Division of Urologic Surgery, Duke University Medical Center, Durham, North Carolina 27710, USA.
J Urol. 2009 Jul;182(1):118-22; discussion 123-4. doi: 10.1016/j.juro.2009.02.127. Epub 2009 May 17.
We determined clinical factors affecting the under grading of biopsy Gleason sum compared with prostatectomy pathology and developed a model predicting the probability of under grading.
We analyzed a cohort of 1,701 patients treated for prostate cancer at our institution between 1988 and 2007 with complete biopsy and pathological data available. Patients with a biopsy Gleason sum of 7 or less were included in our analysis. Cases were categorized as under graded or not under graded by comparing biopsy and radical prostatectomy Gleason sums. Logistic regression was used to determine the predictors of under grading based on clinical variables (race, age at diagnosis, body mass index, prostate weight, diagnostic prostate specific antigen, biopsy positive-to-total core ratio, maximal cancer percent in positive cores and time from diagnosis to surgery). A nomogram was developed to calculate the probability of under grading. Results were validated using bootstrapping.
Under grading occurred in 46.6% of our cohort. Significant variables predicting under grading were age at diagnosis, biopsy Gleason sum, diagnostic prostate specific antigen, prostate weight, biopsy positive-to-total core ratio and maximal percent of cancer in cores (p <0.05). Nomogram predictive accuracy was 72.4%.
The risk of Gleason sum under grading can be predicted to a satisfactory level using our nomogram. Predicting under grading would improve patient consulting and identify those who should consider repeat biopsy, ultimately enhancing the accuracy of prostate cancer diagnosis.
我们确定了影响活检 Gleason 评分与前列腺切除术病理分级不符的临床因素,并建立了一个预测低估分级概率的模型。
我们分析了 1988 年至 2007 年在我们机构接受治疗的 1701 例前列腺癌患者的完整活检和病理数据。我们的分析纳入了活检 Gleason 评分 7 分及以下的患者。通过比较活检和根治性前列腺切除术的 Gleason 评分,将病例分为分级不足或分级不足。基于临床变量(种族、诊断时年龄、体重指数、前列腺重量、诊断前列腺特异性抗原、活检阳性核心与总核心比、阳性核心中最大癌症百分比和诊断至手术时间),采用逻辑回归确定低估分级的预测因素。建立了一个列线图来计算低估分级的概率。使用 bootstrap 验证结果。
我们的队列中有 46.6%的病例存在分级不足。预测分级不足的显著变量是诊断时年龄、活检 Gleason 评分、诊断前列腺特异性抗原、前列腺重量、活检阳性核心与总核心比和核心中最大癌症百分比(p <0.05)。列线图预测准确性为 72.4%。
使用我们的列线图可以预测 Gleason 评分低估分级的风险,达到令人满意的水平。预测分级不足将改善患者咨询,并确定那些应考虑重复活检的患者,最终提高前列腺癌诊断的准确性。