# Radiomics in Gynaecological Imaging: A State-of-the-Art Review
## 4 Cervical Cancer
### Response to Treatment
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**First** & **Publication** & **Study Type** & **Geographical** & **Sample** & **Main Findings** & **ML Method** & **PMID** \\
**Author** & **Year** & **Study Type** & **Area** & **Size** & **Radiomics features on T2 WI, ADC, and contrast-enhanced T1 WI are associated with LNM. Moreover, the radiomic signature can depict LNM with an AUC of 0.825 & LASSO & 32974143 \\ \hline [PERSON] [104] & 2021 & Retrospective, Multicenter & E & 183 & \begin{tabular}{c} A radiomics model can predict \\ pathological complete response after \\ neoadjuvant chemotherapy by \\ using T2 WI with an AUC of 0.80 \\ \end{tabular} & N/A & 33807494 \\ \hline [PERSON] Y [88] & 2021 & Retrospective, Multicenter & A & 219 & \begin{tabular}{c} A CT-based radiomic model can predict \\ normal-size LNM with an AUCs of \\ 0.912 in the training cohort, 0.859 in the internal validation cohort, and 0.800 in \\ the external validation cohort \\ \end{tabular} & SVM & 33975178 \\ \hline [PERSON] H [111] & 2022 & Retrospective, Multicenter & A & 204 & \begin{tabular}{c} Radiomics combined with clinical \\ parameters can increase the prediction \\ of OPR after chemotherapy, with an \\ AUC of 0.709 \\ \end{tabular} & LASSO & 34865079 \\ \hline [PERSON] [75] & 2023 & Retrospective & A & 235 & \begin{tabular}{c} Radiomics can differentiate \\ adenocarcinoma and squamous cell \\ carcinoma with an AUC of 0.777 and \\ 0.750 on T2 WI and T1 WI. AUC can \\ depict low- and high-FIGO stages \\ (AUC = 0.716 and 0.676). Good results \\ were found also in the detection of \\ tumour grade. \\ \end{tabular} & LASSO & 34918963 \\ \hline [PERSON] [89] & 2022 & Retrospective, Multicenter & A & 169 & \begin{tabular}{c} A radiomic signature nomogram can \\ predict LNM status better than a \\ radiomics or clinical model alone \\ (AUC = 0.891 vs. 0.830 vs. 0.812) \\ \end{tabular} & LASSO & 34968703 \\ \hline [PERSON] [126] & 2022 & Retrospective, Multicenter & A & 263 & \begin{tabular}{c} A radiomic signature consisting of four \\ radiomic features for disease-free \\ survival prediction demonstrated better \\ prognostic performance in both primary \\ and validation cohorts (C-index: 0.736 \\ and 0.758, respectively) compared with \\ a clinical-based model (C-index: 0.603 \\ and 0.649, respectively) \\ \end{tabular} & LASSO, regression & 35145910 \\ \hline [PERSON] [127] & 2022 & Retrospective, Multicenter & E & 175 & \begin{tabular}{c} A radiomic model can predict overall \\ survival before starting \\ chemoradiotherapy with an AUC of 0.73 \\ \end{tabular} & LoR & 35325372 \\ \hline [PERSON] [129] & 2022 & Retrospective, Multicenter & A & 83 & \begin{tabular}{c} Authors developed two radiomics \\ models to predict the overall survival by \\ concurrent chemoradiotherapy along or \\ \end{tabular} & \begin{tabular}{c} Elastic Net \\ Regressor \\ \end{tabular} & \\ [PERSON] G [129] & 2022 & Retrospective, Multicenter & A & 83 & \begin{tabular}{c} A radiomic model can predict \\ models to predict the overall survival by \\ concurrent chemoradiotherapy along or \\ by adjuvant chemotherapy, with AUCs \\ of 0.832 and 0.879, respectively \\ \end{tabular} & LASSO & 36536572 \\ \hline [PERSON] Y [96] & 2023 & Retrospective, Multicenter & A & 168 & \
\begin{tabular}{c} The nomogram showed high predictive \\ performance in the training (AUC: 0.883) and test cohort (AUC: 0.830) for \\ predicting LVSI \\ \end{tabular} & [PERSON], LASSO & 36929220 \\ \hline Zhang Y [109] & 2023 & Retrospective, Multicenter & A & 285 &
\begin{tabular}{c} Radiomics signature showed favourable \\ predictive values in differentiating \\ responders from non-responders to \\ neoadjuvant chemotherapy with high \\ AUCs (over 0.90) \\ \end{tabular} & LASSO & 36980381 \\ \hline \hline \end{tabular}  
* ML: machine learning; PMID: PubMed identifier; N: North America; A: Asia–Pacific; E: Europe; MRI: magnetic resonance imaging; DWI: diffusion-weighted imaging; FDG: fluorodeoxyglucose; PET: positron emission tomography; CT: computed tomography; CC: cervical cancer; ADC: apparent diffusion coefficient; RT: radiotherapy; AUC: area under the curve; 2D: two-dimensional; 3D: three-dimensional; T2 WI: T2-weighted imaging; TIWI: T1-weighted imaging; LNM: lymph node metastases; LN: lymph node; OFR: out-of-field recurrence; ROC: receiver operating characteristic LASSO: least absolute shrinkage and selection operator; FIGO: International Federation of Gyneology and Obstetrics; SPAIR: spectral attenuated inversion recovery; LVSI: lymph vascular space invasion; N/A: not applicable/not present; RF: random forest regression; LASSO: least absolute shrinkage and selection operator logistic regression; LoR: logistic regression; SVM: support vector regression.  
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