Cholangiocarcinoma (CCA) is a malignant biliary system tumor and the second most common primary hepatic neoplasm, following hepatocellular carcinoma. CCA still has an extremely high unfavorable prognosis, regardless of type and location, and complete surgical resection remains the only curative therapeutic option; however, due to the underhanded onset and rapid progression of CCA, most patients present with advanced stages at first diagnosis, with only 30 to 60% of CCA patients eligible for surgery. Recent innovations in medical imaging combined with the use of radiomics and artificial intelligence (AI) can lead to improvements in the early detection, characterization, and pre-treatment staging of these tumors, guiding clinicians to make personalized therapeutic strategies. The aim of this review is to provide an overview of how radiological features of CCA can be analyzed through radiomics and with the help of AI for many different purposes, such as differential diagnosis, the prediction of lymph node metastasis, the defining of prognostic groups, and the prediction of early recurrence. The combination of radiomics with AI has immense potential. Still, its effectiveness in practice is yet to be validated by prospective multicentric studies that would allow for the development of standardized radiomics models.

Background: To identify the radiomics features of both granulomas and tumorlets (TL) and to assess the potential role of radiomics in differentiating these two diseases. Methods: From 2013 to 2021, ninety patients who had undergone lung surgery and pre-operative chest CT evaluation, with pathologically proven granulomas or TL, were retrospectively enrolled. Two radiologists, in consensus, manually segmented the lesions on CT images. Radiomic features were then automatically extracted from these segmentations using dedicated software. The performance of CT radiomics features in differentiating TL from granulomas was tested by receiver operating characteristic curves and the areas under the curve (AUCs), calculating sensitivity and specificity. Results: The final population consisted of 55 patients (38 female; mean age 64 ± 14 years), 32 with TL and 23 with granulomas. Significant differences were found in 16/107 radiomic features: 3 Shape, 1 First Order, 2 Grey Level Co-occurrence Matrix (GLCM), 2 Gray Level Dependence Matrix (GLDM), 4 Grey Level Run Length Matrix (GLRLM), and 4 Gray Level Size Zone Matrix (GLSZM). Flatness and Long Run High Gray Level Emphasis showed the best performances in discriminating TL from granulomas (AUC: 0.903; sensitivity: 100%; specificity: 80%; and AUC: 0.896; sensitivity: 92.3%; specificity: 76.5%; respectively; both p < 0.001). Conclusions: Radiomics may be a non-invasive imaging tool for characterization of small lung nodules, differentiating granulomas from TL, and may play a role in preventing TL growth and its possible malignant evolution, avoiding delayed diagnosis.

Background/Objectives: Neuroendocrine tumors (NETs) of the lung account for about 30% of NETs. In localized and locally advanced forms, radical surgical resection is the standard of care. Although considered indolent tumors, they appear to be susceptible to post-surgical recurrence, with rates differing between typical and atypical carcinoid. Although still debated, several clinicopathologic factors are potentially associated with recurrence. The aim of this retrospective/prospective observational study is to evaluate the predictive role of clinicopathological factors and radiomics features in patients with NET of the lung. Methods: From January 2021 to April 2024, 45 consecutive patients who underwent radical (R0) surgery for lung NET at the ENETS Center of Excellence of the Sant’Andrea Hospital were enrolled, all with at least 12 months of postoperative follow-up and availability of preoperative unenhanced chest CT. Clinicopathologic and radiomic factors were considered (107 radiomic features). Of the individual characteristics, the impact on recurrence was assessed by univariate logistic regression. Results: Among the 45 patients included, 4 patients (8.9%) experienced disease recurrence. Among the clinicopathological features, major age at diagnosis (p = 0.020), atypical carcinoid (p = 0.010), presence of functional syndrome (p = 0.002), advanced stage at diagnosis (p = 0.013), necrosis (p = 0.017) higher Ki-67 (p = 0.001), higher mitotic count (p = 0.006), and pathologic lymph node (p = 0.006) were associated with disease recurrence. Three radiomic features were found to predict recurrence: DependenceEntropy (p = 0.049), DependenceNonUniformityNormalized (p = 0.024), and Elongation (p = 0.039). In this preliminary analysis, multivariate analysis was not performed due to the small sample size. Conclusions: This study has shown that radiomics can be a valuable tool in predicting recurrence. Currently, to our knowledge, no other studies on the possible application of radiomics as prognostic factors in patients with lung NET have been published. These encouraging findings warrant further investigations with larger, multicenter cohorts to validate these results and implement them by constructing a predictive model of recurrence.

We report a sustainable and cost‐effective synthesis of cobalt phthalocyanine (CoPc) using low‐impact solvents and a streamlined purification procedure, achieving an 85% yield with limited waste generation, as indicated by an environmental factor (E‐factor) of 18.6. This route provides an eco‐friendly approach to producing a molecular electrocatalyst for reduction. The catalytic activity of the synthesized CoPc is validated by its integration into a gas diffusion electrode with carbon nanotube supports, exhibiting methanol production with a Faradaic efficiency up to 12%, matching the values reached with the conventionally synthesized counterparts. Notably, under high loading (micrometric crystallites of CoPc), ‐to‐CO conversion reached 65%, in fair alignment with literature benchmarks. Our work shows that green synthesis can yield high‐performance catalysts for electrochemical reduction (), enabling sustainable methanol production.

Background/Objectives: Reliable preoperative assessment of treatment response after neoadjuvant chemoimmunotherapy in locally advanced non-small-cell lung cancer (NSCLC) remains challenging because conventional imaging may not accurately distinguish viable tumour from treatment-related fibrosis or immune-mediated changes. This study investigated whether CT-derived radiomic features combined with machine learning could improve the identification of patients achieving pathological complete response (pCR). Methods: Twenty-nine consecutive patients with stage III NSCLC who underwent surgical resection following neoadjuvant chemoimmunotherapy were retrospectively analysed. Radiomic features were extracted from preoperative CT scans and used to develop supervised machine-learning models based on Random Forest, Support Vector Machine, K-Nearest Neighbors, Multi-Layer Perceptron, and Logistic Regression algorithms. Histopathological findings after surgery served as the reference standard. Feature distributions were compared between patients with and without pCR using the Mann–Whitney U test with Bonferroni correction. Results: Surgical procedures included 19 lobectomies, 2 bilobectomies, 3 pneumonectomies, and 5 complex major resections. Pathological complete response was observed in 9 of 29 patients (31%). The proposed radiomics-based model achieved an area under the ROC curve of 0.92 (95% CI, 0.81–1.00), with 91% accuracy, 90% sensitivity, and 92% specificity (p < 0.05). All patients classified by the model as complete responders were confirmed to have pathological complete response at postoperative histological examination. Conclusions: CT-based radiomics combined with machine learning demonstrated promising performance for the preoperative prediction of pathological response after neoadjuvant chemoimmunotherapy in stage III NSCLC. Although these findings require external validation in larger prospective cohorts, this approach may support preoperative treatment assessment and surgical decision-making.