A hybrid machine learning-based method for classifying the Cushing's Syndrome with comorbid adrenocortical lesions.
A hybrid machine learning-based method for classifying the Cushing's Syndrome with comorbid adrenocortical lesions.
复制标题
一种基于混合机器学习的方法，用于将库欣综合症与合并症肾上腺皮质病变分类。
DOI：
10.1186/1471-2164-9-s1-s23
复制
发表时间：
2008
期刊：
影响因子：
4.4
通讯作者：
Huang, Xudong
中科院分区：
文献类型：
--
作者：
Yang, Jack Y.;Yang, Mary Qu;Luo, Zuojie;Ma, Yan;Li, Jianling;Deng, Youping;Huang, Xudong
如果许多癌症能够在微观疾病阶段被检测出来，那么它们的预后可能会得到显着改善。综合统计分析表明，hTERT、PCNA 和 Ki-67 等多种抗原可被视为癌症标志物，而 P27KIP1 和 FHIT 等另一组抗原可能是正常组织的标志物。因为必须考虑不止一种标记物才能获得癌症或非癌症的分类，如果是癌症，则将其分类为恶性、边缘或良性，因此我们必须开发一种智能决策系统来满足这种未满足的医疗需求。我们开发了一个智能决策系统，使用机器学习技术和标记来将组织表征为癌性、非癌性或边缘性组织。该系统融合了支持向量机变体、神经网络、决策树、自组织特征图（SOFM）和递归最大对比度树（RMCT）等学习技术。我们开发的这些变体和算法倾向于根据基因表达水平和代谢谱的特征来检测微观病理变化。我们还使用免疫组织化学技术来测量多种特定类型神经内分泌肿瘤（如嗜铬细胞瘤、副神经节瘤和肾上腺皮质肿瘤）中多种抗原（如细胞周期蛋白 E、P27KIP1、FHIT、Ki-67、PCNA、Bax、Bcl-2、P53、Fas、FasL 和 hTERT）的基因表达谱。 与库欣综合征有关的癌（ACC）、腺瘤（ACA）和增生（ACH）。我们提供的统计证据表明，hTERT、PCNA 和 Ki-67 等的较高表达水平与肿瘤恶性或交界性而非良性的风险较高相关。我们还研究了 P27KIP1 和 FHIT 等的较高表达水平是否与肾上腺髓质肿瘤风险降低相关。虽然恶性、交界性和良性肿瘤的细胞阻滞抗原（例如 P27KIP1）之间没有发现显着差异，但此类抗原在正常肾上腺髓质样本和肾上腺髓质肿瘤中的表达水平之间存在显着差异。我们的框架不仅关注不同的分类方案和特征选择算法，还关注诸如 boosting 和 bagging 之类的集成方法，以努力提高各个分类器的准确性。显然，当各种机器学习和统计学习技术适当地组合成一个集成的智能医疗决策系统时，预测能力可以显着增强。这项研究有许多潜在的应用；它可能提供一种替代诊断工具，更好地了解恶性转化的机制，以及对治疗计划和癌症预防有用的信息。
The prognosis for many cancers could be improved dramatically if they could be detected while still at the microscopic disease stage. It follows from a comprehensive statistical analysis that a number of antigens such as hTERT, PCNA and Ki-67 can be considered as cancer markers, while another set of antigens such as P27KIP1 and FHIT are possible markers for normal tissue. Because more than one marker must be considered to obtain a classification of cancer or no cancer, and if cancer, to classify it as malignant, borderline, or benign, we must develop an intelligent decision system that can fullfill such an unmet medical need. We have developed an intelligent decision system using machine learning techniques and markers to characterize tissue as cancerous, non-cancerous or borderline. The system incorporates learning techniques such as variants of support vector machines, neural networks, decision trees, self-organizing feature maps (SOFM) and recursive maximum contrast trees (RMCT). These variants and algorithms we have developed, tend to detect microscopic pathological changes based on features derived from gene expression levels and metabolic profiles. We have also used immunohistochemistry techniques to measure the gene expression profiles from a number of antigens such as cyclin E, P27KIP1, FHIT, Ki-67, PCNA, Bax, Bcl-2, P53, Fas, FasL and hTERT in several particular types of neuroendocrine tumors such as pheochromocytomas, paragangliomas, and the adrenocortical carcinomas (ACC), adenomas (ACA), and hyperplasia (ACH) involved with Cushing's syndrome. We provided statistical evidence that higher expression levels of hTERT, PCNA and Ki-67 etc. are associated with a higher risk that the tumors are malignant or borderline as opposed to benign. We also investigated whether higher expression levels of P27KIP1 and FHIT, etc., are associated with a decreased risk of adrenomedullary tumors. While no significant difference was found between cell-arrest antigens such as P27KIP1 for malignant, borderline, and benign tumors, there was a significant difference between expression levels of such antigens in normal adrenal medulla samples and in adrenomedullary tumors. Our frame work focused on not only different classification schemes and feature selection algorithms, but also ensemble methods such as boosting and bagging in an effort to improve upon the accuracy of the individual classifiers. It is evident that when all sorts of machine learning and statistically learning techniques are combined appropriately into one integrated intelligent medical decision system, the prediction power can be enhanced significantly. This research has many potential applications; it might provide an alternative diagnostic tool and a better understanding of the mechanisms involved in malignant transformation as well as information that is useful for treatment planning and cancer prevention.