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Differences in histopathological and biochemical outcomes in patients with low Gleason score prostate cancer.低 Gleason 评分前列腺癌患者的组织病理学和生化结局差异。
BJU Int. 2010 Mar;105(6):818-23. doi: 10.1111/j.1464-410X.2009.08841.x. Epub 2009 Sep 14.
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The role of biopsy core number in selecting prostate cancer patients for active surveillance.活检核心数量在选择前列腺癌患者进行主动监测中的作用。
Eur Urol. 2009 Dec;56(6):891-8. doi: 10.1016/j.eururo.2009.07.053. Epub 2009 Aug 12.
3
Percentage of tumour-positive biopsy cores: an independent predictor of extraprostatic disease.
Scand J Urol Nephrol. 2009;43(2):109-13. doi: 10.1080/00365590802670348.
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Pathological outcomes of candidates for active surveillance of prostate cancer.前列腺癌主动监测候选者的病理结果。
J Urol. 2009 Apr;181(4):1628-33; discussion 1633-4. doi: 10.1016/j.juro.2008.11.107. Epub 2009 Feb 23.
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Currently used criteria for active surveillance in men with low-risk prostate cancer: an analysis of pathologic features.目前用于低风险前列腺癌男性主动监测的标准:病理特征分析
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Prognostic value of various morphometric measurements of tumour extent in prostate needle core tissue.前列腺穿刺针芯组织中肿瘤范围的各种形态测量的预后价值。
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Does the extent of carcinoma in prostatic biopsies predict prostate-specific antigen recurrence? A systematic review.前列腺活检中癌的范围能否预测前列腺特异性抗原复发?一项系统评价。
Eur Urol. 2008 Oct;54(4):728-39. doi: 10.1016/j.eururo.2008.06.068. Epub 2008 Jun 26.
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Clinical and pathological features of screen vs non-screen-detected prostate cancers: is there a difference?
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Complications of open radical retropubic prostatectomy in potential candidates for active monitoring.积极监测潜在候选者行耻骨后根治性前列腺切除术的并发症。
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Tyrol Prostate Cancer Demonstration Project: early detection, treatment, outcome, incidence and mortality.蒂罗尔前列腺癌示范项目:早期检测、治疗、结果、发病率和死亡率。
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保守治疗前列腺癌活检队列中癌症程度的测量。

Measurements of cancer extent in a conservatively treated prostate cancer biopsy cohort.

机构信息

Centre for Molecular Oncology and Imaging, Queen Mary University of London, UK.

出版信息

Virchows Arch. 2010 Nov;457(5):547-53. doi: 10.1007/s00428-010-0971-z. Epub 2010 Sep 9.

DOI:10.1007/s00428-010-0971-z
PMID:20827488
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3853376/
Abstract

The optimal method for measuring cancer extent in prostate biopsy specimens is unknown. Seven hundred forty-four patients diagnosed between 1990 and 1996 with prostate cancer and managed conservatively were identified. The clinical end point was death from prostate cancer. The extent of cancer was measured in terms of number of cancer cores (NCC), percentage of cores with cancer (PCC), total length of cancer (LCC) and percentage length of cancer in the cores (PLC). These were correlated with prostate cancer mortality, in univariate and multivariate analysis including Gleason score and prostate-specific antigen (PSA). All extent of cancer variables were significant predictors of prostate cancer death on univariate analysis: NCC, hazard ration (HR) = 1.15, 95% confidence interval (CI) = 1.04-1.28, P = 0.011; PPC, HR = 1.01, 95% CI = 1.01-1.02, P < 0.0001; LCC, HR = 1.02, 95% CI = 1.01-1.03, P = 0.002; PLC, HR = 1.01, 95% CI = 1.01-1.02, P = 0.0001. In multivariate analysis including Gleason score and baseline PSA, PCC and PLC were both independently significant P = 0.004 and P = 0.012, respectively, and added further information to that provided by PSA and Gleason score, whereas NNC and LCC were no longer significant (P = 0.5 and P = 0.3 respectively). In a final model, including both extent of cancer variables, PCC was the stronger, adding more value than PLC (χ² (1df) = 7.8, P = 0.005, χ² (1df) = 0.5, P = 0.48 respectively). Measurements of disease burden in needle biopsy specimens are significant predictors of prostate-cancer-related death. The percentage of positive cores appeared the strongest predictor and was stronger than percentage length of cancer in the cores.

摘要

在前列腺活检标本中测量癌症程度的最佳方法尚不清楚。我们确定了 1990 年至 1996 年间诊断为前列腺癌并接受保守治疗的 744 例患者。临床终点是死于前列腺癌。癌症的程度通过癌核数(NCC)、有癌核的百分比(PCC)、癌总长度(LCC)和癌核中癌长度的百分比(PLC)来衡量。在单变量和包括 Gleason 评分和前列腺特异性抗原(PSA)的多变量分析中,这些都与前列腺癌死亡率相关。在单变量分析中,所有癌症程度变量均为前列腺癌死亡的显著预测因素:NCC,危险比(HR)=1.15,95%置信区间(CI)=1.04-1.28,P=0.011;PCC,HR=1.01,95%CI=1.01-1.02,P<0.0001;LCC,HR=1.02,95%CI=1.01-1.03,P=0.002;PLC,HR=1.01,95%CI=1.01-1.02,P=0.0001。在包括 Gleason 评分和基线 PSA 的多变量分析中,PCC 和 PLC 均为独立显著因素(P=0.004 和 P=0.012),并为 PSA 和 Gleason 评分提供的信息提供了更多信息,而 NCC 和 LCC 则不再显著(P=0.5 和 P=0.3)。在包含癌症程度变量的最终模型中,PCC 是更强的预测因素,比 PLC 更有价值(χ²(1df)=7.8,P=0.005,χ²(1df)=0.5,P=0.48)。在针芯活检标本中测量疾病负担是前列腺癌相关死亡的重要预测因素。阳性核的百分比似乎是最强的预测因素,比核中癌长度的百分比更强。