A Magnetic Resonance Imaging Radiomics Signature to Distinguish Benign From Malignant Orbital Lesions

Abstract

Objectives Distinguishing benign from malignant orbital lesions remains challenging both clinically and with imaging, leading to risky biopsies. The objective was to differentiate benign from malignant orbital lesions using radiomics on 3 T magnetic resonance imaging (MRI) examinations. Materials and Methods This institutional review board–approved prospective single-center study enrolled consecutive patients presenting with an orbital lesion undergoing a 3 T MRI prior to surgery from December 2015 to July 2019. Radiomics features were extracted from 6 MRI sequences (T1-weighted images [WIs], DIXON-T2-WI, diffusion-WI, postcontrast DIXON-T1-WI) using the Pyradiomics software. Features were selected based on their intraobserver and interobserver reproducibility, nonredundancy, and with a sequential step forward feature selection method. Selected features were used to train and optimize a Random Forest algorithm on the training set (75%) with 5-fold cross-validation. Performance metrics were computed on a held-out test set (25%) with bootstrap 95% confidence intervals (95% CIs). Five residents, 4 general radiologists, and 3 expert neuroradiologists were evaluated on their ability to visually distinguish benign from malignant lesions on the test set. Performance comparisons between reader groups and the model were performed using McNemar test. The impact of clinical and categorizable imaging data on algorithm performance was also assessed. Results A total of 200 patients (116 [58%] women and 84 [42%] men; mean age, 53.0 ± 17.9 years) with 126 of 200 (63%) benign and 74 of 200 (37%) malignant orbital lesions were included in the study. A total of 606 radiomics features were extracted. The best performing model on the training set was composed of 8 features including apparent diffusion coefficient mean value, maximum diameter on T1-WIs, and texture features. Area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity on the test set were respectively 0.869 (95% CI, 0.834–0.898), 0.840 (95% CI, 0.806–0.874), 0.684 (95% CI, 0.615–0.751), and 0.935 (95% CI, 0.905–0.961). The radiomics model outperformed all reader groups, including expert neuroradiologists (P < 0.01). Adding clinical and categorizable imaging data did not significantly impact the algorithm performance (P = 0.49). Conclusions An MRI radiomics signature is helpful in differentiating benign from malignant orbital lesions and may outperform expert radiologists.

Evaluation of lateral pterygoid muscle in patients with temporomandibular joint anterior disk displacement using T1-weighted Dixon sequence: a retrospective study

“…Third, other texture analysis [ 21 ] such as histogram analysis, run-length matrix analysis are also needed to be involved in the future study. Forth, the two-dimensional delineation of LPM in this study might miss the heterogeneity of the entire muscle compared with the three-dimensional delineation [ 24 ]. Future study needs to design three-dimensional sequence to comprehensively analyze the heterogeneity of LPM.…”

“…A considerable number of studies have confirmed that texture analysis can be used to quantitatively discriminate normal muscle from myopathic muscle [ 20 – 23 ]. What’s more, recent study showed that texture analysis of in-phase image of Dixon sequence might be used to identify the tissue with more heterogeneity [ 24 ]. Therefore, it can be speculated that the texture features extracted from in-phase image are also related to fat fraction of LPM considering the heterogeneity of muscle might be affected by fatty infiltration.…”

A radiomics-based machine learning pipeline to distinguish between metastatic and healthy bone using lesion-center-based geometric regions of interest

“…Depending on the endpoint of interest, various ML classifiers may be used in a radiomics pipeline. Support vector machine (SVM), Bayesian network (BN), multivariate logistic regression (MLR), k-nearest neighbor (kNN), decision trees (DT), random forests (RF), neural network (NNet), and convolutional neural networks (CNN) are among the ML classifiers that are most commonly used in radiomics-based ML pipelines [8][9][10][11][12][13][14][15][16][17][18][19][20] . The feasibility of using radiomics-based ML pipelines to distinguish between benign and malignant bone lesions has been reported in previous studies 1-4, 6, 7 .…”

“…In recent years, radiomics-based machine learning (ML) classifiers have shown great potential for use in the early detection of bone metastases (BM) and in assessing response of BM to radiotherapy (RT) [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] . However, in order to be clinically acceptable, radiomics models must be trained on large data sets of real-world images.…”

T2‐Weighted MR Imaging‐Derived Radiomics for Pretreatment Determination of Therapeutic Response to Glucocorticoid in Patients With Thyroid‐Associated Ophthalmopathy: Comparison With Semiquantitative Evaluation

“…Radiomics enables noninvasive profiling of tissue heterogeneity by extracting quantitative features from radiological images 15,16 . In the field of orbital diseases, several studies have reported its use in improving screening, diagnostic, and predictive performances 17–19 . Duron et al found that MRI‐derived radiomics could assist in differentiating benign from malignant orbital lesions and may outperform expert radiologists 17 .…”