Table_1_Develop prediction model to help forecast advanced prostate cancer patients’ prognosis after surgery using neural network.docx

BackgroundThe effect of surgery on advanced prostate cancer (PC) is unclear and predictive model for postoperative survival is lacking yet.MethodsWe investigate the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) database, to collect clinical features of advanced PC patients. According to clinical experience, age, race, grade, pathology, T, N, M, stage, size, regional nodes positive, regional nodes examined, surgery, radiotherapy, chemotherapy, history of malignancy, clinical Gleason score (composed of needle core biopsy or transurethral resection of the prostate specimens), pathological Gleason score (composed of prostatectomy specimens) and prostate-specific antigen (PSA) are the potential predictive variables. All samples are divided into train cohort (70% of total, for model training) and test cohort (30% of total, for model validation) by random sampling. We then develop neural network to predict advanced PC patients’ overall. Area under receiver operating characteristic curve (AUC) is used to evaluate model’s performance.Results6380 patients, diagnosed with advanced (stage III-IV) prostate cancer and receiving surgery, have been included. The model using all collected clinical features as predictors and based on neural network algorithm performs best, which scores 0.7058 AUC (95% CIs, 0.7021-0.7068) in train cohort and 0.6925 AUC (95% CIs, 0.6906-0.6956) in test cohort. We then package it into a Windows 64-bit software.ConclusionPatients with advanced prostate cancer may benefit from surgery. In order to forecast their overall survival, we first build a clinical features-based prognostic model. This model is accuracy and may offer some reference on clinical decision making.

背景：对于晚期前列腺癌（PC）手术的影响尚不明确，且缺乏预测术后生存率的预测模型。方法：本研究调查了美国国家癌症研究所的癌症监测、流行病学和结果（SEER）数据库，收集了晚期前列腺癌患者的临床特征。根据临床经验，年龄、种族、分级、病理学、T、N、M、分期、大小、区域性淋巴结阳性、区域性淋巴结检查、手术、放疗、化疗、恶性肿瘤病史、临床Gleason评分（由针吸活检或经尿道前列腺切除标本组成）、病理学Gleason评分（由前列腺切除术标本组成）以及前列腺特异性抗原（PSA）均被视为潜在的预测变量。所有样本通过随机抽样分为训练组（占总样本的70%，用于模型训练）和测试组（占总样本的30%，用于模型验证）。随后，我们开发了基于神经网络的模型来预测晚期前列腺癌患者的总体情况。使用受试者工作特征曲线下面积（AUC）来评估模型性能。结果：共纳入6380名被诊断为晚期（III-IV期）前列腺癌并接受手术的患者。使用所有收集到的临床特征作为预测因子并基于神经网络算法的模型表现最佳，在训练组中AUC得分为0.7058（95%置信区间，0.7021-0.7068），在测试组中AUC得分为0.6925（95%置信区间，0.6906-0.6956）。随后，我们将该模型封装成Windows 64位软件。结论：晚期前列腺癌患者可能从手术中获益。为了预测他们的总体生存情况，我们首先构建了一个基于临床特征的预后模型。该模型具有较高的准确性，并可能在临床决策中提供一定的参考。