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Article

Machine Learning Algorithm: Texture Analysis in CNO and Application in Distinguishing CNO and Bone Marrow Growth-Related Changes on Whole-Body MRI

1
Imaging Department, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy
2
Medical Physics Department, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Diagnostics 2024, 14(1), 61; https://doi.org/10.3390/diagnostics14010061
Submission received: 24 October 2023 / Revised: 17 December 2023 / Accepted: 22 December 2023 / Published: 27 December 2023
(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)

Abstract

Objective: The purpose of this study is to analyze the texture characteristics of chronic non-bacterial osteomyelitis (CNO) bone lesions, identified as areas of altered signal intensity on short tau inversion recovery (STIR) sequences, and to distinguish them from bone marrow growth-related changes through Machine Learning (ML) and Deep Learning (DL) analysis. Materials and methods: We included a group of 66 patients with confirmed diagnosis of CNO and a group of 28 patients with suspected extra-skeletal systemic disease. All examinations were performed on a 1.5 T MRI scanner. Using the opensource 3D Slicer software version 4.10.2, the ROIs on CNO lesions and on the red bone marrow were sampled. Texture analysis (TA) was carried out using Pyradiomics. We applied an optimization search grid algorithm on nine classic ML classifiers and a Deep Learning (DL) Neural Network (NN). The model’s performance was evaluated using Accuracy (ACC), AUC-ROC curves, F1-score, Positive Predictive Value (PPV), Mean Absolute Error (MAE) and Root-Mean-Square Error (RMSE). Furthermore, we used Shapley additive explanations to gain insight into the behavior of the prediction model. Results: Most predictive characteristics were selected by Boruta algorithm for each combination of ROI sequences for the characterization and classification of the two types of signal hyperintensity. The overall best classification result was obtained by the NN with ACC = 0.91, AUC = 0.93 with 95% CI 0.91–0.94, F1-score = 0.94 and PPV = 93.8%. Between classic ML methods, ensemble learners showed high model performance; specifically, the best-performing classifier was the Stack (ST) with ACC = 0.85, AUC = 0.81 with 95% CI 0.8–0.84, F1-score = 0.9, PPV = 90%. Conclusions: Our results show the potential of ML methods in discerning edema-like lesions, in particular by distinguishing CNO lesions from hematopoietic bone marrow changes in a pediatric population. The Neural Network showed the overall best results, while a Stacking classifier, based on Gradient Boosting and Random Forest as principal estimators and Logistic Regressor as final estimator, achieved the best results between the other ML methods.
Keywords: whole-body magnetic resonance imaging; chronic non-bacterial osteomyelitis; bone marrow; machine learning; texture analysis; children whole-body magnetic resonance imaging; chronic non-bacterial osteomyelitis; bone marrow; machine learning; texture analysis; children

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MDPI and ACS Style

Forestieri, M.; Napolitano, A.; Tomà, P.; Bascetta, S.; Cirillo, M.; Tagliente, E.; Fracassi, D.; D’Angelo, P.; Casazza, I. Machine Learning Algorithm: Texture Analysis in CNO and Application in Distinguishing CNO and Bone Marrow Growth-Related Changes on Whole-Body MRI. Diagnostics 2024, 14, 61. https://doi.org/10.3390/diagnostics14010061

AMA Style

Forestieri M, Napolitano A, Tomà P, Bascetta S, Cirillo M, Tagliente E, Fracassi D, D’Angelo P, Casazza I. Machine Learning Algorithm: Texture Analysis in CNO and Application in Distinguishing CNO and Bone Marrow Growth-Related Changes on Whole-Body MRI. Diagnostics. 2024; 14(1):61. https://doi.org/10.3390/diagnostics14010061

Chicago/Turabian Style

Forestieri, Marta, Antonio Napolitano, Paolo Tomà, Stefano Bascetta, Marco Cirillo, Emanuela Tagliente, Donatella Fracassi, Paola D’Angelo, and Ines Casazza. 2024. "Machine Learning Algorithm: Texture Analysis in CNO and Application in Distinguishing CNO and Bone Marrow Growth-Related Changes on Whole-Body MRI" Diagnostics 14, no. 1: 61. https://doi.org/10.3390/diagnostics14010061

APA Style

Forestieri, M., Napolitano, A., Tomà, P., Bascetta, S., Cirillo, M., Tagliente, E., Fracassi, D., D’Angelo, P., & Casazza, I. (2024). Machine Learning Algorithm: Texture Analysis in CNO and Application in Distinguishing CNO and Bone Marrow Growth-Related Changes on Whole-Body MRI. Diagnostics, 14(1), 61. https://doi.org/10.3390/diagnostics14010061

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