Radiomics nomogram for differentiating between benign and malignant soft-tissue masses of the extremities

Preoperative differentiation between malignant and benign tumors is important for treatment decisions.

To investigate/validate a radiomics nomogram for preoperative differentiation between malignant and benign masses.

Retrospective.

Imaging data of 91 patients.

T₁ -weighted images (570 msec repetition time [TR]; 17.9 msec echo time [TE], 200-400 mm field of view [FOV], 208-512 × 208-512 matrix), fat-suppressed fast-spin-echo (FSE) T₂ -weighted images (T₂ WIs) (4331 msec TR; 87.9 msec TE, 200-400 mm FOV, 312 × 312 matrix), slice thickness 4 mm, and slice spacing 1 mm.

Fat-suppressed FSE T₂ WIs were selected for extraction of features. Radiomics features were extracted from fat-suppressed T₂ WIs. A radiomics signature was generated from the training dataset using least absolute shrinkage and selection operator algorithms. Independent risk factors were identified by multivariate logistic regression analysis and a radiomics nomogram was constructed. Nomogram capability was evaluated in the training dataset and validated in the validation dataset. Performance of the nomogram, radiomics signature, and clinical model were compared.

1) Independent t-test or Mann-Whitney U-test: for continuous variables. Fisher's exact test or χ² test: comparing categorical variables between two groups. Univariate analysis: evaluating associations between clinical/morphological characteristics and malignancy. 2) Least absolute shrinkage and selection operator (LASSO)-logistic regression model: selection of malignancy features. 3) Significant clinical/morphological characteristics and radiomics signature were input variables for multiple logistic regression analysis. Area under the curve (AUC): evaluation of ability of the nomogram to identify malignancy. Hosmer-Lemeshow test and decision curve: evaluation and validation of nomogram results.

The radiomics nomogram was able to differentiate malignancy from benignity in the training and validation datasets with an AUC of 0.94. The nomogram outperformed both the radiomics signature and clinical model alone.

This radiomics nomogram is a noninvasive, low-cost preoperative prediction method combining the radiomics signature and clinical model.

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:155-163.