Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
Department of Radiology, Kyung Hee University Hospital at Gangdong, Kyung Hee University School of Medicine, Seoul, Korea.
Sci Rep. 2019 Mar 20;9(1):4897. doi: 10.1038/s41598-019-41230-0.
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.
本研究旨在开发、验证并比较使用常规和弥散加权磁共振成像（MRI）测量值对软组织肿瘤（STT）恶性肿瘤进行预测的列线图。2011 年 5 月至 2016 年 12 月，回顾性纳入了 236 例经病理证实的 STT 患者的 239 次 MRI 检查，并将其随机分配至训练（n=100）和验证（n=139）队列。对每个病变的 MRI 进行评估，以评估常规和弥散加权成像（DWI）测量值。使用常规测量值和常规测量值加 DWI 测量值进行基于逻辑回归分析的多变量列线图构建。使用一致性指数（C 指数）和校准图来衡量预测准确性。分析了两种模型的 C 指数之间的统计学差异。通过留一法交叉验证和验证队列对模型进行验证。病变大小、浸润、水肿和缺乏分割脂肪征的存在是恶性肿瘤的显著且独立的预测因素，并包含在常规模型中。除了这些测量值外，还包括平均和最小表观扩散系数值到 DWI 模型中。DWI 模型仅在验证队列中显示出显著更高的诊断性能（训练队列，0.899 与 0.886，P=0.284；验证队列，0.791 与 0.757，P=0.020）。校准图显示了两个队列中列线图预测值与实际观察值之间的良好一致性。总之，使用 MRI 特征作为变量的列线图可用于预测 STT 患者的恶性肿瘤概率。当使用额外的 DWI 特征时，诊断准确性没有明显提高。
