# Radiomics in Gynaecological Imaging: A State-of-the-Art Review
## 4 Cervical Cancer
### Response to Treatment  
Another interesting and widely studied field of radiomics research in patients with CC is the assessment of response to treatments including conization [98], radiotherapy, chemotherapy, and chemo-radiation therapy [99; 100; 101; 102; 103; 104; 105; 106; 107; 108; 109], the evaluation of recurrence [110; 111; 112; 113], and toxicity related to radiotherapy [114; 115; 116]. Among all studies on the assessment of response to treatments, there are four multicentric studies and one prospective study that deserve attention (Table 2). Three multicentric studies were focused on the response to neoadjuvant chemotherapy. The study by [PERSON] et al. [100] included 275 patients with locally advanced CC and showed that the combined model of the intratumoral zone of T1 WI, intratumoral zone of T2 WI, and peritumoral zone of T2 WI achieved an AUC of 0.998 in the training set and 0.999 in the testing set. [PERSON] et al. [101] included 277 patients and developed a radiomics signature containing pre- and post-contrast imaging features that were able to distinguish responders from non-responders in both primary and validation cohorts with an AUC of 0.773 and 0.816, respectively, and that remained relatively stable across centres. In addition, the combined model incorporating radiomics signature with clinical factors yielded an even better predictive performance compared with radiomics signature alone. More recently, [PERSON] et al. [109] developed a deep-learning radiomics nomogram to predict response to neoadjuvant chemotherapy with high accuracy; this nomogram performed better than the clinical model and was strongly correlated with disease-free survival. The prospective observational study by [PERSON] et al. [99] investigated the role of radiomics in predicting the response of PET and MRI in FIGO IB2 -IVA patients receiving external radiotherapy and brachytherapy. Finally, the multicentre retrospective study by [PERSON] et al. [104] analysed 183 patients from two institutions and created a radiomics model to predict pathological complete response after neoadjuvant chemoradiotherapy,yielding promising results (the RF_DEF model showed a mean AUC of 0.80). It is also interesting to mention that some studies have investigated the role of radiomics in predicting malnutrition after intervention [117], as well as haematological toxicities, changes in the pelvic bone marrow, and proctitis related to radiotherapy [114; 115; 116]. Several radiomics original studies also looked for radiomics features and signatures that could help in prognostication [113; 118; 119; 120; 121; 122; 123; 124; 125; 126; 127; 128; 129; 130]. Among these, five multicentre studies, one prospective study, and one metabanski are worth mentioning. Some of these studies have focused their attention on radiomics applied to ADC maps [120], yielding high accuracy. Other papers have analysed radiomics of T2 WI alone [127; 129] and combined it with contrast-enhanced T1 WI [119] or with ADC [126]. These models identified radiomics signatures that are promising in predicting several prognostic outcomes. A recently published meta-analysis aimed to determine the impact of radiomics in overall and disease-free survival. The authors demonstrated that current predictive models for treatment toxicity, local or distant recurrence, and survival prediction yield promising results with reasonable predictive accuracy [131]. Nevertheless, studies with large data sets, external validation, validation in prospective clinical trials, and evaluation of the integration of these models into clinical practice are still needed before routine implementation of radiomics in patients with CC. \begin{table}
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