Pancreatic ductal adenocarcinoma is a highly aggressive tumor with an overall 5-year survival rate of less than 5%. Prognosis and treatment depend on whether the tumor is resectable or not, which mostly depends on how quickly the diagnosis is made. Chemotherapy and radiotherapy can be both used in cases of non-resectable pancreatic cancer. In cases of pancreatic neoplasm that is locally advanced, non-resectable, but non-metastatic, it is possible to apply percutaneous treatments that are able to induce tumor cytoreduction. The aim of this article will be to describe the multiple currently available treatment techniques (radiofrequency ablation, microwave ablation, cryoablation, and irreversible electroporation), their results, and their possible complications, with the aid of a literature review.

Background/Aim: Re-staging of ductal adenocarcinoma using computed tomography (CT) scan can be problematic so new imaging techniques and evaluation parameters are required. The aim of the study was to evaluate the added value of CT texture analysis in estimation of tissue changes in ductal adenocarcinoma downsized and resected after chemotherapy. Materials and Methods: Patients with ductal adenocarcinoma downstaged after neoadjuvant treatment, and resected, were included. A pre- and post-treatment CT was obtained. In comparison, patients with disease progression were included for texture analysis evaluation at CT pre- and post-treatment. CT texture analysis results were compared. Results: A total of 17 patients affected by un-resectable or borderline ductal adenocarcinoma reached the resectable stage after treatment. The comparison between Kurtosis pre- and Kurtosis post-treatment showed a statistically significant difference. On the contrary, in the comparison group composed of 14 patients with disease progression there was no statistical difference regarding this parameter. Conclusion: This evaluation may represent an added value in tumor tissue changes judgment and can be extremely useful to diagnose downstaging in those cases with no evident downsizing after chemotherapy.

Purpose: To classify COVID-19, COVID-19-like and non-COVID-19 interstitial pneumonia using lung CT radiomic features. Material and Methods: CT data of 115 patients with respiratory symptoms suspected for COVID-19 disease were retrospectively analyzed. Based on the results of nasopharyngeal swab, patients were divided into two main groups, COVID-19 positive (C +) and COVID-19 negative (C−), respectively. C− patients, however, presented with interstitial lung involvement. A subgroup of C−, COVID-19-like (CL), were considered as highly suggestive of COVID pneumonia at CT. Radiomic features were extracted from the whole lungs. A dual machine learning (ML) model approach was used. The first one excluded CL patients from the training set, eventually included on the test set. The second model included the CL patients also in the training set. Results: The first model classified C + and C− pneumonias with AUC of 0.83. CL median response (0.80) was more similar to C + (0.92) compared to C− (0.17). Radiomic footprints of CL were similar to the C + ones (possibly false negative swab test). The second model, however, merging C + with CL patients in the training set, showed a slight decrease in classification performance (AUC = 0.81). Conclusion: Whole lung ML models based on radiomics can classify C + and C− interstitial pneumonia. This may help in the correct management of patients with clinical and radiological stigmata of COVID-19, however presenting with a negative swab test. CL pneumonia was similar to C + pneumonia, albeit with slightly different radiomic footprints.