Multi-institutional validation of a radiomics signature for identification of postoperative progression of soft tissue sarcoma

Abstract

Background: To develop a magnetic resonance imaging (MRI)-based radiomics signature for evaluating the risk of soft tissue sarcoma (STS) disease progression.

Methods: We retrospectively enrolled 335 patients with STS (training, validation, and The Cancer Imaging Archive sets, n = 168, n = 123, and n = 44, respectively) who underwent surgical resection. Regions of interest were manually delineated using two MRI sequences. Among 12 machine learning-predicted signatures, the best signature was selected, and its prediction score was inputted into Cox regression analysis to build the radiomics signature. A nomogram was created by combining the radiomics signature with a clinical model constructed using MRI and clinical features. Progression-free survival was analyzed in all patients. We assessed performance and clinical utility of the models with reference to the time-dependent receiver operating characteristic curve, area under the curve, concordance index, integrated Brier score, decision curve analysis.

Results: For the combined features subset, the minimum redundancy maximum relevance-least absolute shrinkage and selection operator regression algorithm + decision tree classifier had the best prediction performance. The radiomics signature based on the optimal machine learning-predicted signature, and built using Cox regression analysis, had greater prognostic capability and lower error than the nomogram and clinical model (concordance index, 0.758 and 0.812; area under the curve, 0.724 and 0.757; integrated Brier score, 0.080 and 0.143, in the validation and The Cancer Imaging Archive sets, respectively). The optimal cutoff was - 0.03 and cumulative risk rates were calculated.

Data conclusion: To assess the risk of STS progression, the radiomics signature may have better prognostic power than a nomogram/clinical model.

Keywords: Disease Progression; Progression-free survival; Radiomics; Soft tissue sarcoma.

Fig. 1

The study flow chart

Fig. 2

(a) MRI feature selection using the least absolute shrinkage and selection operator regression algorithm. (b) The seven selected MRI features and their coefficients

Fig. 3

Time-dependent receiver operating characteristic curves for each model and the different cohorts

Fig. 4

(a–c) Calibration curves of the different models using the training, validation, and TCIA sets. (d–f) Prediction error curves of the different models using the training, validation, and TCIA sets. (g) Results of the decision curve analysis of all cohorts

Fig. 5

Cumulative progression rates predicted by the radiomics signature according to risk group