摘要
目的探索膀胱癌患者手术治疗生存预后的影响因素,形成人工智能推荐算法及软件转化,用于对不同手术方案治疗效果的术前预判。方法纳入2007年1月-2019年1月在大连医科大学附属第二医院(大医二院)和南方医科大学南方医院(南方医院)初诊、行手术治疗、有完整临床资料及随访数据的膀胱癌患者资料。采用深度神经网络(DNN)建立人工智能算法模型,建立患者基础及治疗因素对生存预后的模型预测,通过人工智能算法探索生存影响因素并排序。结果共纳入膀胱癌患者832例,其中438(52.64%)例在大医二院接受治疗,394(47.36%)例就诊于南医;579(69.6%)例为非肌层浸润性膀胱癌,253(30.4%)例为肌层浸润性膀胱癌;539(64.8%)例接受经尿道膀胱肿瘤电切,66(7.9%)例接受膀胱部分切除,227(27.3%)例接受膀胱全切。纳入大医二院患者数据为训练组进行DNN建模,采用南医患者数据为测试组进行建模后外部验证,依据权重得出影响患者生存预后的因素由高到低依次是T分期、病理分级、高血压或心脑血管疾病、血红蛋白、血钙、吸烟、白蛋白、淋巴细胞、年龄、白蛋白/球蛋白比、术式、N分期、肌酐清除率。模型可用于患者术前预测。结论通过DNN建模及内外部验证,可以较为准确预测膀胱癌患者术后生存的影响因素,并用于患者术前手术效果预测,为患者术式选择及术后随访方案的制定提供软件和人工智能算法支持。
Objective To explore the factors influencing the survival and prognosis of patients with bladder urothelial carcinoma(BUC)after surgical treatment,and to establish an artificial intelligence algorithm to predict the effects of different surgical regimens.Methods BUC patients treated with surgery during Jan.2007 and Jan.2019 in The Second Hospital of Dalian Medical University and Nanfang Hospital of Southern Medical University were enrolled.The complete clinical and follow-up data were collected.Deep neural network(DNN)was used to establish an artificial intelligence algorithm model.A prediction model of survival and prognosis was established,and the influencing factors of survival were explored and ranked by the artificial intelligence algorithm.Results A total of 832 patients were involved,including 438(52.64%)treated in The Second Hospital of Dalian Medical University,and 394(47.36%)treated in Nanfang Hospital of Southern Medical University.Of all cases,579(69.6%)were non-muscle invasive bladder cancer,and 253(30.4%)were muscle invasive bladder cancer.Transurethral resection of bladder tumor was conducted in 539(64.8%)cases,partial cystectomy in 66(7.9%)cases,and total cystectomy in 227(27.3%)cases.The data of patients treated in Second Hospital of Dalian Medical University were used for DNN modeling,and the data of patients treated in Nanfang Hospital of Southern Medical University were used for external verification after modeling.Finally,it was concluded that the factors affecting survival and prognosis were T stage,pathological grade,hypertension or cardiovascular and cerebrovascular disease,hemoglobin,blood calcium,smoking,albumin,lymphocytes,age,ratio of albumin/globulin,operation method,N stage,and creatinine clearance rate in descending order.The model could be used for preoperative prediction.Conclusion Through DNN modeling and external verification,the influencing factors of postoperative survival can be predicted for patients with bladder cancer,and the surgical effects can also be predicted before o
作者
张玥
张策
杨玻
沈惠文
马得原
温立洁
谭万龙
于洋
ZHANG Yue;ZHANG Ce;YANG Bo;SHEN Huiwen;MA Deyuan;WEN Lijie;TAN Wanlong;YU Yang(Department of Urology,The Second Hospital of Dalian Medical University,Dalian 116000;Department of Development Planning and Quality Management,The Second Hospital of Dalian Medical University,Dalian 116000;Department of Pharmacy,The Second Hospital of Dalian Medical University,Dalian 116000;Department of Urology,Nanfang Hospital of Southern Medical University,Guangzhou 510080,China)
出处
《现代泌尿外科杂志》
CAS
2023年第6期480-486,496,共8页
Journal of Modern Urology
关键词
膀胱尿路上皮癌
手术治疗
深度学习算法
生存预后
手术预后系统
bladder urothelial carcinoma
surgical treatment
deep learning algorithm
survival prognosis
surgery prognosis system
