使用基于前列腺肺结直肠卵巢癌查试验数据开发的机器学习模型改进卵巢癌风险评估
Seyyed Mostafa Mousavi Janbeh Sarayi1, Martin Tammemägi2, Larissa A Meyer3
1The University of Texas MD Anderson Cancer Center, Department of Health Services Research, Houston, TX, USA.
概括
开发一个卵巢癌风险预测工具可以通过识别高风险个体来改善预防. 与现有工具相比,这种新模型显示出更高的性能,有助于针对性干预.
科学领域:
- 在瘤学瘤学.
- 流行病学 流行病学
- 生物统计学 生物统计学
背景情况:
- 对于一般人群来说,卵巢癌查对于降低死亡率是无效的,因为发病率很低.
- 风险评估对于将卵巢癌预防策略针对高风险个体至关重要.
研究的目的:
- 开发和验证具有高预测性能的卵巢癌风险预测工具.
- 通过识别有风险的个体来指导卵巢癌预防工作.
主要方法:
- 利用前列腺,肺,结肠直肠和卵巢 (PLCO) 癌症查试验的数据进行模型开发和验证.
- 对比各种机器学习算法,包括极端梯度提升,以创建一个10年的卵巢癌风险模型.
- 使用曲线下的面积 (AUC),灵敏度,特异性和积极的预测值来评估模型性能.
主要成果:
- 极端梯度提升模型在训练组中实现了0.80的AUC,在验证组中达到0.66的AUC.
- 关键预测因素包括体重指数,年龄,使用激素的持续时间 (风险增加) 和双边卵巢切除术,活产数量 (风险降低).
- 该模型在相同的验证数据集上,与之前发布的模型相比,显示出更高的性能.
结论:
- 开发的卵巢癌发病率风险模型显示了比现有工具更好的性能.
- 需要进一步的研究来提高预测准确度,并确保基于风险的卵巢癌预防计划的实际实施.
更多相关视频
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
8.6K
08:55Murine Model for Non-invasive Imaging to Detect and Monitor Ovarian Cancer Recurrence
Published on: November 2, 2014
12.7K
相关概念视频
Mouse Models of Cancer Study
6.3K
Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
6.3K
Cancer Survival Analysis
630
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
630
