Texture analysis to differentiate anterior cruciate ligament in patients after surgery with platelet-rich plasma
Abstract Background Platelet-rich plasma (PRP) has been used to favor anterior cruciate ligament (ACL) healing after reconstruction surgeries. However, clinical data are still inconclusive and subjective about PRP. Thus, we propose a quantitative method to demonstrate that PRP produced morphological...
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doaj-5d6506556d3045c29628c942fdfb5e202021-05-02T11:28:37ZengBMCJournal of Orthopaedic Surgery and Research1749-799X2021-04-011611810.1186/s13018-021-02437-yTexture analysis to differentiate anterior cruciate ligament in patients after surgery with platelet-rich plasmaAllan Felipe Fattori Alves0José Ricardo de Arruda Miranda1Sérgio Augusto Santana de Souza2Ricardo Violante Pereira3Paulo Roberto de Almeida Silvares4Seizo Yamashita5Elenice Deffune6Diana Rodrigues de Pina7Medical School, Sao Paulo State University Julio de Mesquita FilhoInstitute of Bioscience, Sao Paulo State University Julio de Mesquita FilhoInstitute of Bioscience, Sao Paulo State University Julio de Mesquita FilhoMedical School, Sao Paulo State University Julio de Mesquita FilhoMedical School, Sao Paulo State University Julio de Mesquita FilhoMedical School, Sao Paulo State University Julio de Mesquita FilhoMedical School, Sao Paulo State University Julio de Mesquita FilhoMedical School, Sao Paulo State University Julio de Mesquita FilhoAbstract Background Platelet-rich plasma (PRP) has been used to favor anterior cruciate ligament (ACL) healing after reconstruction surgeries. However, clinical data are still inconclusive and subjective about PRP. Thus, we propose a quantitative method to demonstrate that PRP produced morphological structure changes. Methods Thirty-four patients undergoing ACL reconstruction surgery were evaluated and divided into control group (sixteen patients) without PRP application and experiment group (eighteen patients) with intraoperative application of PRP. Magnetic resonance imaging (MRI) scans were performed 3 months after surgery. We used Matlab® and machine learning (ML) in Orange Canvas® to texture analysis (TA) features extraction. Experienced radiologists delimited the regions of interest (RoIs) in the T2-weighted images. Sixty-two texture parameters were extracted, including gray-level co-occurrence matrix and gray level run length. We used the algorithms logistic regression (LR), naive Bayes (NB), and stochastic gradient descent (SGD). Results The accuracy of the classification with NB, LR, and SGD was 83.3%, 75%, 75%, respectively. For the area under the curve, NB, LR, and SGD presented values of 91.7%, 94.4%, 75%, respectively. In clinical evaluations, the groups show similar responses in terms of improvement in pain and increase in the IKDC index (International Knee Documentation Committee) and Lysholm score indices differing only in the assessment of flexion, which presents a significant difference for the group treated with PRP. Conclusions Here, we demonstrated quantitatively that patients who received PRP presented texture changes when compared to the control group. Thus, our findings suggest that PRP interferes with morphological parameters of the ACL. Trial registration Protocol no. CAAE 56164316.6.0000.5411.https://doi.org/10.1186/s13018-021-02437-yKnee jointClassificationMagnetic resonance imagingTexture analysisMachine learningPlatelet-rich plasma |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Allan Felipe Fattori Alves José Ricardo de Arruda Miranda Sérgio Augusto Santana de Souza Ricardo Violante Pereira Paulo Roberto de Almeida Silvares Seizo Yamashita Elenice Deffune Diana Rodrigues de Pina |
spellingShingle |
Allan Felipe Fattori Alves José Ricardo de Arruda Miranda Sérgio Augusto Santana de Souza Ricardo Violante Pereira Paulo Roberto de Almeida Silvares Seizo Yamashita Elenice Deffune Diana Rodrigues de Pina Texture analysis to differentiate anterior cruciate ligament in patients after surgery with platelet-rich plasma Journal of Orthopaedic Surgery and Research Knee joint Classification Magnetic resonance imaging Texture analysis Machine learning Platelet-rich plasma |
author_facet |
Allan Felipe Fattori Alves José Ricardo de Arruda Miranda Sérgio Augusto Santana de Souza Ricardo Violante Pereira Paulo Roberto de Almeida Silvares Seizo Yamashita Elenice Deffune Diana Rodrigues de Pina |
author_sort |
Allan Felipe Fattori Alves |
title |
Texture analysis to differentiate anterior cruciate ligament in patients after surgery with platelet-rich plasma |
title_short |
Texture analysis to differentiate anterior cruciate ligament in patients after surgery with platelet-rich plasma |
title_full |
Texture analysis to differentiate anterior cruciate ligament in patients after surgery with platelet-rich plasma |
title_fullStr |
Texture analysis to differentiate anterior cruciate ligament in patients after surgery with platelet-rich plasma |
title_full_unstemmed |
Texture analysis to differentiate anterior cruciate ligament in patients after surgery with platelet-rich plasma |
title_sort |
texture analysis to differentiate anterior cruciate ligament in patients after surgery with platelet-rich plasma |
publisher |
BMC |
series |
Journal of Orthopaedic Surgery and Research |
issn |
1749-799X |
publishDate |
2021-04-01 |
description |
Abstract Background Platelet-rich plasma (PRP) has been used to favor anterior cruciate ligament (ACL) healing after reconstruction surgeries. However, clinical data are still inconclusive and subjective about PRP. Thus, we propose a quantitative method to demonstrate that PRP produced morphological structure changes. Methods Thirty-four patients undergoing ACL reconstruction surgery were evaluated and divided into control group (sixteen patients) without PRP application and experiment group (eighteen patients) with intraoperative application of PRP. Magnetic resonance imaging (MRI) scans were performed 3 months after surgery. We used Matlab® and machine learning (ML) in Orange Canvas® to texture analysis (TA) features extraction. Experienced radiologists delimited the regions of interest (RoIs) in the T2-weighted images. Sixty-two texture parameters were extracted, including gray-level co-occurrence matrix and gray level run length. We used the algorithms logistic regression (LR), naive Bayes (NB), and stochastic gradient descent (SGD). Results The accuracy of the classification with NB, LR, and SGD was 83.3%, 75%, 75%, respectively. For the area under the curve, NB, LR, and SGD presented values of 91.7%, 94.4%, 75%, respectively. In clinical evaluations, the groups show similar responses in terms of improvement in pain and increase in the IKDC index (International Knee Documentation Committee) and Lysholm score indices differing only in the assessment of flexion, which presents a significant difference for the group treated with PRP. Conclusions Here, we demonstrated quantitatively that patients who received PRP presented texture changes when compared to the control group. Thus, our findings suggest that PRP interferes with morphological parameters of the ACL. Trial registration Protocol no. CAAE 56164316.6.0000.5411. |
topic |
Knee joint Classification Magnetic resonance imaging Texture analysis Machine learning Platelet-rich plasma |
url |
https://doi.org/10.1186/s13018-021-02437-y |
work_keys_str_mv |
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