Commentary: Radiomics in oncology: A 10-year bibliometric analysis

Fan, Guoxin; Qin, Jiaqi; Liu, Huaqing; Liao, Xiang

doi:10.3389/fonc.2022.891056

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…Radiomics is one of the most popular techniques for extracting high-throughput image features of ROIs from multi-dimensional data [ 28 ]. When combined with ML techniques, radiomic data can be used to achieve the accurate, quantitative, and interpretative evaluation of ROIs, which is expected to assist in the diagnosis, classification, and decision making of diseases [ 30 ].…”

Section: Discussionmentioning

confidence: 99%

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Machine Learning Predicts Decompression Levels for Lumbar Spinal Stenosis Using Canal Radiomic Features from Computed Tomography Myelography

Fan,

Wang,

et al. 2023

Diagnostics

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Background: The accurate preoperative identification of decompression levels is crucial for the success of surgery in patients with multi-level lumbar spinal stenosis (LSS). The objective of this study was to develop machine learning (ML) classifiers that can predict decompression levels using computed tomography myelography (CTM) data from LSS patients. Methods: A total of 1095 lumbar levels from 219 patients were included in this study. The bony spinal canal in CTM images was manually delineated, and radiomic features were extracted. The extracted data were randomly divided into training and testing datasets (8:2). Six feature selection methods combined with 12 ML algorithms were employed, resulting in a total of 72 ML classifiers. The main evaluation indicator for all classifiers was the area under the curve of the receiver operating characteristic (ROC-AUC), with the precision–recall AUC (PR-AUC) serving as the secondary indicator. The prediction outcome of ML classifiers was decompression level or not. Results: The embedding linear support vector (embeddingLSVC) was the optimal feature selection method. The feature importance analysis revealed the top 5 important features of the 15 radiomic predictors, which included 2 texture features, 2 first-order intensity features, and 1 shape feature. Except for shape features, these features might be eye-discernible but hardly quantified. The top two ML classifiers were embeddingLSVC combined with support vector machine (EmbeddingLSVC_SVM) and embeddingLSVC combined with gradient boosting (EmbeddingLSVC_GradientBoost). These classifiers achieved ROC-AUCs over 0.90 and PR-AUCs over 0.80 in independent testing among the 72 classifiers. Further comparisons indicated that EmbeddingLSVC_SVM appeared to be the optimal classifier, demonstrating superior discrimination ability, slight advantages in the Brier scores on the calibration curve, and Net benefits on the Decision Curve Analysis. Conclusions: ML successfully extracted valuable and interpretable radiomic features from the spinal canal using CTM images, and accurately predicted decompression levels for LSS patients. The EmbeddingLSVC_SVM classifier has the potential to assist surgical decision making in clinical practice, as it showed high discrimination, advantageous calibration, and competitive utility in selecting decompression levels in LSS patients using canal radiomic features from CTM.

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Section: Discussionmentioning

confidence: 99%

“…Radiomics is one of the most popular techniques for extracting high-throughput image features in quantitative analysis studies [ 28 ]. Radiomics is a process that converts digital medical images (MR, CT, etc.)…”

Section: Introductionmentioning

confidence: 99%