Tumor-to-bone distance and radiomic features on MRI distinguish intramuscular lipomas from well-differentiated liposarcomas

Abstract

Background To develop a machine learning model based on tumor-to-bone distance and radiomic features derived from preoperative MRI images to distinguish intramuscular (IM) lipomas and atypical lipomatous tumors/well-differentiated liposarcomas (ALTs/WDLSs) and compared with radiologists. Methods The study included patients with IM lipomas and ALTs/WDLSs diagnosed between 2010 and 2022, and with MRI scans (sequence/field strength: T1-weighted (T1W) imaging at 1.5 or 3.0 Tesla MRI). Manual segmentation of tumors based on the three-dimensional T1W images was performed by two observers to appraise the intra- and interobserver variability. After radiomic features and tumor-to-bone distance were extracted, it was used to train a machine learning model to distinguish IM lipomas and ALTs/WDLSs. Both feature selection and classification steps were performed using Least Absolute Shrinkage and Selection Operator logistic regression. The performance of the classification model was assessed using a tenfold cross-validation strategy and subsequently evaluated using the receiver operating characteristic curve (ROC) analysis. The classification agreement of two experienced musculoskeletal (MSK) radiologists was assessed using the kappa statistics. The diagnosis accuracy of each radiologist was evaluated using the final pathological results as the gold standard. Additionally, we compared the performance of the model and two radiologists in terms of the area under the receiver operator characteristic curves (AUCs) using the Delong’s test. Results There were 68 tumors (38 IM lipomas and 30 ALTs/WDLSs). The AUC of the machine learning model was 0.88 [95% CI 0.72–1] (sensitivity, 91.6%; specificity, 85.7%; and accuracy, 89.0%). For Radiologist 1, the AUC was 0.94 [95% CI 0.87–1] (sensitivity, 97.4%; specificity, 90.9%; and accuracy, 95.0%), and as to Radiologist 2, the AUC was 0.91 [95% CI 0.83–0.99] (sensitivity, 100%; specificity, 81.8%; and accuracy, 93.3%). The classification agreement of the radiologists was 0.89 of kappa value (95% CI 0.76–1). Although the AUC of the model was lower than of two experienced MSK radiologists, there was no statistically significant difference between the model and two radiologists (all P > 0.05). Conclusions The novel machine learning model based on tumor-to-bone distance and radiomic features is a noninvasive procedure that has the potential for distinguishing IM lipomas from ALTs/WDLSs. The predictive features that suggested malignancy were size, shape, depth, texture, histogram, and tumor-to-bone distance.

CT and MRI radiomics of bone and soft-tissue sarcomas: an updated systematic review of reproducibility and validation strategies

“…ICC threshold ranged between 0.7 [ 54 ] and 0.9 [ 20 , 46 ] for reproducible features. Additionally, the following statistical methods were used less commonly: Bland–Altman method [ 54 ], Pearson’s correlation coefficient [ 52 ], and Spearman’s rank-order coefficient [ 52 ].…”

“…At least one machine learning validation technique was used in 34 (62%) of the 55 papers. K-fold cross-validation was used in most of the studies [ 18 , 20 , 22 , 24 , 27 , 31 , 37 – 39 , 44 , 47 , 49 , 52 , 54 , 57 , 58 , 60 – 68 ]. The following machine learning validation techniques were used less commonly: bootstrapping [ 34 , 46 ], leave-one-out cross-validation [ 17 , 28 ], and nested cross-validation [ 43 , 55 , 56 , 69 ].…”

“…A clinical validation of the radiomics-based prediction model was reported in 38 (69%) of the 55 studies. In 22 (40%) studies, it was performed on a separate set of data from the primary institution, namely the internal test dataset, which was chosen randomly [ 19 , 20 , 24 , 28 , 29 , 31 , 32 , 37 , 41 , 42 , 45 , 47 , 52 , 53 , 59 , 65 , 66 , 68 ], based on temporal criteria [ 61 , 69 , 70 ] or different acquisition scanners [ 62 ]. Of note, in a multi-center study, patients were split into training and test cohorts randomly rather than following geographical criteria [ 68 ].…”

Relaxed Adaptive Lasso for Classification on High-Dimensional Sparse Data with Multicollinearity

“…Particularly needed are predictive modeling techniques that can provide accurate results to help decision-making. Logistic regression is one of the techniques that is widely employed in data analysis and machine learning communities [1][2][3][4]. This predictive modeling technique describes the relationships between independent and outcome variables and predicts the outcome variables' future values [5,6].…”