# Radiomics in Gynaecological Imaging: A State-of-the-Art Review
## 7 Conclusions  
In conclusion, several radiomics studies applied to gynaecological pathologies have shown promising results in terms of diagnostic and prognostic efficacy. However, many radiomics models were developed using small populations and did not include validation cohorts, which raises doubts about the reproducibility of their results. Moreover, these investigations were mostly performed using manual segmentation methods and thus can be extremely time consuming, especially in the case of radiomics features extracted from multiple acquisitions. For these reasons, researchers' efforts in the future should concentrate on improving the reproducibility and feasibility of radiomics models, in order to accelerate the path to their effective clinical application and, hopefully, to increase their diagnostic and therapeutic impact.  
Conceptualization, P.N.F., F.V., C.M. and R.C.; methodology, P.N.F., F.V., C.M. and R.C.; investigation, P.N.F., F.V., C.M. and R.C.; resources, P.N.F., F.V., C.M. and R.C.; data curation, P.N.F., F.V., C.M. and R.C.; writing--original draft preparation, P.N.F., F.V., C.M. and R.C.; writing--review and editing, P.N.F., F.V., C.M., R.C., M.O.-G. and D.I.; visualization, P.N.F. and C.M.; supervision, F.V., C.M., M.O.-G. and D.I.; project administration, P.N.F., F.V., C.M. and R.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Ethical review and approval were waived for this study due to the nature of the study.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**First Author** & **Publication Year** & **Study Type** & **Geographical Area** & **Sample Size** & **Main Findings** & **ML Method** & **PMID** \\ \hline [PERSON] [150] & 2022 & Retrospective & A & 1329 & The machine learning classifier provided an AUC of 0.91 for the radiomics model and 0.96 for the mixed model for the differential diagnosis between benign and malignant ovarian tumours & KNN, XGBost & 36016613 \\ \hline [PERSON] [157] & 2022 & Retrospective, Multicentre & A & 417 & The combined model had an AUC of 0.86 for the differentiation between benign and borderline epithelial ovarian tumours in the external validation set & LoR & 35943620 \\ \hline [PERSON] [155] & 2022 & Retrospective, Multicentre & E & 323 & The radiomic prognostic vector score was independently associated with significantly worse progression-free survival & Cox regression & 34923575 \\ \hline [PERSON] [162] & 2023 & Retrospective & A & 128 & The radiomic-clinical nomogram had an AUC of 0.900 for the prediction of residual tumour in the separate validation cohort & LASSO & 36587996 \\ \hline Li H [163] & 2023 & Retrospective, Multicentre & A & 301 & The combined radiomics nomogram had an AUC of 0.799 for the prediction of platinum resistance in the testing cohort & LoR & 36995415 \\ \hline \hline \end{tabular}  
ML: machine learning; PMID: PubMed identifier; A: Asia-Pacific; N: North America; E: Europe; CT: computed tomography; MRI: magnetic resonance imaging; AUC: area under the curve; T2 WI: T2-weighted imaging: LASSO: least absolute shrinkage and selection operator logistic regression; LoR: logistic regression; KNN: k-nearest neighbour; SVM: support vector machines; RF: random forest; MLP: multi-layer perceptron; XGBost: extreme gradient boosting.  
\end{table}
Table 3: _Cont._Conflicts of Interest: None of the authors declared a conflict of interest for this study. [PERSON] has the following disclosures: support for attending meetings from Bracco and Bayer; research collaboration with Siemens Healthcare; co-funding by the European Union--FESR or FSE, PON Research and Innovation 2014-2020-DM 1062/2021. [PERSON] has the following disclosure, not related to this work: received support from GE Healthcare and Bracco Imaging to attend meetings and from [PERSON] for a lecture.