## 4 Discussion  
Our results showed significant performances from pre-p-ChT radiomics and \(\Delta\)Radiomics in differentiating between responder and non-responder patients with gastric cancer treated with a FLOT regimen before surgery. The best performance for pre-p-ChT radiomics showed an AUC of 0.907 for _Cluster, Shade_, and _Autocorrelation_ (_GLCM_ features) with sensitivity of 66.67% and 88.89% and specificity of 100% and 83.33%, respectively (all \(p<0.0001\)). Additionally, \(\Delta\)Radiomics showed interesting results in terms of diagnostic performance with the best ROC curve showing an AUC = 0.889 for _MeshVolume_ (_Shape_ feature) and for _LongRunEmphasis_ (_GLRLM_ feature), with sensitivity and specificity of 66.67% and 100% for both (all \(p<0.0001\)). Up to now, only a few studies have assessed the role of radiomics in the prediction of response to perioperative or neoadjuvant chemotherapy [17; 27; 28; 29]. To the best of our knowledge, no CT radiomics studies predicting the response to perioperative chemotherapy with a FLOT regimen in gastric cancer patients have been performed. The potential of radiomics in the early identification of perioperative treatment response represents a great opportunity for developing treatment strategies. Neoadjuvant chemotherapy responders showed an improvement in survival after gastrectomy [27]. The possibility of the early identification of non-responder patients would allow a differently tailored therapeutic strategy. Different aspects of gastric cancer disease and radiomics have been investigated in several studies, which have included correlations with histological grade [28], staging, and therapeutic outcome [29; 30; 31; 32; 33]. As mentioned above, some studies have reported interesting results in response to neoadjuvant chemotherapy assessment. In particular, [PERSON] and colleagues performed pre-neoadjuvant ChT radiomics on 34 gastric cancer patients, cor  
Figure 4: Significant receiver operating characteristic (ROC) curves of \(\Delta\)Radiomics obtained from the differences between pre-perioperative chemotherapy CT and post-perioperative chemotherapy CT. The area under the curve (AUC) and the \(p\)-value are reported in each ROC curve. The best AUC achieved is represented by _MeshVolume_ and _LongRunEmphasis_ features. relating the extracted features with TRG. Their results indicated that three pre-treatment radiomic parameters (_Entropy, Root Mean Square, and Range_) have a possible predictive value in the discrimination between responders and non-responders in patients naive for treatment. Discrepancies with our study in terms of different significant radiomic features could be related to the small population sample and to their use of filtered radiomics that enhance some features more than others. Another interesting study performed by [PERSON] et al. assessed the role of radiomics in the response to neoadjuvant chemotherapy prediction. The authors enrolled 30 patients and performed radiomics on pre-treatment CT on both arterial and portal-venous phases. Results showed how portal-venous phase radiomics performed better than arterial phase radiomics in the prediction of TRG, with an AUC of 0.72 compared to one of 0.60 [17]. These results are important in terms of radiomics application due to the increased availability of portal-venous phase in comparison to arterial phase in oncologic studies, and this reinforced our decision to test radiomics on the portal-venous phase, even though more data are needed for confirmation. Moreover, [PERSON] et al. performed a combined multivariate analysis on 106 patients divided into training and validation cohorts. The researchers proposed a RAD score selected by the randomized decision tree method, and then integrated it into a RAD clinical score. Results showed how RAD score alone showed the best performance in assessing response to neoadjuvant ChT compared to RAD clinical score and clinical score only (AUC 0.82 vs. 0.70 and 0.62 respectively).