Du Xian, Dua Sumeet
Xian Du, Sumeet Dua, Department of Computer Science, Louisiana Tech University, Ruston, LA 71272, United States.
World J Clin Oncol. 2011 Jan 10;2(1):44-9. doi: 10.5306/wjco.v2.i1.44.
The proposed techniques investigate the strength of support vector regression (SVR) in cancer prognosis using imaging features. Cancer image features were extracted from patients and recorded into censored data. To employ censored data for prognosis, SVR methods are needed to be adapted to uncertain targets. The effectiveness of two principle breast features, tumor size and lymph node status, was demonstrated by the combination of sampling and feature selection methods. In sampling, breast data were stratified according to tumor size and lymph node status. Three types of feature selection methods comprised of no selection, individual feature selection, and feature subset forward selection, were employed. The prognosis results were evaluated by comparative study using the following performance metrics: concordance index (CI) and Brier score (BS). Cox regression was employed to compare the results. The support vector regression method (SVCR) performs similarly to Cox regression in three feature selection methods and better than Cox regression in non-feature selection methods measured by CI and BS. Feature selection methods can improve the performance of Cox regression measured by CI. Among all cross validation results, stratified sampling of tumor size achieves the best regression results for both feature selection and non-feature selection methods. The SVCR regression results, perform better than Cox regression when the techniques are used with either CI or BS. The best CI value in the validation results is 0.6845. The best CI value corresponds to the best BS value 0.2065, which were obtained in the combination of SVCR, individual feature selection, and stratified sampling of the number of positive lymph nodes. In addition, we also observe that SVCR performs more consistently than Cox regression in all prognosis studies. The feature selection method does not have a significant impact on the metric values, especially on CI. We conclude that the combinational methods of SVCR, feature selection, and sampling can improve cancer prognosis, but more significant features may further enhance cancer prognosis accuracy.
所提出的技术利用成像特征研究支持向量回归（SVR）在癌症预后方面的优势。从患者身上提取癌症图像特征并记录为截尾数据。为了将截尾数据用于预后分析，需要对SVR方法进行调整以适应不确定的目标。通过采样和特征选择方法的结合，证明了两个主要乳腺特征（肿瘤大小和淋巴结状态）的有效性。在采样过程中，乳腺数据根据肿瘤大小和淋巴结状态进行分层。采用了三种特征选择方法，即无选择、单个特征选择和特征子集向前选择。通过使用以下性能指标的对比研究来评估预后结果：一致性指数（CI）和布里尔评分（BS）。采用Cox回归来比较结果。在三种特征选择方法中，支持向量回归方法（SVCR）的表现与Cox回归相似，而在非特征选择方法中，以CI和BS衡量，SVCR的表现优于Cox回归。特征选择方法可以提高以CI衡量的Cox回归的性能。在所有交叉验证结果中，肿瘤大小的分层采样在特征选择和非特征选择方法中均取得了最佳回归结果。当使用CI或BS时，SVCR回归结果比Cox回归表现更好。验证结果中的最佳CI值为0.6845。最佳CI值对应最佳BS值0.2065，这是在SVCR、单个特征选择和阳性淋巴结数量的分层采样相结合的情况下获得的。此外，我们还观察到，在所有预后研究中，SVCR的表现比Cox回归更稳定。特征选择方法对指标值，尤其是对CI，没有显著影响。我们得出结论，SVCR、特征选择和采样的组合方法可以改善癌症预后，但更显著的特征可能会进一步提高癌症预后的准确性。
