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Background FLOT regimen is the standard perioperative treatment in Western countries for patients with locally advanced gastric (GC) or gastroesophageal junction cancer (GEJC). High microsatellite instability (MSI-H) and Mismatch Repair deficient (dMMR) demonstrated a favorable prognostic role and a concomitant negative predictive impact on the benefit of perioperative 5-fluorouracil-based doublets; however, its role in pts receiving FLOT chemotherapy is still unclear. Methods This is a retrospective, multicenter observational study of 265 pts with GC/GEJC treated with perioperative FLOT regimen in 11 Italian oncology centers between January 2017 to December 2021 and analyzed for microsatellite status. Results The MSI-H phenotype was found in 27 (10.2%) of 265 analyzed tumors. Compared to microsatellite stable (MSS) and Mismatch Repair proficient (pMMR) cases, MSI-H/dMMR were more frequently female (48.1% vs. 27.3%, p = 0.0424), elderly pts (age > 70 years, 44.4% vs. 13.4%, p = 0.0003), Laurens’s intestinal type (62.5% vs. 36.1%, p = 0.02) and pts with a primary location tumor in the antrum (37 vs. 14.3%, p = 0.0004). A statistically significant difference in the rate of pathologically negative lymph node emerged (63% vs 30.7%, p = 0.0018). Compared to the MSS/pMMR tumor population, the MSI-H/dMMR subgroup had a better DFS (median not reached [NR] vs. 19.5 [15.59–23.59] mos, p = 0.031) and OS (median NR vs. 34.84 [26.68–47.60] mos, p = 0.0316). Conclusions These real-world data confirm that FLOT treatment is effective in daily clinical practice for locally advanced GC/GEJC, also in the MSI-H/dMMR subgroup. It also showed a higher rate of nodal status downstaging and a better outcome of MSI-H/dMMR pts in comparison to MSS/pMMR.

In the fight against the racketeering and terrorism, knowledge about the structure and the organization of criminal networks is of fundamental importance for both the investigations and the development of efficient strategies to prevent and restrain crimes. Intelligence agencies exploit information obtained from the analysis of large amounts of heterogeneous data deriving from various informative sources including the records of phone traffic, the social networks, surveillance data, interview data, experiential police data, and police intelligence files, to acquire knowledge about criminal networks and initiate accurate and destabilizing actions. In this context, visual representation techniques coordinate the exploration of the structure of the network together with the metrics of social network analysis. Nevertheless, the utility of visualization tools may become limited when the dimension and the complexity of the system under analysis grow beyond certain terms. In this paper we show how we employ some interactive visualization techniques to represent criminal and terrorist networks reconstructed from phone traffic data, namely foci, fisheye and geo-mapping network layouts. These methods allow the exploration of the network through animated transitions among visualization models and local enlargement techniques in order to improve the comprehension of interesting areas. By combining the features of the various visualization models it is possible to gain substantial enhancements with respect to classic visualization models, often unreadable in those cases of great complexity of the network., 6 pages, 4 figures, DataWiz 2014 (held in conjunction with ACM Hypertext 2014)

Online Social Networks (OSN) during last years acquired a huge and increasing popularity as one of the most important emerging Web phenomena, deeply modifying the behavior of users and contributing to build a solid substrate of connections and relationships among people using the Web. In this preliminary work paper, our purpose is to analyze Facebook, considering a significant sample of data reflecting relationships among subscribed users. Our goal is to extract, from this platform, relevant information about the distribution of these relations and exploit tools and algorithms provided by the Social Network Analysis (SNA) to discover and, possibly, understand underlying similarities between the developing of OSN and real-life social networks., Comment: 6 pages, 1 figure; MIFI '10: Proceedings of the 1st International Workshop on Mining the Future Internet

3 adenomas and 5 carcinomas of the ampulla of Vater are studied to evaluate their histogenesis and diagnostic significance of the antigen defined by the monoclonal antibody CA 19/9. In the adenomas, some dysplastic glands show a faint positivity for CA 19/9 antigen, whereas in the ampullomas this antigen is present in adenomatous residue and in well differentiated adenocarcinomatous areas. The present observations indicate that the endoscopical and macro-microscopical examinations are fundamental to evaluate the location of the ampullary neoplasia. Moreover, the immunohistochemical finding of the CA 19/9 antigen in adenomas and carcinomas suggests their histogenetic relationship, but it is not useful in their differential diagnosis.

Aim: A link has been established between malnutrition, immunological status, and hepatocellular carcinoma (HCC). The prognostic nutritional index (PNI) has been recognized as a prognostic indicator in early-stage HCC and in patients treated with first-line therapy. However, to date, the role of the PNI in HCC patients treated with regorafenib has not been reported. Methods: We undertook a multicentric analysis on a cohort of 284 patients affected by advanced HCC treated with regorafenib. The PNI was calculated as follows: 10×serum albumin concentration (g/dl)+0.005×peripheral lymphocyte count (number/mm3). Univariate and multivariate analyses were used to investigate the association between PNI and survival outcomes. Results: A PNI cut-off value of 44.45 was calculated by a receiver operating characteristic analysis. The median overall survival was 12.8 and 7.8 months for patients with high (>44.45) and low (≤44.45) PNI, respectively (hazard ratio, 0.58; 95% confidence interval, 0.43–0.77; p=0.0002). In the univariate and multivariate analyses, low PNI value and increased serum bilirubin level emerged as independent prognostic factors for overall survival. No differences were found between high and low PNI in terms of progression-free survival (p=0.14). Conclusion: If validated, the PNI could represent an easy-to-use prognostic tool able to guide the clinical decision-making process in HCC patients treated with regorafenib.

Background: Gemcitabine plus nab-paclitaxel (Gem-Nab) represents a standard first-line treatment for metastatic pancreatic cancer (mPC), but few data are available for elderly patients. We aimed to add evidence about safety and efficacy of Gem-Nab in this population. Methods: We collected data of 156 patients with mPC aged ≥65 years receiving Gem-Nab. Patients were stratified according to age: 0.05). The starting dose of Gem-Nab did not affect PFS and OS (p > 0.05). Conclusion: Gem-Nab is active and effective in older patients with mPC, with the results in line with the general mPC population enrolled in clinical trials. Mild dose modifications for elderly patients might be considered to improve safety without impairing efficacy.

Background: Recently, three published phase III trials highlighted the superiority of investigational drugs compared to placebo, thus leading to their approval in the second-line setting. We report here a MAIC of second-line MKI options for patients with HCC previously treated with sorafenib using individual real-world data of regorafenib and aggregate data of second-line cabozantinib from the CELESTIAL trial. Methods: Data from 278 patients who received regorafenib as second-line therapy after sorafenib failure for unresectable HCC were used as IPD. Data inclusion were adapted to those reported in the CELESTIAL trial in the subset of patients who received sorafenib as the only prior therapy. Survival medians and rates were obtained from Kaplan–Meier curves, and differences between regorafenib and cabozantinib groups were explored through Cox regression adjusted for weights originating from MAIC. Results: The median OS of the weighted regorafenib group was 11.1months (IQR: 5.6–16.4) and 11.3 (IQR: 6.7–22.4) for cabozantinib; HR 0.83 (95%CI 0.62–1.09). The median PFS of the weighted regorafenib group was 3.0months (IQR: 1.9–4.8) and 5.5 (IQR: 2.3–9.3) for cabozantinib; HR 0.50 (95%CI 0.41–0.62). In the subgroup who received prior sorafenib for < 3months, the median OS of the regorafenib group was 6.5months (IQR: 4.7–10.9) and 9.5months (IQR: 5.9–18.2) for cabozantinib; HR 0.68 (95%CI 0.39–1.16). In the subgroup receiving prior sorafenib for 3 to < 6months, the median OS of the regorafenib group was 8.0months (IQR: 4.2–15.2) and 11.5 (IQR: 6.5–23.9) for cabozantinib; HR 0.66 (95%CI 0.42–1.02). In the subgroup receiving prior sorafenib for ≥ 6months, the median OS of the regorafenib group was 13.4 (IQR: 8.1–46.5) and 12.3 (IQR: 6.6–22.9) for cabozantinib; HR 0.89 (95%CI 0.52–1.51). Conclusion: Our results confirmed no differences between regorafenib and cabozantinib in terms of OS. However, in earlier progressors on prior sorafenib a larger benefit might be expected from cabozantinib treatment.

Compared to other types of social networks, criminal networks present hard challenges, due to their strong resilience to disruption, which poses severe hurdles to law-enforcement agencies. Herein, we borrow methods and tools from Social Network Analysis to (i) unveil the structure of Sicilian Mafia gangs, based on two real-world datasets, and (ii) gain insights as to how to efficiently disrupt them. Mafia networks have peculiar features, due to the links distribution and strength, which makes them very different from other social networks, and extremely robust to exogenous perturbations. Analysts are also faced with the difficulty in collecting reliable datasets that accurately describe the gangs' internal structure and their relationships with the external world, which is why earlier studies are largely qualitative, elusive and incomplete. An added value of our work is the generation of two real-world datasets, based on raw data derived from juridical acts, relating to a Mafia organization that operated in Sicily during the first decade of 2000s. We created two different networks, capturing phone calls and physical meetings, respectively. Our network disruption analysis simulated different intervention procedures: (i) arresting one criminal at a time (sequential node removal); and (ii) police raids (node block removal). We measured the effectiveness of each approach through a number of network centrality metrics. We found Betweeness Centrality to be the most effective metric, showing how, by neutralizing only the 5% of the affiliates, network connectivity dropped by 70%. We also identified that, due the peculiar type of interactions in criminal networks (namely, the distribution of the interactions frequency) no significant differences exist between weighted and unweighted network analysis. Our work has significant practical applications for tackling criminal and terrorist networks., 12 pages, 4 figures, paper submitted to PLOS ONE Journal

In this paper, we focus on the study of Sicilian Mafia organizations through Social Network Analysis. We analyse datasets reflecting two different Mafia Families, based on examinations of digital trails and judicial documents, respectively. The first dataset includes the phone calls logs among suspected individuals. The second one is based on police traces of meeting that have taken place among different types of criminals. Our breakthrough is twofold. First in the method followed to generate these new datasets. Second, in the method used to carry out a quantitative phenomena investigation that are hard to evaluate. Our networks are weighted ones, with each weight catching the frequency of interactions between criminals. Therefore, our analysis focuses on weight and shortest paths distributions in both networks. We identify new types of unusual interactions in the Mafia networks, leading to substantial differences between Mafia networks and other types of criminal or terrorist networks.

Link prediction exercises may prove particularly challenging with noisy and incomplete networks, such as criminal networks. Also, the link prediction effectiveness may vary across different relations within a social group. We address these issues by assessing the performance of different link prediction algorithms on a mafia organization. The analysis relies on an original dataset manually extracted from the judicial documents of operation “Montagna”, conducted by the Italian law enforcement agencies against individuals affiliated with the Sicilian Mafia. To run our analysis, we extracted two networks: one including meetings and one recording telephone calls among suspects, respectively. We conducted two experiments on these networks. First, we applied several link prediction algorithms and observed that link prediction algorithms leveraging the full graph topology (such as the Katz score) provide very accurate results even on very sparse networks. Second, we carried out extensive simulations to investigate how the noisy and incomplete nature of criminal networks may affect the accuracy of link prediction algorithms. The experimental findings suggest the soundness of link predictions is relatively high provided that only a limited amount of knowledge about connections is hidden or missing, and the unobserved edges follow some kind of generative law. The different results on the meeting and telephone call networks indicate that the specific features of a network should be taken into careful consideration.