相关实验视频

Updated: Jun 14, 2025

Murine Model for Non-invasive Imaging to Detect and Monitor Ovarian Cancer Recurrence

Murine Model for Non-invasive Imaging to Detect and Monitor Ovarian Cancer Recurrence

Published on: November 2, 2014

基于森林的随机模型用于边界卵巢瘤的复发预测:临床开发和验证

Liheng Yan¹, Qiulin Ye², Baole Shi¹

¹College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, China.

Journal of cancer research and clinical oncology

|May 11, 2025

概括

这项研究开发了一种机器学习模型,用于预测边缘性卵巢瘤 (BOT) 复发. 随机森林 (RF) 模型有效地识别了关键因素,有助于为个性化治疗做出临床决定.

科学领域:

妇科瘤学妇科瘤学
机器学习在医学中的应用
预测分析是一种预测分析.

背景情况:

边缘性卵巢瘤 (BOT) 是一种具有中间恶性潜力的独特瘤群.
准确预测BOT复发对于有效的患者管理和治疗规划至关重要.

研究的目的:

开发和验证基于机器学习 (ML) 的预测模型,用于边界卵巢瘤 (BOT) 复发.
为了确定与BOT复发相关的关键临床因素.
为准确的临床诊断和精确的治疗提供准则.

主要方法:

利用了660名被诊断为BOT的患者的数据集.
开发并比较了五种ML模型:随机森林 (RF),物流回归 (LR),梯度增强 (GB),多层感知器 (MLP) 和支持矢量机器 (SVM).
使用AUC,PPV,ACC,REC和SPE评估模型性能;使用SHAP值来确定特征的重要性,并使用CIC,DCA和无复发生存分析来评估临床价值.

主要成果:

随机森林 (RF) 模型显示了BOT复发的优异预测性能.
SHAP分析确定了影响BOT复发的关键临床因素.
通过决策曲线分析 (DCA),校准指数 (CIC) 和无复发生存率 (RFS) 分析证实了临床效用,支持个性化治疗策略.

结论:

开发的基于射频的模型是预测BOT复发的有效工具.
一个用户友好的基于网络的计算器已经创建,以帮助临床医生在决策BOT复发.
该模型促进了个性化治疗策略和明智的临床决策.

关键词:

边缘性卵巢瘤是发生在卵巢中的.节育性手术可以节省生育能力.机器学习机器学习预测建模的预测建模.随机的森林随机的森林预测复发的预测.

更多相关视频

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

相关概念视频

Survival Tree

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...

Receiver Operating Characteristic Plot

Receiver Operating Characteristic Plot

A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...