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Completed

NCT Number: NCT06497023

Radiomics Model Based on DCE-MRI and Ultrasound Images for Breast Lesion Classification

To develop and compare multi-modality radiomics models based on DCE-MRI, B-mode ultrasound (BMUS) and strain elastography (SE) images for classifying benign and malignant breast lesions.

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Key information

Conditions

Breast Diseases Skin Diseases Skin and Connective Tissue Diseases

Age range

15 year–80 year

Sex eligibility

Female

Study type

Observational

Primary location

QianfoshanH

Jinan, Shandong, 250014, China

About this study

In this retrospective study,555 breast lesions from 555 patients who underwent DCE-MRI, BMUS and SE examinations were randomly divided into training (n =388) and testing (n = 167) datasets. Radiomics features were extracted from manually contoured images. The inter-class correlation coefficient (ICC), Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) regression was applied for feature selection and radiomics signature building.Nine radiomics models including four single-modality radiomics models (DCE-3D, DCE-2D, BMUS, and SE), four multi-modality radiomics models (BMUS + SE, DCE-3D + BMUS, DCE-3D + SE, and DCE-3D+SE+BMUS), and the combination diagnostic model(clinical features and DCE-3D+SE+BMUS features) were developed and evaluated by their discrimination, calibration, and clinical usefulness.

Who can participate

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

Patients with breast lesions underwent biopsy or surgical resection between January 1, 2018 and March 30, 2024.

Exclusion criteria

  • pathologicalresult of biopsy or surgical specimen was unavailable for the target lesion;
  • patients without DCE-MRI, BMUS and SE examinations before biopsy or surgery within one month;
  • patients hadperformed radiotherapy, chemotherapy, or breast biopsy before MRI and ultrasound examinations;
  • patients without completeDICOM data for each examination;
  • patients with poor-qualityimages.

Treatment and study plan

Primary outcomes

  1. Accuracy of different diagnostic models

    Time frame: Immediately evaluated after the radiomcis diagnostic model was built

    The accuracy of nine diagnostic models, including four single-modality radiomics models (DCE-3D, DCE-2D, BMUS, and SE), four multi-modality radiomics models (BMUS + SE, DCE-3D + BMUS, DCE-3D + SE, and DCE-3D+SE+BMUS),and the combination diagnostic model is the ratio of the sum of True Positive and True Negative to the total of True Positive, True Negative, False Positive, and False Negative.

Secondary outcomes

  1. Sensitivity of different diagnostic models

    Time frame: Immediately evaluated after the radiomcis diagnostic model was built

    The sensitivity of nine diagnostic models, four single-modality radiomics models (DCE-3D, DCE-2D, BMUS, and SE), including four multi-modality radiomics models (BMUS + SE, DCE-3D + BMUS, DCE-3D + SE, and DCE-3D+SE+BMUS), and the combination diagnostic model is the ratio of True Positive to the sum of True Positive and False Negative.

Other outcomes

  1. Specificity of different diagnostic models

    Time frame: immediately evaluated after the combination diagnostic model was built

    The specificity of nine different diagnostic models, including four single-modality radiomics models (DCE-3D, DCE-2D, BMUS, and SE), four multi-modality radiomics models (BMUS + SE, DCE-3D + BMUS, DCE-3D + SE, and DCE-3D+SE+BMUS), and the combination diagnostic model is the ratio of True Negative to the sum of True Negative and False Positive.

  2. PPV of different diagnostic models

    Time frame: immediately evaluated after the combination diagnostic model was built

    The PPV of nine different diagnostic models, including four single-modality radiomics models (DCE-3D, DCE-2D, BMUS, and SE), four multi-modality radiomics models (BMUS + SE, DCE-3D + BMUS, DCE-3D + SE, and DCE-3D+SE+BMUS),and the combination diagnostic model is the ratio of True Positive to the sum of True Positive and False Positive.

  3. NPV of different diagnostic models

    Time frame: immediately evaluated after the combination diagnostic model was built

    The NPV of nine different diagnostic models, including four single-modality radiomics models (DCE-3D, DCE-2D, BMUS, and SE), four multi-modality radiomics models (BMUS + SE, DCE-3D + BMUS, DCE-3D + SE, and DCE-3D+SE+BMUS), and the combination diagnostic model is the ratio of True Negative to the sum of True Negative and False Negative.

Sponsors and collaborators

Lead sponsor

Ma Zhe

Other

Registry information

Official study title

Multi-modality Radiomics Diagnostic Model Based on DCE-MRI and Ultrasound Images for Benign and Malignant Breast Lesion Classification

Important dates

Study start
2018
Primary completion
2024
Study completion
2024
First posted
Jul 11, 2024
Registry last updated
Jul 11, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

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This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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