Department of Radiology, Charité University Hospital Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Berlin Institute of Health at Charité-Universitätsmedizin Berlin, BIH Biomedical Innovation Academy, Berlin, Germany.
Prostate Cancer Prostatic Dis. 2023 Sep;26(3):543-551. doi: 10.1038/s41391-022-00599-2. Epub 2022 Oct 8.
Magnetic resonance imaging (MRI) is used to detect the prostate index lesion before targeted biopsy. However, the number of biopsy cores that should be obtained from the index lesion is unclear. The aim of this study is to analyze how many MRI-targeted biopsy cores are needed to establish the most relevant histopathologic diagnosis of the index lesion and to build a prediction model.
We retrospectively included 451 patients who underwent 10-core systematic prostate biopsy and MRI-targeted biopsy with sampling of at least three cores from the index lesion. A total of 1587 biopsy cores were analyzed. The core sampling sequence was recorded, and the first biopsy core detecting the most relevant histopathologic diagnosis was identified. In a subgroup of 261 patients in whom exactly three MRI-targeted biopsy cores were obtained from the index lesion, we generated a prediction model. A nonparametric Bayes classifier was trained using the PI-RADS score, prostate-specific antigen (PSA) density, lesion size, zone, and location as covariates.
The most relevant histopathologic diagnosis of the index lesion was detected by the first biopsy core in 331 cases (73%), by the second in 66 cases (15%), and by the third in 39 cases (9%), by the fourth in 13 cases (3%), and by the fifth in two cases (<1%). The Bayes classifier correctly predicted which biopsy core yielded the most relevant histopathologic diagnosis in 79% of the subjects. PI-RADS score, PSA density, lesion size, zone, and location did not independently influence the prediction model.
The most relevant histopathologic diagnosis of the index lesion was made on the basis of three MRI-targeted biopsy cores in 97% of patients. Our classifier can help in predicting the first MRI-targeted biopsy core revealing the most relevant histopathologic diagnosis; however, at least three MRI-targeted biopsy cores should be obtained regardless of the preinterventionally assessed covariates.
磁共振成像(MRI)用于在靶向活检前检测前列腺指数病变。然而,从指数病变中获得的活检核心数量尚不清楚。本研究旨在分析需要多少 MRI 靶向活检核心才能建立指数病变的最相关组织病理学诊断,并建立预测模型。
我们回顾性纳入了 451 例接受 10 核系统前列腺活检和 MRI 靶向活检的患者,至少从指数病变中采集 3 个核心的样本。共分析了 1587 个活检核心。记录了核心采样序列,并确定了第一个检测到最相关组织病理学诊断的活检核心。在 261 例恰好从指数病变中获得 3 个 MRI 靶向活检核心的亚组中,我们生成了预测模型。使用 PI-RADS 评分、前列腺特异性抗原(PSA)密度、病变大小、区域和位置作为协变量,使用非参数贝叶斯分类器进行训练。
在 331 例(73%)、66 例(15%)、39 例(9%)、13 例(3%)和 2 例(<1%)患者中,通过第一、第二、第三、第四和第五个活检核心检测到了最相关的组织病理学诊断。贝叶斯分类器正确预测了在 79%的受试者中哪个活检核心获得了最相关的组织病理学诊断。PI-RADS 评分、PSA 密度、病变大小、区域和位置并不能独立影响预测模型。
在 97%的患者中,通过三个 MRI 靶向活检核心即可做出指数病变的最相关组织病理学诊断。我们的分类器可以帮助预测揭示最相关组织病理学诊断的第一个 MRI 靶向活检核心;然而,无论术前评估的协变量如何,至少应获得三个 MRI 靶向活检核心。