Version 1
: Received: 25 June 2024 / Approved: 25 June 2024 / Online: 25 June 2024 (09:04:48 CEST)
How to cite:
Liao, L.-J.; Cheng, P.-C.; Chan, F.-T. Machine Learning on Ultrasound Texture Analysis Data for Characterizing of Salivary Glandular Tumors: A Feasibility Study. Preprints2024, 2024061750. https://doi.org/10.20944/preprints202406.1750.v1
Liao, L.-J.; Cheng, P.-C.; Chan, F.-T. Machine Learning on Ultrasound Texture Analysis Data for Characterizing of Salivary Glandular Tumors: A Feasibility Study. Preprints 2024, 2024061750. https://doi.org/10.20944/preprints202406.1750.v1
Liao, L.-J.; Cheng, P.-C.; Chan, F.-T. Machine Learning on Ultrasound Texture Analysis Data for Characterizing of Salivary Glandular Tumors: A Feasibility Study. Preprints2024, 2024061750. https://doi.org/10.20944/preprints202406.1750.v1
APA Style
Liao, L. J., Cheng, P. C., & Chan, F. T. (2024). Machine Learning on Ultrasound Texture Analysis Data for Characterizing of Salivary Glandular Tumors: A Feasibility Study. Preprints. https://doi.org/10.20944/preprints202406.1750.v1
Chicago/Turabian Style
Liao, L., Ping-Chia Cheng and Feng-Tsan Chan. 2024 "Machine Learning on Ultrasound Texture Analysis Data for Characterizing of Salivary Glandular Tumors: A Feasibility Study" Preprints. https://doi.org/10.20944/preprints202406.1750.v1
Abstract
Background: Objective quantitative texture characteristics may be helpful in salivary glandular tumor differential diagnosis. This study uses machine learning (ML) to explore and validate the performance of ultrasound (US) texture features in diagnosing salivary glandular tumors. Material and methods: 122 patients with salivary glandular tumors, including 71 benign and 51 malignant tumors, are enrolled. A representative brightness mode US pictures are selected for further Gray Level Co-occurrence Matrix (GLCM) texture analysis. We use t-test to test the significance and use receiver operating characteristic curve method to fund optimal cut-point for these significant features. After splitting 80% data into training set and 20% data into testing set, we use five machine learning models k-nearest Neighbors (kNN), Naïve Bayes, Logistic regression, Artificial Neural Networks (ANN) and supportive vector machine (SVM) to explore and validate the performance of US GLCM texture features in diagnosing salivary glandular tumors. Results: This study includes 49 female and 73 male patients, with a mean age of 53 years old, ranging from 21 to 93. We find that six GLCM texture features (contrast, inverse difference movement, entropy, dissimilarity, inverse difference and difference entropy) are significantly different between benign from malignant tumors (p<0.05). On ML, the overall accuracy rates are 74.3% (95%CI: 59.8-88.8%), 94.3% (86.6-100%), 72% (54-89%), 84% (69.5-97.3%) and 73.5% (58.7-88.4%) for kNN, Naïve Bayes, Logistic regression, one node ANN and SVM, respectively. Conclusion: US texture analysis with ML has potential as an objective and valuable tool for assessment of salivary gland tumors.
Keywords
ultrasound; texture analysis; machine learning
Subject
Medicine and Pharmacology, Otolaryngology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
