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病灶 PSA 密度:一个利用临床和病理数据预测前列腺癌患者生化复发的数学公式。

PSA density of the lesion: a mathematical formula that uses clinical and pathological data to predict biochemical recurrence in prostate cancer patients.

机构信息

- Hospital Oswaldo Cruz, Centro de Cirurgia Robótica - São Paulo - SP - Brasil.

- Faculdade de Medicina da Universidade de São Paulo, Urologia, Laboratório de Investigação Médica - São Paulo - SP - Brasil.

出版信息

Rev Col Bras Cir. 2021 Oct 11;48:e20212965. doi: 10.1590/0100-6991e-20212965. eCollection 2021.

DOI:10.1590/0100-6991e-20212965
PMID:34644741
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10683463/
Abstract

A main challenge in the clinical management of prostate cancer is to identify which tumor is aggressive and needs invasive treatment. Thus, being able to predict which cancer will progress to biochemical recurrence is a great strategy to stratify prostate cancer patients. With that in mind, we created a mathematical formula that takes into account the patients clinical and pathological data resulting in a quantitative variable, called PSA density of the lesion, which has the potential to predict biochemical recurrence. To test if our variable is able to predict biochemical recurrence, we use a cohort of 219 prostate cancer patients, associating our new variable and classic parameters of prostate cancer with biochemical recurrence. Total PSA, lesion weight, volume and classic PSA density were positively associated with biochemical recurrence (p<0.05). ISUP score was also associated with biochemical recurrence in both biopsy and surgical specimen (p<0.001). The increase of PSA density of the lesion was significantly associated with the biochemical recurrence (p=0.03). Variables derived from the formula, PSA 15% and PSA 152, were also positive associated with the biochemical recurrence (p=0.01 and p=0.002 respectively). Logistic regression analysis shows that classic PSA density, PSA density of the lesion and total PSA, together, can explain up to 13% of cases of biochemical recurrence. PSA density of the lesion alone would have the ability to explain up to 7% of cases of biochemical recurrence. In conclusion, this new mathematical approach could be a useful tool to predict disease recurrence in prostate cancer.

摘要

在前列腺癌的临床管理中,主要挑战是确定哪些肿瘤具有侵袭性,需要进行侵入性治疗。因此,能够预测哪些癌症会进展为生化复发是对前列腺癌患者进行分层的重要策略。考虑到这一点,我们创建了一个数学公式,该公式考虑了患者的临床和病理数据,从而产生了一个称为病变 PSA 密度的定量变量,该变量有可能预测生化复发。为了测试我们的变量是否能够预测生化复发,我们使用了 219 例前列腺癌患者的队列,将我们的新变量和前列腺癌的经典参数与生化复发相关联。总 PSA、病变重量、体积和经典 PSA 密度与生化复发呈正相关(p<0.05)。ISUP 评分在活检和手术标本中也与生化复发相关(p<0.001)。病变 PSA 密度的增加与生化复发显著相关(p=0.03)。源自公式的变量,PSA 15%和 PSA 152,也与生化复发呈正相关(p=0.01 和 p=0.002 分别)。逻辑回归分析表明,经典 PSA 密度、病变 PSA 密度和总 PSA 一起可以解释高达 13%的生化复发病例。单独的病变 PSA 密度将有能力解释高达 7%的生化复发病例。总之,这种新的数学方法可能是预测前列腺癌疾病复发的有用工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2d39/10683463/b53727446c14/rcbc-48-e20212965-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2d39/10683463/4baac71a2202/rcbc-48-e20212965-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2d39/10683463/d58b3b0f21f4/rcbc-48-e20212965-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2d39/10683463/b53727446c14/rcbc-48-e20212965-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2d39/10683463/4baac71a2202/rcbc-48-e20212965-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2d39/10683463/d58b3b0f21f4/rcbc-48-e20212965-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2d39/10683463/b53727446c14/rcbc-48-e20212965-g002.jpg

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本文引用的文献

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