Machine Learning Predicts Recurrent Lumbar Disc Herniation Following Percutaneous Endoscopic Lumbar Discectomy
STUDY DESIGN: Retrospective study.
OBJECTIVES: To develop machine learning (ML) models to predict recurrent lumbar disc herniation (rLDH) following percutaneous endoscopic lumbar discectomy (PELD).
METHODS: We retrospectively analyzed 1159 patients who had undergone single-level PELD for lumbar disc herniation (LDH) between July 2014 to December 2019 at our institution. Various preoperative imaging variables and demographic metrics were brought in analysis. Student's t test and Chi-squared test were applied for univariate analysis, which were feature selection for ML models. We established ML models to predict rLDH: Artificial neural networks (ANN), Extreme Gradient Boost classifier (XGBoost), KNeighborsClassifier (KNN), Decision tree classifier (Decision Tree), Random forest classifier (Random Forest), and support vector classifier (SVC).
RESULTS: A total 130 patients (11.22%) were diagnosed as rLDH in 1159 patients. Recurrence occurred within 10.25 ± 11.05 months. Body mass index (BMI) (P = .027), facet orientation (FO) (P < .001), herniation type (P = .012), Modic changes (P = .004), and disc calcification (P = .013) are significant factors in univariate analysis (P < .05). Extreme Gradient Boost classifier, Random Forest, ANN showed fine area under the curve, .9315, .9220, and .8814 respectively.
CONCLUSION: We developed a deep learning and 2 ensemble models with fine performance in prediction of rLDH following PELD. Predicting re-herniation before surgery has the potential to optimize decision-making and meaningfully decrease the rates of rLDH following PELD. Our ML model identified higher BMI, lower FO, Modic changes, disc calcification in a non-protrusive region, and herniation type (noncontained herniation) as significant features for predicting rLDH.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 2023 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:14 |
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| Enthalten in: |
Global spine journal - 14(2023), 1 vom: 05. Jan., Seite 146-152 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Ren, GuanRui [VerfasserIn] |
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| Links: |
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| Themen: |
Deep learning |
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| Anmerkungen: |
Date Revised 27.11.2023 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1177/21925682221097650 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM340281707 |
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| 100 | 1 | |a Ren, GuanRui |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Machine Learning Predicts Recurrent Lumbar Disc Herniation Following Percutaneous Endoscopic Lumbar Discectomy |
| 264 | 1 | |c 2024 | |
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| 500 | |a Date Revised 27.11.2023 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a STUDY DESIGN: Retrospective study | ||
| 520 | |a OBJECTIVES: To develop machine learning (ML) models to predict recurrent lumbar disc herniation (rLDH) following percutaneous endoscopic lumbar discectomy (PELD) | ||
| 520 | |a METHODS: We retrospectively analyzed 1159 patients who had undergone single-level PELD for lumbar disc herniation (LDH) between July 2014 to December 2019 at our institution. Various preoperative imaging variables and demographic metrics were brought in analysis. Student's t test and Chi-squared test were applied for univariate analysis, which were feature selection for ML models. We established ML models to predict rLDH: Artificial neural networks (ANN), Extreme Gradient Boost classifier (XGBoost), KNeighborsClassifier (KNN), Decision tree classifier (Decision Tree), Random forest classifier (Random Forest), and support vector classifier (SVC) | ||
| 520 | |a RESULTS: A total 130 patients (11.22%) were diagnosed as rLDH in 1159 patients. Recurrence occurred within 10.25 ± 11.05 months. Body mass index (BMI) (P = .027), facet orientation (FO) (P < .001), herniation type (P = .012), Modic changes (P = .004), and disc calcification (P = .013) are significant factors in univariate analysis (P < .05). Extreme Gradient Boost classifier, Random Forest, ANN showed fine area under the curve, .9315, .9220, and .8814 respectively | ||
| 520 | |a CONCLUSION: We developed a deep learning and 2 ensemble models with fine performance in prediction of rLDH following PELD. Predicting re-herniation before surgery has the potential to optimize decision-making and meaningfully decrease the rates of rLDH following PELD. Our ML model identified higher BMI, lower FO, Modic changes, disc calcification in a non-protrusive region, and herniation type (noncontained herniation) as significant features for predicting rLDH | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a deep learning | |
| 650 | 4 | |a machine learning | |
| 650 | 4 | |a percutaneous endoscopic lumbar discectomy | |
| 650 | 4 | |a recurrent lumbar disc herniation | |
| 700 | 1 | |a Liu, Lei |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Po |e verfasserin |4 aut | |
| 700 | 1 | |a Xie, ZhiYang |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, PeiYang |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Hui |e verfasserin |4 aut | |
| 700 | 1 | |a Shen, MeiJi |e verfasserin |4 aut | |
| 700 | 1 | |a Deng, LiTing |e verfasserin |4 aut | |
| 700 | 1 | |a Tao, YuAo |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Xi |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, JiaoDong |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, YunTao |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, XiaoTao |e verfasserin |4 aut | |
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