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

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

Status

Completed

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT06497023

Enrollment

550

Registered

2024-07-11

Start date

2018-01-01

Completion date

2024-03-30

Last updated

2024-07-11

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Breast Diseases

Brief summary

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.

Detailed description

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.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

FEMALE

Age

15 Years to 80 Years

Inclusion criteria

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

Exclusion criteria

1. pathologicalresult of biopsy or surgical specimen was unavailable for the target lesion; 2. patients without DCE-MRI, BMUS and SE examinations before biopsy or surgery within one month; 3. patients hadperformed radiotherapy, chemotherapy, or breast biopsy before MRI and ultrasound examinations; 4. patients without completeDICOM data for each examination; 5. patients with poor-qualityimages.

Design outcomes

Primary

Measure	Time frame	Description
Accuracy of different diagnostic models	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

Measure	Time frame	Description
Sensitivity of different diagnostic models	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

Measure	Time frame	Description
Specificity of different diagnostic models	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.
PPV of different diagnostic models	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.
NPV of different diagnostic models	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.

Countries

China

Outcome results