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系统活检和靶向活检与前列腺切除术的可靠性

Reliability of Systematic and Targeted Biopsies versus Prostatectomy.

作者信息

Guan Tianyuan, Sidana Abhinav, Rao Marepalli B

机构信息

College of Public Health, Kent State University, Kent, OH 44240, USA.

Division of the Biological Sciences, The University of Chicago, 5841 S Maryland Avenue, Chicago, IL 60637, USA.

出版信息

Bioengineering (Basel). 2023 Dec 6;10(12):1395. doi: 10.3390/bioengineering10121395.

Abstract

Systematic Biopsy (SBx) has been and continues to be the standard staple for detecting prostate cancer. The more expensive MRI guided biopsy (MRITBx) is a better way of detecting cancer. The prostatectomy can provide an accurate condition of the prostate. The goal is to assess how reliable SBx and MRITBx are vis à vis prostatectomy. Graded Gleason scores are used for comparison. Cohen's Kappa index and logistic regression after binarization of the graded Gleason scores are some of the methods used to achieve our goals. Machine learning methods, such as classification trees, are employed to improve predictability clinically. The Cohen's Kappa index is 0.31 for SBx versus prostatectomy, which means a fair agreement. The index is 0.34 for MRITBx versus prostatectomy, which again means a fair agreement. A direct comparison of SBx versus prostatectomy via binarized graded scores gives sensitivity 0.83 and specificity 0.50. On the other hand, a direct comparison of MRITBx versus prostatectomy gives sensitivity 0.78 and specificity 0.67, putting MRITBx on a higher level of accuracy. The SBx and MRITBx do not yet match the findings of prostatectomy completely, but they are useful. We have developed new biomarkers, considering other pieces of information from the patients, to improve the accuracy of SBx and MRITBx. From a clinical point of view, we provide a prediction model for prostatectomy Gleason grades using classification tree methodology.

摘要

系统活检(SBx)一直是且仍然是检测前列腺癌的标准方法。更昂贵的磁共振成像引导活检(MRITBx)是检测癌症的更好方法。前列腺切除术可以提供前列腺的准确状况。目标是评估SBx和MRITBx相对于前列腺切除术的可靠性。采用分级Gleason评分进行比较。分级Gleason评分二值化后的Cohen's Kappa指数和逻辑回归是用于实现我们目标的一些方法。机器学习方法,如分类树,被用于临床提高预测能力。SBx与前列腺切除术的Cohen's Kappa指数为0.31,这意味着一致性一般。MRITBx与前列腺切除术的指数为0.34,同样意味着一致性一般。通过二值化分级评分直接比较SBx与前列腺切除术,敏感性为0.83,特异性为0.50。另一方面,直接比较MRITBx与前列腺切除术,敏感性为0.78,特异性为0.67,这使MRITBx具有更高的准确性水平。SBx和MRITBx尚未完全与前列腺切除术的结果相匹配,但它们是有用的。我们考虑了患者的其他信息,开发了新的生物标志物,以提高SBx和MRITBx的准确性。从临床角度来看,我们使用分类树方法提供了一个前列腺切除术Gleason分级的预测模型。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a779/10740569/e1681bfe79b6/bioengineering-10-01395-g001.jpg

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