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Sci Rep. 2019 Mar 20;9(1):4897. doi: 10.1038/s41598-019-41230-0.

Development and Validation of Nomograms for Malignancy Prediction in Soft Tissue Tumors Using Magnetic Resonance Imaging Measurements.

Author information

1
Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
2
Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea. youngcheol.yoon@gmail.com.
3
Department of Radiology, Kyung Hee University Hospital at Gangdong, Kyung Hee University School of Medicine, Seoul, Korea.
4
Department of Radiology, Soonchunhyang University Bucheon Hospital, Bucheon, Korea.
5
Statistics and Data Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Korea.

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.

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