Characterizing Breast Masses Using an Integrative Framework of Machine Learning and Radiomics
试验速览
- 阶段
- 1 期
- 状态
- 招募中
- 入组人数
- 100
- 试验地点
- 2
- 主要终点
- Performance of radiomics-based ML approach to prevent unnecessary biopsies
研究概览
简要总结
This clinical trial investigates the role of contrast enhanced ultrasound (CEUS) in identifying cystic breast masses as benign or malignant. Ultrasound is a diagnostic imaging test that uses sound waves to make pictures of the body without using radiation (x-rays). Ultrasounds are widely used to diagnose many diseases in the body. This trial may help researchers learn if using CEUS will help in determining whether or not an ultrasound guided biopsy is necessary.
详细描述
PRIMARY OBJECTIVES:
I. To examine and compare the distribution of CEUS parameters in breast masses that were evaluated as Breast Imaging Reporting and Data System (BI-RADS) 4a, 4b, 4c or 5 by conventional ultrasound (US) and were recommended for ultrasound guided biopsy, and to evaluate whether these parameters can be used to classify suspicious cystic-appearing breast masses as benign or malignant.
Ia. To develop a CEUS-based radiomics workflow to extract radiomic metrics (> 1600 features) in classifying breast mass malignancy (Radiomics).
Ib. To develop a systematic and rigorous machine learning (ML)-based framework comprised of classification, cross-validation and statistical analyses to identify the best performing classifier for breast malignancy stratification based on CEUS-derived radiomic metrics (time-intensity curve [TIC] analysis and Radiomics).
Ic. To assess the independent contribution of radiomics classifier and time-intensity curve classifier to the model accuracy in discriminating benign from malignant cases (TIC analysis versus [vs.] Radiomics).
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Diagnostic
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- Female
- 接受健康志愿者
- 否
入选标准
- •Newly diagnosed breast masses assigned as BIRADS 4a, 4b, 4c or 5 by conventional US and recommended for ultrasound guided biopsy
- •Age >= 18 years
排除标准
- •Contraindications to microbubble contrast: Patients who have a known pulmonary hypertension and any known hypersensitivity to US contrast agent
- •Women with renal failure or insufficiency (only if patient is receiving CESM scan)
- •Women with Iodine contrast allergy (only if patient is receiving CESM scan)
- •Women with the largest side of the mass measuring ≤ 1 cm (only if patient is receiving CEUS scan)
- •Women who are pregnant, possibly pregnant, or lactating
- •Women currently undergoing neoadjuvant chemotherapy
- •Women < 18 years of age
- •Patient ≤ 30 years (only if patient is receiving CESM scan)
- •Masses in the same breast that had prior lumpectomy for cancer
- •Women with cancer in the same breast will be excluded however, women with cancer in the contralateral breast will be eligible to participate in the study
- •Women with an allergy to perflutren (only if patient is receiving CEUS scan)
- •Prior history of biopsy for that specific lesion
- •Women with breast implants
研究组 & 干预措施
Diagnostic (contrast agent, CEUS)
Patients receive a contrast tracer (Lumason or DEFINITY) IV and then undergo CEUS scan over 60-90 minutes.
干预措施: Contrast-Enhanced Ultrasound (Procedure)
Diagnostic (contrast agent, CEUS)
Patients receive a contrast tracer (Lumason or DEFINITY) IV and then undergo CEUS scan over 60-90 minutes.
干预措施: Perflutren Lipid Microspheres (Drug)
Diagnostic (contrast agent, CEUS)
Patients receive a contrast tracer (Lumason or DEFINITY) IV and then undergo CEUS scan over 60-90 minutes.
干预措施: Sulfur Hexafluoride Lipid Microspheres (Drug)
结局指标
主要结局
Performance of radiomics-based ML approach to prevent unnecessary biopsies
时间窗: Up to 12 months
Will assess the percentage of benign cases that can be classified as benign by ML (Specificity) thus been prevented from biopsy. Will select the diagnostic cut-off point based on the ROC curve constructed from the predicted probability. Such a cut-off point will result in a maximal sensitivity (100%). Specificity with 95% Clopper Pearson confidence interval will be obtained.
Radiomics-based ML-classifier framework
时间窗: Up to 12 months
The performance of radiomics-based ML classifier framework will be compared to the performance of the TIC metrics. The joint performance of radiomics and TIC analysis will be compared to their individual performances. The classifier performance will be assessed using the area under curve (AUC). The Z-test will be used to compare the difference between the area under the curves 1) AUCboth versus (vs.) AUCradiomic 2) AUCboth vs. AUCTIC 3) AUCTIC vs. AUCradiomic.
次要结局
未报告次要终点