Matsui Yoshiyuki, Egawa Shin, Tsukayama Chotatsu, Terai Akito, Kuwao Sadahito, Baba Shiro, Arai Yoichi
Department of Urology, Kurashiki Central Hospital, Okayama, Japan.
Jpn J Clin Oncol. 2002 Dec;32(12):530-5. doi: 10.1093/jjco/hyf114.
Although prostate cancer has been prevalent in Japan, there has been no particular model for predicting the pathological stage in the Japanese population. We examined whether artificial neural network analysis (ANNA), which is a relatively new diagnostic tool in prostate cancer, can be one of the predictive methods for predicting organ confinement, compared with the traditional logistic regression model, in the Japanese population for the first time.
The study population comprised 178 men who underwent radical prostatectomy at our institutions between October 1992 and May 1999. As additional pretreatment parameters to the preoperative serum PSA level, clinical TNM classification and biopsy Gleason score, the percentage of number of cores exhibiting traces of tumor, maximum tumor length in biopsy cores, PSA density and patient age were used. The predictive ability of ANNA with several parameters for a set of 36 randomly selected test data was compared with those of logistic regression analysis and 'Partin Tables' by area under the receiver operating characteristics (ROC) curve analysis.
Of 178 patients, 97 (54.5%) had organ-confined disease but 81 (45.5%) had locally advanced disease. With three parameters, the area under the ROC curve of ANNA (0.825 +/- 0.071) was larger than those for logistic regression (0.782 +/- 0.079) and Partin Tables (0.756 +/- 0.087), but not to a significant extent (P = 0.690 and 0.541). Although the expansion of the parameters did not increase the difference in area under the ROC curve between the best ANNA and logistic regression (0.899 +/- 0.053 and 0.873 +/- 0.065, respectively), the difference between the best ANNA and Partin Tables did not reach but approached statistical significance (P = 0.157).
Although more modeling optimization is necessary to improve the predictive accuracy and generalizability of ANNA, we suggest that there is the possibility for this new predictive method to evolve in the analysis of clinical staging of prostate cancer.
尽管前列腺癌在日本较为普遍，但在日本人群中尚无预测病理分期的特定模型。我们首次在日本人群中研究了人工神经网络分析（ANNA）这一前列腺癌相对较新的诊断工具，与传统逻辑回归模型相比，是否可作为预测器官局限性的预测方法之一。
研究人群包括1992年10月至1999年5月在我们机构接受根治性前列腺切除术的178名男性。作为术前血清PSA水平、临床TNM分类和活检Gleason评分之外的额外预处理参数，使用了显示肿瘤痕迹的穿刺针芯数量百分比、活检针芯中的最大肿瘤长度、PSA密度和患者年龄。通过受试者操作特征（ROC）曲线下面积分析，将具有多个参数的ANNA对一组36个随机选择的测试数据的预测能力与逻辑回归分析和“Partin表”的预测能力进行比较。
178例患者中，97例（54.5%）患有器官局限性疾病，81例（45.5%）患有局部进展性疾病。使用三个参数时，ANNA的ROC曲线下面积（0.825±0.071）大于逻辑回归（0.782±0.079）和Partin表（0.756±0.087），但差异不显著（P = 0.690和0.541）。尽管参数扩展并未增加最佳ANNA与逻辑回归之间ROC曲线下面积的差异（分别为0.899±0.053和0.873±0.065），但最佳ANNA与Partin表之间的差异未达到但接近统计学显著性（P = 0.157）。
尽管需要更多的模型优化来提高ANNA的预测准确性和通用性，但我们认为这种新的预测方法在前列腺癌临床分期分析中有可能得到发展。
