Abstract
The objective of this study was to develop, validate, and compare nomograms for malignancy prediction in soft tissue tumors (STTs) using conventional and diffusion-weighted magnetic resonance imaging (MRI) measurements. Between May 2011 and December 2016, 239 MRI examinations from 236 patients with pathologically proven STTs were included retrospectively and assigned randomly to training (n = 100) and validation (n = 139) cohorts. MRI of each lesion was reviewed to assess conventional and diffusion-weighted imaging (DWI) measurements. Multivariate nomograms based on logistic regression analyses were built using conventional measurements with and without DWI measurements. Predictive accuracy was measured using the concordance index (C-index) and calibration plots. Statistical differences between the C-indexes of the two models were analyzed. Models were validated by leave-one-out cross-validation and by using a validation cohort. The mean lesion size, presence of infiltration, edema, and the absence of the split fat sign were significant and independent predictors of malignancy and included in the conventional model. In addition to these measurements, the mean and minimum apparent diffusion coefficient values were included in the DWI model. The DWI model exhibited significantly higher diagnostic performance only in the validation cohort (training cohort, 0.899 vs. 0.886, P = 0.284; validation cohort, 0.791 vs. 0.757, P = 0.020). Calibration plots showed fair agreements between the nomogram predictions and actual observations in both cohorts. In conclusion, nomograms using MRI features as variables can be utilized to predict the malignancy probability in patients with STTs. There was no definite gain in diagnostic accuracy when additional DWI features were used.
Highlights
Quantitative parameter of water diffusion in tissue, has been reported to be useful in tumor characterization[21] and treatment response evaluation[22,23] in musculoskeletal imaging
The diagnostic performance of a nomogram based on conventional and diffusion-weighted imaging (DWI) measurements together was compared with that of a nomogram based on conventional measurements alone to determine whether diagnostic accuracy increases when using DWI measurements
A total of 239 magnetic resonance imaging (MRI) examinations from 236 patients were included; 40 of the subjects were overlapped with a previous study[26]; This prior article dealt with tumor spatial heterogeneity whereas in this manuscript we report on predictive nomograms for soft tissue tumors (STTs)
Summary
Quantitative parameter of water diffusion in tissue, has been reported to be useful in tumor characterization[21] and treatment response evaluation[22,23] in musculoskeletal imaging. It was suggested that DWI can potentially improve diagnostic performance in the differentiation of benign and malignant STTs24. Most previous MRI studies regarding STT differentiation included small numbers of patients, focused on specific subtypes, or focused on a limited number of imaging findings[4,8,9,10,24]. Few investigations described a systematic imaging approach for differentiating between benign and malignant STTs8,25. We aimed to build predictive nomograms by combining known clinical and MRI measurements described in the previous literatures[2,3,4,5,6,7,8,9,10,11,12,14,15] and to validate them using a validation cohort. The diagnostic performance of a nomogram based on conventional and DWI measurements together was compared with that of a nomogram based on conventional measurements alone to determine whether diagnostic accuracy increases when using DWI measurements
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