 2024

Record last verified: 2024-06

Data Sharing

• IPD Sharing: Will not share