# Radiomics Analysis in Ovarian Cancer: A Narrative Review
## 6 Magnetic Resonance Imaging  
The value of MRI in the assessment of adnexal masses has increased over time [58; 59; 60]. With high soft-tissue resolution and no radiation, MRI is widely used to evaluate the etiology of adnexal lesions. Despite these aforementioned advantages, it is still not easy to identify OC subtypes with MRI alone [61; 62; 63; 64], and the differentiation of masses remains a challenge due to the morphological complexity of tumors and overlap [65]. Ovarian-Adnexal Reporting and Data System Magnetic Resonance Imaging (O-RADS MRI), similar to the aforementioned O-RADS ultrasound, was also recently proposed. It \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Author** & **Year** & **Population** & **Radiomic Platform** & **Radiomic Features** & **ML Algorithms** & **Outcome** & **Study Design** \\ \hline [PERSON] et al. [42] & 2020 & 241 & TRACE4\(\copyright\) & 319 & Ensemble & \begin{tabular}{c} Benign/malignant \\ Oms \\ \end{tabular} & Retrospective \\ \hline [PERSON] et al. [56] & 2021 & 127 & \
\begin{tabular}{c} Python 3.7.0 and \\ package \\ Pyradiomics 2.2.0 \\ \end{tabular} & 97 & U-net &
\begin{tabular}{c} Segmentation \\ (U-net vs. human) \\ \end{tabular} & Retrospective \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Technical characteristics of reviewed ultrasound studies deploying radiomic analysis. aims to ensure uniform clear MRI evaluations of ovarian or other adnexal lesions, giving each lesion a risk category of malignancy, which directs the proper management. Five risk classes were established [66]. The latter algorithm was validated prospectively in a study on 1340 women. The AUROC for differentiating benign from malignant tumors was 0.961 (95% CI 0.948-0.971) among skilled radiologists [67]. According to many studies encountered, the qualitative diagnosis of OC by MRI is superior to other imaging methods [68; 69; 70]; nevertheless, more advanced techniques such as radiomics analysis may be necessary to improve the characterization and/or differentiation of complex adnexal masses by analyzing additional aspects as reported in Table 2[71]. [PERSON] et al. assessed the ability of radiomic MRI to classify OMs in benign or malignant and in type I and type II OC. They also determined the association between MRI radiomics and survival among OC patients. Their results reported that radiomic features derived from MRI were highly correlated with OC classification and prognosis with high accuracy. In particular, for benign versus malignant classification, the accuracy was 0.90, and for the type I and type II OC discrimination task, the accuracy was 0.93 [72]. Furthermore, to differentiate benign, malignant, and borderline OMs, [PERSON] et al. evaluated the ability of second and third class classification predictive tasks done with radiomic features applied to a dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) pharmacokinetic (PK) protocol. The second class classification task was split into three subtasks--benign vs. borderline (task A), benign vs. malignant (task B), and borderline vs. malignant (task C). The combination of the PK radiomics signatures model (PK model) represents a potential technique to carry out a differential diagnosis among benign, borderline, and malignant OM [73], showing an AUC of 0.899, 0.865, and 0.893, respectively. Additionally, the three-class classification task exhibited a good discrimination performance with AUCs of 0.893, 0.944, and 0.891 for tasks A, B, and C, respectively. Two studies in the literature evaluated the performance of radiomics applied to MRI images to predict the type of EOC. [PERSON] et al. also utilized an MRI-based radiomics model to differentiate between type I and type II OC, recognizing the critical region for differential diagnosis [74]. They reported that the combined model did well in both the internal and external validation cohorts (ROC AUCs of 0.806 and 0.847, respectively).