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Using Deep Learning and Radiomics to Diagnose Benign and Malignant Breast Lesions Based on Ultrasound

Sponsor
Ma Zhe (Other)
Overall Status
Completed
CT.gov ID
NCT06069921
Collaborator
(none)
400
Enrollment
1
Location
95.9
Actual Duration (Months)
4.2
Patients Per Site Per Month

Study Details

Study Description

Brief Summary

This retrospective study aimed to create a prediction model using deep learning and radiomics features extracted from intratumoral and peritumoral regions of breast lesions in ultrasound images, to diagnose benign and malignant breast lesions with BI-RADS 4 classification.

Materials and methods: Patients who visited in The First Affiliated Hospital of Shandong First Medical University ＆ Shandong Provincial Qianfoshan Hospital were collected. Their general clinical features, information on preoperative ultrasound diagnosis, and postoperative pathologic data were reviewed.

Condition or Disease Intervention/Treatment Phase

    Study Design

    Study Type:
    Observational
    Actual Enrollment :
    400 participants
    Observational Model:
    Cohort
    Time Perspective:
    Retrospective
    Official Title:
    Ultrasound-based Deep Learning Signature and Radiomics Signature Nomogram for Diagnosis of Benign and Malignant Breast Lesions of BI-RADS Category 4 Using Intratumoral and Peritumoral Regions
    Actual Study Start Date :
    Jan 1, 2015
    Actual Primary Completion Date :
    Dec 30, 2022
    Actual Study Completion Date :
    Dec 30, 2022

    Arms and Interventions

    Arm Intervention/Treatment
    maligant

    female patients with US-visible solid maligant breast masses who underwent biopsy and/or surgical resection.
    benign

    female patients with US-visible solid benign breast masses who underwent biopsy and/or surgical resection.

    Outcome Measures

    Primary Outcome Measures

    1. radiomcis prediction model and the model evaluation [Immediately evaluated after the radiomcis prediction model was built]

      three radiomics models were established using the support vector machines algorithm based on features extracted from the intratumoral, peritumoral, and combined regions of the breast lesions.The models were evaluated using various metrics, including AUC, accuracy, sensitivity, specificity, PPV, and NPV

    Secondary Outcome Measures

    1. deep learning prediction model and the model evaluation [Immediately evaluated after the deep learning prediction model was built]

      three deep learning models were established using the support vector machines algorithm based on features extracted from the intratumoral, peritumoral, and combined regions of the breast lesions.The models were evaluated using various metrics, including AUC, accuracy, sensitivity, specificity, PPV, and NPV

    Other Outcome Measures

    1. the combination prediction model and the model evaluation [Immediately evaluated after the combination prediction model was built]

      the combination model was established using clinical features , deep learning score and radiomics score.The models were evaluated using various metrics, including AUC, accuracy, sensitivity, specificity, PPV, and NPV

    Eligibility Criteria

    Criteria

    Ages Eligible for Study:
    15 Years to 80 Years
    Sexes Eligible for Study:
    Female
    Accepts Healthy Volunteers:
    No
    Inclusion Criteria:
    • female patients with US-visible solid breast masses who underwent biopsy and/or surgical resection, and were classified as having BI-RADS 4 lesions in medical US reports.
    Exclusion Criteria:
    • preoperative endocrine therapy, chemotherapy, or radiotherapy, preoperative invasive breast operation, insufficient image quality, and no pathological results.

    Contacts and Locations

    Locations

    Site City State Country Postal Code
    1 QianfoshanH Jinan Shandong China 250014

    Sponsors and Collaborators

    • Ma Zhe

    Investigators

    None specified.

    Study Documents (Full-Text)

    None provided.

    More Information

    Publications

    None provided.
    Responsible Party:
    Ma Zhe, Director of Ultrasound, Qianfoshan Hospital
    ClinicalTrials.gov Identifier:
    NCT06069921
    Other Study ID Numbers:
    • YXLL-KY-2023(045)
    First Posted:
    Oct 6, 2023
    Last Update Posted:
    Oct 6, 2023
    Last Verified:
    Sep 1, 2023
    Individual Participant Data (IPD) Sharing Statement:
    No
    Plan to Share IPD:
    No
    Studies a U.S. FDA-regulated Drug Product:
    No
    Studies a U.S. FDA-regulated Device Product:
    No
    Additional relevant MeSH terms:
    Breast Diseases

    Study Results

    No Results Posted as of Oct 6, 2023