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中华腔镜泌尿外科杂志(电子版)

中华腔镜泌尿外科杂志(电子版) ›› 2022, Vol. 16 ›› Issue (01) : 7 -13. doi: 10.3877/cma.j.issn.1674-3253.2022.01.002

临床研究

基于机器学习模型分析不进行穿刺活检的前列腺根治术的可行性
陶金1, 于栓宝1, 范雅峰1, 董彪1, 洪国栋1, 屈武功1, 任选义2, 张雪培3,†()   
  1. 1. 450052　郑州大学第一附属医院泌尿外科
    2. 475000　河南，开封市中心医院泌尿外科
    3. 450052　郑州大学第一附属医院泌尿外科；450052　河南省慢性肾脏疾病精准诊疗重点实验室
  • 收稿日期:2020-04-13 出版日期:2022-02-01
  • 通信作者: 张雪培
  • 基金资助:
    河南省医学科技攻关计划联合共建项目(LHGJ20190181，LHGJ20200334)

The feasibility of radical prostatectomy without prostate biopsy based on machine-learning models

Jin Tao1, Shuanbao Yu1, Yafeng Fan1, Biao Dong1, Guodong Hong1, Wugong Qu1, Xuanyi Ren2, Xuepei Zhang3,†()   

  1. 1. Department of Urology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
    2. Department of Urology, the Central Hospital of Kaifeng, Henan 475000, China
    3. Department of Urology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China; Key Laboratory of Precision Diagnosis and Treatment for Chronic Kidney Disease in Henan Province, Zhengzhou 450052, China
  • Received:2020-04-13 Published:2022-02-01
  • Corresponding author: Xuepei Zhang
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引用本文：

陶金, 于栓宝, 范雅峰, 董彪, 洪国栋, 屈武功, 任选义, 张雪培. 基于机器学习模型分析不进行穿刺活检的前列腺根治术的可行性[J]. 中华腔镜泌尿外科杂志(电子版), 2022, 16(01): 7-13.

Jin Tao, Shuanbao Yu, Yafeng Fan, Biao Dong, Guodong Hong, Wugong Qu, Xuanyi Ren, Xuepei Zhang. The feasibility of radical prostatectomy without prostate biopsy based on machine-learning models[J]. Chinese Journal of Endourology(Electronic Edition), 2022, 16(01): 7-13.

目的

基于机器学习算法构建前列腺癌（PCa）和临床显著前列腺癌（CSPCa）的诊断模型，并探讨不进行穿刺活检的前列腺根治术的可行性。

方法

回顾性收集2016年4月至2020年3月期间符合纳入标准的688例前列腺穿刺患者的临床资料，包括年龄、PSA、前列腺体积、磁共振报告和穿刺活检病理结果，基于人工神经网络（ANN）、支持向量机（SVM）、随机森林（RF）和Logistic回归（LR）算法，构建PCa和CSPCa的诊断模型，采用受试者工作特征曲线评价各模型的诊断准确性，并分析不进行穿刺活检的前列腺根治术的可行性。

结果

ANN（AUC=0.891和0.911）、SVM（AUC=0.903和0.925）、RF（AUC=0.897和0.916）和LR（AUC=0.894和0.917）模型预测前列腺癌和临床显著前列腺癌的诊断效能均优于PSA检测（AUC=0.805和0.792）和多参数磁共振检查（AUC=0.773和0.807） （P均<0.05）。SVM模型预测PCa和CSPCa的诊断效能均高于其他模型，但各模型间的诊断效能差异不显著（P均>0.05）。基于验证数据集，SVM的PCa预测模型能够使44例（44/208，21%）患者直接进行前列腺根治术，包括41例（41/65，63%）CSPCa和3例（3/12，25%）GS=3+3的PCa病例，而不过度治疗前列腺穿刺结果为良性的病例，优于ANN（33/208，16%）、RF（17%，36/208）和LR（35/208，17%）模型。

结论

机器学习模型预测PCa和CSPCa的诊断效能优于多参数磁共振等单一临床检查，能够使超过60%的CSPCa病例直接进行前列腺根治术，而不会导致过度治疗良性前列腺病例。但需开展前瞻性研究进一步验证该研究结果。

关键词: 前列腺癌, 机器学习, 穿刺, 算法, 模型
Objective

To develop machine-learning models for prediction of prostate cancer (PCa) and clinically significant prostate cancer (CSPCa), and explore the feasibility of radical prostatectomy without prostate biopsy based on machine-learning models.

Methods

The total of 688 patients who underwent prostate biopsy according to inclusion criteria between April 2016 and March 2020 were enrolled retrospectively, and the clinical data including age, PSA test, prostate volume (PV), reports of mpMRI examination, and pathological results of prostate biopsy were collected. The diagnostic models of PCa and CSPCa were constructed based on Artificial Neural Network (ANN), Support Vector Machine (SVC), Random Forest (RF) and Logistic Regression (LR) algorithms; and examined with receiver operating characteristic curves. Additionally, we analyzed the feasibility of radical prostatectomy without prostate biopsy based on machine-learning models.

Results

The ANN (AUC=0.89 and 0.911), SVM (AUC=0.903 and 0.925), RF (AUC=0.897 and 0.916), and LR (AUC=0.894 and 0.917) models outperformed PSA test (AUC=0.805 and 0.792) and mpMRI examination (AUC=0.773 and 0.807) in prediction of PCa and CSPCa (all P<0.05). The SVM models had higher diagnostic accuracy than other models for PCa and CSPCa, but the difference was not significant among the models (each P>0.05). The SVM model for PCa enabled 44(44/208, 21%) patients to undergo radical prostatectomy without prostate biopsy, including 41(41/65, 63%) cases of CSPCa and 3(3/12, 25%) cases of PCa (GS=3+3), without overtreatment of cases with benign biopsy in the validation cohort.

Conclusions

The machine-learning models outperformed single clinical examination such as mpMRI in prediction of PCa and CSPCa, enabling more than 60% of CSPCa cases to undergo radical prostatectomy without prostate biopsy, and would not lead to overtreatment of benign prostate cases. Further prospective validation is required.

Key words: Prostate cancer, Machine learning, Piopsy, Algorithms, Model
表1 不同Gleason评分的前列腺穿刺病例的临床特征比较[M(IQR)]
  例数 年龄（岁） tPSA（ng/ml） f/tPSA PSAD（ng/ml2） PV（ml）
良性前列腺 443 66(61~72) 10.5(6.65~17.0) 0.15(0.10~0.21) 0.18(0.11~0.29) 58(39~82)
GS=3+3 45 70(66~75) 14.2(7.70~19.4) 0.14(0.10~0.18) 0.28(0.16~0.47) 47(30~70)
GS=3+4 43 69(62~77) 19.7(11.8~27.0) 0.10(0.07~0.13) 0.57(0.36~0.68) 35(24~56)
GS=4+3 61 70(63~75) 25.5(11.9~34.1) 0.11(0.07~0.17) 0.62(0.31~1.06) 35(26~49
GS≥4+4 96 70(64~76) 23.8(10.4~34.7) 0.12(0.08~0.21) 0.44(0.25~0.72) 41(30~63)
P值   <0.001 <0.001 <0.001 <0.001 <0.001
  磁共振诊断前列腺癌[例(%)] 磁共振诊断精囊腺侵犯[例(%)] 磁共振诊断淋巴结转移[例(%)]
阴性 不确定 可疑 阴性 不确定 可疑 阴性 不确定 可疑
良性前列腺 250(56) 95(21) 98(22) 439(99) 1(0.2) 3(0.7) 432(98) 11(2) 0(0)
GS=3+3 14(31) 9(20) 22(49) 42(93) 0(0) 3(7) 43(96) 1(2) 1(2)
GS=3+4 11(26) 8(19) 24(56) 38(88) 1(2) 4(9) 41(95) 0(0) 2(5)
GS=4+3 6(10) 3(5) 52(85) 40(66) 5(8) 16(26) 52(85) 3(5) 6(10)
GS≥4+4 6(6) 9(9) 81(84) 43 (45) 1(1) 52(54) 66(69) 14(15) 16(17)
P值 <0.001 <0.001 <0.001
图1 不同模型及参数预测前列腺癌和临床显著前列腺癌的ROC曲线。（a）不同模型和参数预测前列腺癌的ROC曲线；（b）不同模型和参数预测临床显著前列腺癌的ROC曲线注：ANN：人工神经网络；SVM：支持向量机；RF：随机森林；LR：Logistic回归；mpMRI：多参数磁共振；PSA：前列腺特异性抗原检测
图2 机器学习算法的PCa和CSPCa模型预测不同Gleason评分穿刺病例的概率分布：（a）人工神经网络PCa模型的预测概率分布；（b）支持向量机PCa模型的预测概率分布；（c）随机森林PCa模型的预测概率分布；（d）Logistic回归PCa模型的预测概率分布；（e）人工神经网络CSPCa模型的预测概率分布；（f）支持向量机CSPCa模型的预测概率分布；（g）随机森林CSPCa模型的预测概率分布；（h）Logistic回归CSPCa模型的预测概率分布注：GS：Gleason评分；三条灰色虚线（从上到下）分别代表各模型诊断PCa的特异度分别为100%，99%和95%
表2 基于验证队列，机器学习和Logistic回归模型在特定前列腺癌检测特异度条件下直接前列腺癌根治术的比例和过度治疗良性前列腺病例的比例
模型 前列腺癌检测特异度 预测概率阈值 前列腺癌检测灵敏度 直接前列腺根治术(n=208)[例(%)] 过度治疗良性前列腺(n=131)[例(%)] 直接前列腺癌根治术[例(%)]
GS=3+3(n=12) GS=3+4(n=11) GS=4+3(n=18) GS≥4+4(n=36)
前列腺癌预测模型                
ANN 131/131(100%) 79% 33/77(43%) 33(16) 0(0) 1(8) 2(18) 11(61) 19(53)
SVM 131/131(100%) 65% 44/77(57%) 44(21) 0(0) 3(25) 3(27) 14(78) 24(67)
RF 131/131(100%) 69% 36/77(47%) 36(17) 0(0) 1(8) 1(9) 12(67) 22(61)
LR 131/131(100%) 83% 35/77(45% 35(17) 0(0) 1(8) 2(18) 12(67) 20(56)
ANN 130/131(99%) 75% 34/77(44%) 35(17) 1(1) 2(17) 2(18) 11(61) 19(53)
SVM 130/131(99%) 61% 45/77(58%) 46(22) 1(1) 3(25) 4(36) 14(78) 24(67)
RF 130/131(99%) 56% 43/77(56%) 44(21) 1(1) 2(17) 2(18) 14(78) 25(69)
LR 130/131(99%) 70% 44/77(57%) 45(22) 1(1) 3(25) 3(27) 14(78) 24(67)
ANN 125/131(95%) 57% 47/77(61%) 53(25) 6(5) 3(25) 4(36) 15(83) 25(69)
SVM 125/131(95%) 47% 50/77(65%) 56(27) 6(5) 4(33) 5(45) 15(83) 26(72)
RF 125/131(95%) 49% 52/77(68%) 58(28) 6(5) 3(25) 5(45) 15(83) 29(81)
LR 125/131(95%) 53% 51/77(66%) 57(27) 6(5) 4(33) 5(45) 16(89) 26(72)
临床显著前列腺癌预测模型              
ANN 131/131(100%) 70% 27/77(35%) 27(13) 0(0) 0(0) 0(0) 10(56) 17(47)
SVM 131/131(100%) 44% 43/77(56%) 43(21) 0(0) 3(25) 3(27) 14(78) 23(64)
RF 131/131(100%) 62% 31/77(40%) 31(15) 0(0) 1(8) 1(9) 11(61) 18(50)
LR 131/131(100%) 66% 36/77(47%) 36(17) 0(0) 2(17) 2(18) 12(67) 20(56)
ANN 130/131(99%) 55% 37/77(48%) 38(18) 1(1) 1(8) 1(9) 13(72) 22(61)
SVM 130/131(99%) 41% 44/77(57%) 45(22) 1(1) 3(25) 3(27) 14(78) 24(67)
RF 130/131(99%) 51% 40/77(52%) 41(20) 1(1) 2(17) 1(9) 14(78) 23(64)
LR 130/131(99%) 59% 41/77(53%) 42(20) 1(1) 2(17) 2(18) 13(72) 24(67)
ANN 125/131(95%) 43% 44/77(57%) 50(24) 6(5) 2(17) 3(27) 14(78) 25(69)
SVM 125/131(95%) 31% 49/77(64%) 55(26) 6(5) 4(33) 5(45) 14(78) 26(72)
RF 125/131(95%) 42% 49/77(64%) 55(26) 6(5) 3(25) 5(45) 14(78) 27(75)
LR 125/131(95%) 40% 51/77(66%) 57(27) 6(5) 5(42) 5(45) 15(83) 26(72)
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