# Radiomics in Gynaecological Imaging: A State-of-the-Art Review
## 3 Endomet Cancer
### Lymph Vascular Space Invasion and Tumour Grading  
LVSI, defined as the presence of tumour cells in a space lined by endothelial cells outside the invasive border, is another key prognostic factor in EC [49]. In [PERSON] et al.'s research, 12 texture features extracted from tumour segmentation of 137 MRIs finally correlated with LVSI. AUC, sensitivity, specificity, and accuracy were estimated at 0.80, 80.9%, 72.5%, 76.6%, and 74.3% [38]. A further study performed on a small cohort of MRIs (\(n=73\)) demonstrated that texture analysis may have a limited role in the diagnosis of LVSI since it yielded low diagnostic values (AUC: 0.59; sensitivity: 71%; specificity: 59%) [50]. Almost comparable values were observed by [PERSON] and colleagues. They evaluated an LVSI predictive model based on a single feature from ADC as a predictor, which achieved an AUC of 0.59, a sensitivity of 50%, and a specificity of 61% [51]. More recently, four investigations registered more promising performances of radiomics analysis for LVSI assessment, with reported AUC values between 0.79 and 0.85 [44; 45; 46; 52].  
Several authors also investigated how radiomics may be useful for the assessment of EC histological grades. In a prospective investigation, a texture feature obtained from DCE images was capable of independently predicting a high-risk histological EC subtype (OR 1.01, \(p=0.004\)) [40]. In a recently published multicentre research, [PERSON] et al. developed a model based on clinical and radiomic features for pathological grade prediction, which outperformed clinical and radiomics-only models, yielding AUCs of 0.920, 0.882, and 0.881 for the training, internal validation, and external validation sets, respectively [53]. Moreover, the classification model based on clinical and T2 WI signatures generated by [PERSON] et al. obtained very promising results for histological type prediction (AUC: 0.91) on the independent external testing dataset [45]. [PERSON] et al. explored the possible role of harmonic signatures for predicting EC high tumour grade, describing a good performance with an AUC, a sensitivity, and a specificity of 0.81, 93%, and 63%, respectively [54].  
With regard to the assessment of the clinical risk category, one of the most robust studies in the literature was published in 2020 by [PERSON] and colleagues on a cohort of 717 pathological confirmed ECs, divided into a primary group and two validation groups. The authors developed a radiomics nomogram by combining selected radiomics MRI features and clinical parameters. The AUC for the prediction of high-risk EC for the radiomics nomogram in the primary group and the validation groups were 0.896, 0.877, and 0.919, respectively [55]. [PERSON] et al. compared the performance for predicting low-risk EC in a cohort of 102 patients with pathologically proven stage I EC of a model based on clinical and conventional MRI characteristics with those of a model based only on radiomic features extracted from T2 WI. Their results showed that the radiomic model had a better performance (AUC of 0.946 vs. AUC of 0.756) [56]. Conversely, the model proposed by [PERSON] et al. for predicting low-risk EC, which was based on two features from ADC maps and T2 WI, had a less promising performance (AUC: 0.74) [51]. Moreover, the abovementioned model generated by [PERSON] et al. was also tested for predicting high-risk categories, reporting an AUC of 0.82 [45].