Preoperative Differentiation of Uterine Sarcoma from Leiomyoma: Comparison of Three Models Based on Different Segmentation Volumes Using Radiomics

Purpose: To investigate the impact of applying three different volume of interests (VOIs) in ADC map-based radiomic analysis and compare their diagnostic performance in the differentiation of uterine sarcoma and atypical leiomyoma.

Procedures: Seventy-eight patients (29 uterine sarcomas, 49 uterine leiomyomas) imaged with pelvic magnetic resonance imaging (MRI) prior to surgery were included in this retrospective study. Manually, segmentations of VOIs covered three different regions on apparent diffusion coefficient (ADC) maps: (1) tumor, (2) tumor and small piece of surrounded tissue, and (3) whole uterus. Texture and non-texture features were extracted from each VOI. The 0.623 + bootstrap method and the area under the receiver-operating characteristic curve (AUC) were used to select the features. Twenty logistic regression models (orders of 1-20) based on different combination of image features were built for each way of image segmentation.

Results: For the first VOI region, model 18 with 18 features yielded the highest AUC of 0.830, sensitivity of 76.0 %, specificity of 73.2 %, and accuracy of 73.9 %. For the second VOI region, model 17 with 17 features yielded the highest AUC of 0.853, sensitivity of 75.5 %, specificity of 75.5 %, and accuracy of 76.8 %. For the third VOI region, model 20 with 20 features yielded the highest AUC of 0.876, sensitivity of 76.3 %, specificity of 84.5 %, and accuracy of 82.4 %.

Conclusions: Radiomic model based on features extracted from VOI that covered the whole uterus had the best diagnostic performance. Adopting VOI contained more image information that was useful in improving diagnostic performance of radiomic model.