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Machine Learning Assisting the Prediction of Clinical Outcomes following Nucleoplasty for Lumbar Degenerative Disc Disease

Background: Lumbar degenerative disc disease (LDDD) is a leading cause of chronic lower back pain; however, a lack of clear diagnostic criteria and solid LDDD interventional therapies have made predicting the benefits of therapeutic strategies challenging. Our goal is to develop machine learning (ML...

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Autores principales: Chiu, Po-Fan, Chang, Robert Chen-Hao, Lai, Yung-Chi, Wu, Kuo-Chen, Wang, Kuan-Pin, Chiu, You-Pen, Ji, Hui-Ru, Kao, Chia-Hung, Chiu, Cheng-Di
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10252482/
https://www.ncbi.nlm.nih.gov/pubmed/37296715
http://dx.doi.org/10.3390/diagnostics13111863
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author Chiu, Po-Fan
Chang, Robert Chen-Hao
Lai, Yung-Chi
Wu, Kuo-Chen
Wang, Kuan-Pin
Chiu, You-Pen
Ji, Hui-Ru
Kao, Chia-Hung
Chiu, Cheng-Di
author_facet Chiu, Po-Fan
Chang, Robert Chen-Hao
Lai, Yung-Chi
Wu, Kuo-Chen
Wang, Kuan-Pin
Chiu, You-Pen
Ji, Hui-Ru
Kao, Chia-Hung
Chiu, Cheng-Di
author_sort Chiu, Po-Fan
collection PubMed
description Background: Lumbar degenerative disc disease (LDDD) is a leading cause of chronic lower back pain; however, a lack of clear diagnostic criteria and solid LDDD interventional therapies have made predicting the benefits of therapeutic strategies challenging. Our goal is to develop machine learning (ML)–based radiomic models based on pre-treatment imaging for predicting the outcomes of lumbar nucleoplasty (LNP), which is one of the interventional therapies for LDDD. Methods: The input data included general patient characteristics, perioperative medical and surgical details, and pre-operative magnetic resonance imaging (MRI) results from 181 LDDD patients receiving lumbar nucleoplasty. Post-treatment pain improvements were categorized as clinically significant (defined as a ≥80% decrease in the visual analog scale) or non-significant. To develop the ML models, T2-weighted MRI images were subjected to radiomic feature extraction, which was combined with physiological clinical parameters. After data processing, we developed five ML models: support vector machine, light gradient boosting machine, extreme gradient boosting, extreme gradient boosting random forest, and improved random forest. Model performance was measured by evaluating indicators, such as the confusion matrix, accuracy, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUC), which were acquired using an 8:2 allocation of training to testing sequences. Results: Among the five ML models, the improved random forest algorithm had the best performance, with an accuracy of 0.76, a sensitivity of 0.69, a specificity of 0.83, an F1 score of 0.73, and an AUC of 0.77. The most influential clinical features included in the ML models were pre-operative VAS and age. In contrast, the most influential radiomic features had the correlation coefficient and gray-scale co-occurrence matrix. Conclusions: We developed an ML-based model for predicting pain improvement after LNP for patients with LDDD. We hope this tool will provide both doctors and patients with better information for therapeutic planning and decision-making.
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spelling pubmed-102524822023-06-10 Machine Learning Assisting the Prediction of Clinical Outcomes following Nucleoplasty for Lumbar Degenerative Disc Disease Chiu, Po-Fan Chang, Robert Chen-Hao Lai, Yung-Chi Wu, Kuo-Chen Wang, Kuan-Pin Chiu, You-Pen Ji, Hui-Ru Kao, Chia-Hung Chiu, Cheng-Di Diagnostics (Basel) Article Background: Lumbar degenerative disc disease (LDDD) is a leading cause of chronic lower back pain; however, a lack of clear diagnostic criteria and solid LDDD interventional therapies have made predicting the benefits of therapeutic strategies challenging. Our goal is to develop machine learning (ML)–based radiomic models based on pre-treatment imaging for predicting the outcomes of lumbar nucleoplasty (LNP), which is one of the interventional therapies for LDDD. Methods: The input data included general patient characteristics, perioperative medical and surgical details, and pre-operative magnetic resonance imaging (MRI) results from 181 LDDD patients receiving lumbar nucleoplasty. Post-treatment pain improvements were categorized as clinically significant (defined as a ≥80% decrease in the visual analog scale) or non-significant. To develop the ML models, T2-weighted MRI images were subjected to radiomic feature extraction, which was combined with physiological clinical parameters. After data processing, we developed five ML models: support vector machine, light gradient boosting machine, extreme gradient boosting, extreme gradient boosting random forest, and improved random forest. Model performance was measured by evaluating indicators, such as the confusion matrix, accuracy, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUC), which were acquired using an 8:2 allocation of training to testing sequences. Results: Among the five ML models, the improved random forest algorithm had the best performance, with an accuracy of 0.76, a sensitivity of 0.69, a specificity of 0.83, an F1 score of 0.73, and an AUC of 0.77. The most influential clinical features included in the ML models were pre-operative VAS and age. In contrast, the most influential radiomic features had the correlation coefficient and gray-scale co-occurrence matrix. Conclusions: We developed an ML-based model for predicting pain improvement after LNP for patients with LDDD. We hope this tool will provide both doctors and patients with better information for therapeutic planning and decision-making. MDPI 2023-05-26 /pmc/articles/PMC10252482/ /pubmed/37296715 http://dx.doi.org/10.3390/diagnostics13111863 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chiu, Po-Fan
Chang, Robert Chen-Hao
Lai, Yung-Chi
Wu, Kuo-Chen
Wang, Kuan-Pin
Chiu, You-Pen
Ji, Hui-Ru
Kao, Chia-Hung
Chiu, Cheng-Di
Machine Learning Assisting the Prediction of Clinical Outcomes following Nucleoplasty for Lumbar Degenerative Disc Disease
title Machine Learning Assisting the Prediction of Clinical Outcomes following Nucleoplasty for Lumbar Degenerative Disc Disease
title_full Machine Learning Assisting the Prediction of Clinical Outcomes following Nucleoplasty for Lumbar Degenerative Disc Disease
title_fullStr Machine Learning Assisting the Prediction of Clinical Outcomes following Nucleoplasty for Lumbar Degenerative Disc Disease
title_full_unstemmed Machine Learning Assisting the Prediction of Clinical Outcomes following Nucleoplasty for Lumbar Degenerative Disc Disease
title_short Machine Learning Assisting the Prediction of Clinical Outcomes following Nucleoplasty for Lumbar Degenerative Disc Disease
title_sort machine learning assisting the prediction of clinical outcomes following nucleoplasty for lumbar degenerative disc disease
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10252482/
https://www.ncbi.nlm.nih.gov/pubmed/37296715
http://dx.doi.org/10.3390/diagnostics13111863
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