Potter S R, Miller M C, Mangold L A, Jones K A, Epstein J I, Veltri R W, Partin A W
James Buchanan Brady Urological Institute, Johns Hopkins Medical Institution, Baltimore, Maryland 21287-2101, USA.
Urology. 1999 Nov;54(5):791-5. doi: 10.1016/s0090-4295(99)00328-3.
To use pathologic, morphometric, DNA ploidy, and clinical data to develop and test a genetically engineered neural network (GENN) for the prediction of biochemical (prostate-specific antigen [PSA]) progression after radical prostatectomy in a select group of men with clinically localized prostate cancer.
Two hundred fourteen men who underwent anatomic radical retropubic prostatectomy for clinically localized prostate cancer were selected on the basis of adequate follow-up, pathologic criteria indicating an intermediate risk of progression, and availability of archival tissue. The median age was 58.9 years (range 40 to 87). Men with Gleason score 5 to 7 and clinical Stage T1b-T2c tumors were included. Follow-up was a median of 9.5 years. Three GENNs were developed using pathologic findings (Gleason score, extraprostatic extension, surgical margin status), age, quantitative nuclear grade (QNG), and DNA ploidy. These networks were developed using three randomly selected training (n = 136) and testing (n = 35) sets. Different variable subsets were compared for the ability to maximize prediction of progression. Both standard logistic regression and Cox regression analyses were used concurrently to calculate progression risk.
Biochemical (PSA) progression occurred in 84 men (40%), with a median time to progression of 48 months (range 1 to 168). GENN models were trained using inputs consisting of (a) pathologic features and patient age; (b) QNG and DNA ploidy; and (c) all variables combined. These GENN models achieved an average accuracy of 74.4%, 63.1 %, and 73.5%, respectively, for the prediction of progression in the training sets. In the testing sets, the three GENN models had an accuracy of 74.3%, 80.0%, and 78.1%, respectively.
The GENN models developed show promise in predicting progression in select groups of men after radical prostatectomy. Neural networks using QNG and DNA ploidy as input variables performed as well as networks using Gleason score and staging information. All GENN models were superior to logistic regression modeling and to Cox regression analysis in prediction of PSA progression. The development of models using improved input variables and imaging systems in larger, well-characterized patient groups with long-term follow-up is ongoing.
运用病理学、形态计量学、DNA倍体分析及临床数据，开发并测试一种基因工程神经网络（GENN），用于预测一组经选择的临床局限性前列腺癌男性患者根治性前列腺切除术后的生化（前列腺特异性抗原[PSA]）进展情况。
基于充分的随访、提示进展风险中等的病理标准以及存档组织的可用性，选择214例因临床局限性前列腺癌接受耻骨后根治性前列腺切除术的男性患者。中位年龄为58.9岁（范围40至87岁）。纳入Gleason评分5至7分且临床分期为T1b - T2c期肿瘤的患者。随访时间中位数为9.5年。利用病理结果（Gleason评分、前列腺外扩展、手术切缘状态）、年龄、定量核分级（QNG）和DNA倍体分析开发了三个GENN。这些网络是使用三个随机选择的训练集（n = 136）和测试集（n = 35）开发的。比较不同变量子集在最大化预测进展方面的能力。同时使用标准逻辑回归和Cox回归分析来计算进展风险。
84例男性患者（40%）出现生化（PSA）进展，进展的中位时间为48个月（范围1至168个月）。GENN模型使用以下输入进行训练：（a）病理特征和患者年龄；（b）QNG和DNA倍体分析；（c）所有变量组合。这些GENN模型在训练集中预测进展的平均准确率分别为74.4%、63.1%和73.5%。在测试集中，三个GENN模型的准确率分别为74.3%、80.0%和78.1%。
所开发的GENN模型在预测根治性前列腺切除术后特定男性群体的进展方面显示出前景。使用QNG和DNA倍体分析作为输入变量的神经网络与使用Gleason评分和分期信息的网络表现相当。在预测PSA进展方面，所有GENN模型均优于逻辑回归建模和Cox回归分析。目前正在更大规模、特征明确且有长期随访的患者群体中，利用改进的输入变量和成像系统开发模型。
