机器学习模型在预测肾结石输尿管软镜碎石术后早期结石清除率中的应用

doi:10.19723/j.issn.1671-167X.2019.04.010

Abstract

Abstract:

Objective: To establish predictive models based on random forest and XGBoost machine learning algorithm and to investigate their value in predicting early stone-free rate (SFR) after flexible ureteroscopic lithotripsy (fURL) in patients with renal stones.Methods: The clinical data of 201 patients with renal stones who underwent fURL were retrospectively investigated. According to the stone-free standard, the patients were divided into stone-free group (SF group) and stone-residual group (SR group). We compared a number of factors including patient age, body mass index (BMI), stone number, stone volume, stone density and hydronephrosis between the two groups. For low calyceal calculi, renal anatomic parameters including infundibular angle (IPA), infundibular width (IW), infundibular length (IL) and pelvic calyceal height (PCH), would be measured. We brought above potential predictive factors into random forest and XGBoost machine learning algorithm respectively to develop two predictive models. The receiver operating characteristic curve (ROC curve) was established in order to test the predictive ability of the model. Clinical data of 71 patients were collected prospectively to validate the predictive models externally.Results: In this study, 201 fURL operations were successfully completed. The one-phase early SFR was 61.2%. We built two predictive models based on random forest and XGBoost machine learning algorithm. The predictive variables’ importance scores were obtained. The area under the ROC curve (AUROC) of the two predictive models for early stone clearance status prediction was 0.77. In the study, 71 test samples were used for external validation. The results showed that the total predictive accuracy, predictive specificity and predictive sensitivity of the random forest and XGBoost models were 75.7%, 82.6%, 60.0%, and 81.4%, 87.0%, 68.0%, respectively. The first four predictive variables in importance were stone volume, mean stone density, maximal stone density and BMI in both random forest and XGBoost predictive models.Conclusion: The predictive models based on random forest and XGBoost machine learning algorithm can predict postoperative early stone status after fURL for renal stones accurately, which will facilitate preoperative evaluation and clinical decision-making. Stone volume, mean stone density, maximal stone density and BMI may be the important predictive factors affecting early SFR after fURL for renal stones.

Key words: Machine learning, Random forest, XGBoost, Renal stones, Stone-free rate, Predictive model

CLC Number:

R692.4

Xue-hua ZHU,Ming-yu YANG,Hai-zhui XIA,Wei HE,Zhi-ying ZHANG,Yu-qing LIU,Chun-lei XIAO,Lu-lin MA,Jian LU. Application of machine learning models in predicting early stone-free rate after flexible ureteroscopic lithotripsy for renal stones[J].Journal of Peking University(Health Sciences), 2019, 51(4): 653-659.

Figures/Tables 6

Figure 1

Table 1

Comparison of demographic data and stone characteristics between stone-free group and stone-residual group"

Items	Stone-free group (n=123)	Stone-residual group (n=78)	P value
Male, n (%)	75.0 (61.0)	44.0 (56.4)	0.163^a
Age/year, $x ?$ ±s	51.4±14.4	50.5±13.4	0.655^b
BMI/(kg/m²), median (min-max)	25.0 (18.0-33.1)	24.5 (17.7-35.0)	0.475^c
History of urologic operation, n (%)	32.0 (26.0)	27.0 (34.6)	0.192^a
Hydronephrosis, n (%)	67.0 (54.5)	53.0 (68.0)	0.058^a
Low calyceal stones, n (%)	75 (61.0)	51.0 (65.4)	0.887^a
Stone number, median (min-max)	1.0 (1.0-5.0)	1.5 (1.0-5.0)	0.000^c
Stone volume/mm³, median (min-max)	492.7 (8.37-15 055.4)	1 406.7 (133.2-11 992.0)	0.000^c
Mean stone density/Hu, $x ?$ ±s	548.7±273.7	734.2±299.1	0.000^b
Maximal stone density/Hu, median (min-max)	1 049.0 (240.0-2 134.0)	1 266.00 (304.0-1 819.0)	0.000^c
Operation time/min, median (min-max)	89.0 (20.0-326.0)	111.0 (22.0-282.0)	0.000^c

Table 1

Table 2

Comparison of low calyceal anatomic parameters between stone-free group and stone-residual group"

Items	Stone-free group (n=75)	Stone-residual group (n=51)	P value
PCH/mm, $x ?$ ±s	17.0±4.3	21.8±5.3	0.000^b
IPA/(°), median (min-max)	44.3 (29.2-71.5)	34.9 (22.3-44.6)	0.000^c
IL/mm, $x ?$ ±s	22.9±3.7	27.8±5.0	0.000^b
IW/mm, median (min-max)	7.3 (1.0-22.0)	6.8 (1.0-9.5)	0.076^c

Table 2

Figure 2

Figure 3

Table 3

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Application of machine learning models in predicting early stone-free rate after flexible ureteroscopic lithotripsy for renal stones

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Items	Stone-residual	Stone-free	Total
Random forest model, n
Positive	15	8	23
Negative	10	38	48
Total	25	46	71
XGBoost model, n
Positive	17	6	23
Negative	8	40	48
Total	25	46	71

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