# Radiomics Analysis in Ovarian Cancer: A Narrative Review
## 7 Computed Tomography CT
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Author** & **Year** & \begin{tabular}{c} **Population** \\ **Number** \\ \end{tabular} & \begin{tabular}{c} **Radiomic** \\ **Platform** \\ \end{tabular} & \begin{tabular}{c} **Radiomics** \\ **Features** \\ \end{tabular} & **ML Algorithm** & **Outcome** & \begin{tabular}{c} **Study** \\ **Design** \\ \end{tabular} \\ \hline [PERSON] et al. [81] & 2017 & 101 & \begin{tabular}{c} IBEX tool (Imaging \\ Biomarker Explorer \\ Software, v. 1.0\(\uprb\)) \\ \end{tabular} & 516 & Logistic regression & \begin{tabular}{c} RT, \\ PD12 \\ \end{tabular} & Retrospective \\ \hline [PERSON] et al. [82] & 2019 & 142 & ITK-SNAP & 620 & LASSO & \begin{tabular}{c} 18 months-PFS, \\ 3 years-PFS \\ \end{tabular} & Retrospective \\ \hline [PERSON] et al. [83] & 2019 & 75 & \begin{tabular}{c} ITK-SNAP- \\ MATLAB \\ \end{tabular} & 7 & \begin{tabular}{c} Cox proportional \\ hazard regressions, \\ LASSO \\ \end{tabular} & PFS \textgreater{} 24 weeks & Prospective \\ \hline [PERSON] et al. [84] & 2017 & 38 & 3D Slicer & 12 & LASSO & \begin{tabular}{c} CSR, \\ OS \\ \end{tabular} & Retrospective \\ \hline [PERSON] et al. & 2020 & 75 & \begin{tabular}{c} Computational \\ environment for \\ radiological \\ research (CERR) \\ \end{tabular} & 75 & \
\begin{tabular}{c} Support vector \\ machine, Cox \\ proportional hazard \\ regressions \\ \end{tabular} & Retrospective \\ \hline [PERSON] et al. [86] & 2019 & 364 & TextLAB 2.0 & 657 &
\begin{tabular}{c} Cox proportional \\ hazard regressions \\ \end{tabular} & OS & Retrospective \\ \hline \hline \end{tabular} RT, residual tumor; PD12, 12-month disease progression; LASSO, least absolute shrinkage and selection operator; PFS, progression-free survival; CSR, complete surgical resection; OS, overall survival.  
\end{table} 
Table 3: Technical characteristics of reviewed CT studies deploying radiomic analysis. outcome of interest was sustained clinical benefit, defined as progression-free survival (PFS) of 24 or more weeks. In multivariable analysis, higher energy largest-lesion (indicator of lower intra-tumor heterogeneity; odds ratio (OR) 1.41, (95% CI 1.11-1.81)) and fewer disease sites (OR 1.64, 95% CI 1.19-2.27) were important indicators of strong clinical benefit [83]. [PERSON] et al. evaluated the associations between clinical outcomes and radiomics-derived inter-site spatial heterogeneity metrics across multiple metastatic lesions on CT in HGSC. They reported that tumor heterogeneity metrics were most relevant for predicting complete surgical resection (CSR) and survival. Inter-site texture heterogeneity metrics were evaluated, out of which three measures outlining the differences in texture similarities across sites were associated with shorter OS. No significant association between OS and the remaining nine heterogeneity metrics that averaged the inter-site similarities was observed, though the following features were ranked highest for predicting OS using recursive feature elimination and decision trees: inter-site similarity entropy (SE), similarity cluster prominence (SCP), similarity cluster shade (SCS), correlation, and contrast. Independent evaluation of the data with leave-one-out cross-validation using LASSO regression identified SE for predicting overall survival (OS). The negative weight indicates that smaller values of SE (less heterogeneity) predicted longer OS. Supervised fuzzy c signifies clustering using the inter-site tumor heterogeneity metrics that outperformed the other texture measures for separating the low-risk group (complete resection, survival of >60 months, no _CCNE1_ amplification) and high-risk groups (incomplete surgical resection or had _CCNE1_ amplification with overall survival of <60 months) with an accuracy of 71%, true positive rate (TPR) of 60%, and true negative rate (TNR) of 86% [84]. [PERSON] et al. developed and validated a machine-learning-based integrated marker of HGSC outcomes.