预测乳腺侵袭性导管癌的组织学级:使用DCE-MRI的基于放射学的机器学习模型
Ziwen Wang1, Chenglin Bai2, Naiyou Zhang2
1Radiology Department of Chaoyang Central Hospital Affiliated to China Medical University, Chaoyang, Liaoning, China.
这项研究表明,使用DCE-MRI放射学特征的逻辑回归模型可以准确预测乳腺侵入性导管癌 (IDC) 组织学级. 这种非侵入性方法有助于在手术前做出决定,以提供个性化的患者护理.
科学领域:
- 放射学 放射学是一门学科.
- 在瘤学瘤学.
- 机器学习 机器学习
背景情况:
- 准确预测乳腺侵入性导管癌 (IDC) 组织学级对于治疗规划至关重要.
- 目前的方法通常依赖于侵入性活检,需要非侵入性替代品.
研究的目的:
- 评估使用手术前DCE-MRI放射学预测IDC病理差异化等级的可行性.
- 为此预测任务确定最佳的机器学习模型.
主要方法:
- 对198名IDC患者的DCE-MRI数据进行了追溯分析.
- 使用3D Slicer软件提取放射学特征.
- 开发和比较五种机器学习模型:决策树,高斯素朴贝叶斯,后勤回归,随机森林和AdaBoost.
主要成果:
- 在稳定性和冗余性选后,选择了22个关键的放射性特征.
- 后勤回归模型在验证集中实现了0.795的曲线下的最大面积 (AUC).
- 后勤回归优于其他模型,包括随机森林 (AUC=0.700) 和AdaBoost (AUC=0.718).
结论:
- 使用后勤回归的DCE-MRI放射学模型可以有效地和非侵入性地预测IDC组织学等级.
- 这种方法具有显著的潜力,可以支持乳腺癌管理中的个性化临床决策.
更多相关视频
07:13Initiation of Metastatic Breast Carcinoma by Targeting of the Ductal Epithelium with Adenovirus-Cre: A Novel Transgenic Mouse Model of Breast Cancer
Published on: March 26, 2014
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018
相关概念视频
Simplified Synchronous Machine Model
In this model, each generator is connected to a...
Wind Turbine Machine Models
Induction machines interact through the rotating magnetic field generated by the stator and the rotor. The key parameter is slip, which is the difference between synchronous speed and rotor speed relative to synchronous speed. Slip is...
Graded Potential
Graded potentials fall into two categories: depolarizing and hyperpolarizing. Depolarizing graded potentials typically occur when sodium (Na+) or...
Predicting Molecular Geometry
Machines
A free-body diagram of the...
Types of Aggregate Grading
Well-graded aggregates include a complete range of necessary size fractions that fit together to create a dense matrix with minimal voids, represented by a smooth, continuous gradation curve. This type of grading ensures good...
