Unenhanced whole-body MRI versus PET-CT for the detection of prostate cancer metastases after primary treatment

The aim of this study was to evaluate the accuracy of unenhanced whole-body MRI, including whole-body Diffusion Weighted Imaging (DWI), used as a diagnostic modality to detect  pathologic lymph nodes and skeletal metastases in patients with prostate cancer (PCa) undergoing restaging after primary treatment

Preoperative staging of colorectal cancer using virtual colonoscopy: correlation with surgical results

The aim of this study was to evaluate the clinical usefulness of computed tomography colonography (CTC) in the preoperative staging in patients with abdominal pain for occlusive colorectal cancer (CRC) and to compare the results of CTC with the surgical ones

Identification of hemodynamically stable patients with acute pulmonary embolism at high risk for death: external validation of different models

Background: The optimal strategy for identification of hemodynamically stable patients with acute pulmonary embolism (PE) at risk for death and clinical deterioration remains undefined. Objectives: We aimed to assess the performances of currently available models/scores for identifying hemodynamically stable patients with acute, symptomatic PE at risk of death and clinical deterioration. Methods: This was a prospective multicenter cohort study including patients with acute PE (NCT03631810). Primary study outcome was in-hospital death within 30 days or clinical deterioration. Other outcomes were in-hospital death, death, and PE-related death, all at 30 days. We calculated positive and negative predictive values, c-statistics of European Society of Cardiology (ESC)-2014, ESC-2019, Pulmonary Embolism Thrombolysis (PEITHO), Bova, Thrombo-embolism lactate outcome study (TELOS), fatty acid binding protein, syncope and tachicardia (FAST), and National Early Warning Scale 2 (NEWS2) for the study outcomes. Results: In 5036 hemodynamically stable patients with acute PE, positive predictive values for the evaluated models/scores were all below 10%, except for TELOS and NEWS2; negative predictive values were above 98% for all the models/scores, except for FAST and NEWS2. ESC-2014 and TELOS had good performances for in-hospital death or clinical deterioration (c-statistic of 0.700 and 0.722, respectively), in-hospital death (c-statistic of 0.713 and 0.723, respectively), and PE-related death (c-statistic of 0.712 and 0.777, respectively); PEITHO, Bova, and NEWS2 also had good performances for PE-related death (c-statistic of 0.738, 0.741, and 0.742, respectively). Conclusion: In hemodynamically stable patients with acute PE, the accuracy for identification of hemodynamically stable patients at risk for death and clinical deterioration varies across the available models/scores; TELOS seems to have the best performance. These data can inform management studies and clinical practice

Clinical patterns of hepatocellular carcinoma in nonalcoholic fatty liver disease: A multicenter prospective study

107noNonalcoholic fatty liver disease (NAFLD) represents the hepatic manifestation of metabolic syndrome and may evolve into hepatocellular carcinoma (HCC). Only scanty clinical information is available on HCC in NAFLD. The aim of this multicenter observational prospective study was to assess the clinical features of patients with NAFLD-related HCC (NAFLD-HCC) and to compare them to those of hepatitis C virus (HCV)-related HCC. A total of 756 patients with either NAFLD (145) or HCV-related chronic liver disease (611) were enrolled in secondary care Italian centers. Survival was modeled according to clinical parameters, lead-time bias, and propensity analysis. Compared to HCV, HCC in NAFLD patients had a larger volume, showed more often an infiltrative pattern, and was detected outside specific surveillance. Cirrhosis was present in only about 50% of NAFLD-HCC patients, in contrast to the near totality of HCV-HCC. Regardless of tumor stage, survival was significantly shorter (P = 0.017) in patients with NAFLD-HCC, 25.5 months (95% confidence interval 21.9-29.1), than in those with HCV-HCC, 33.7 months (95% confidence interval 31.9-35.4). To eliminate possible confounders, a propensity score analysis was performed, which showed no more significant difference between the two groups. Additionally, analysis of patients within Milan criteria submitted to curative treatments did not show any difference in survival between NAFLD-HCC and HCV-HCC (respectively, 38.6 versus 41.0 months, P = nonsignificant) Conclusions: NAFLD-HCC is more often detected at a later tumor stage and could arise also in the absence of cirrhosis, but after patient matching, it has a similar survival rate compared to HCV infection; a future challenge will be to identify patients with NAFLD who require more stringent surveillance in order to offer the most timely and effective treatment. (Hepatology 2016;63:827-838)openopenPiscaglia F.; Svegliati-Baroni G.; Barchetti A.; Pecorelli A.; Marinelli S.; Tiribelli C.; Bellentani S.; Bernardi M.; Biselli M.; Caraceni P.; Domenicali M.; Garuti F.; Gramenzi A.; Lenzi B.; Magalotti D.; Cescon M.; Ravaioli M.; Del Poggio P.; Olmi S.; Rapaccini G.L.; Balsamo C.; Di Nolfo M.A.; Vavassori E.; Alberti A.; Benvegnau L.; Gatta A.; Giacomin A.; Vanin V.; Pozzan C.; Maddalo G.; Giampalma E.; Cappelli A.; Golfieri R.; Mosconi C.; Renzulli M.; Roselli P.; Dell'isola S.; Ialungo A.M.; Risso D.; Marenco S.; Sammito G.; Bruzzone L.; Bosco G.; Grieco A.; Pompili M.; Rinninella E.; Siciliano M.; Chiaramonte M.; Guarino M.; Camma C.; Maida M.; Costantino A.; Barcellona M.R.; Schiada L.; Gemini S.; Lanzi A.; Stefanini G.F.; Dall'aglio A.C.; Cappa F.M.; Suzzi A.; Mussetto A.; Treossi O.; Missale G.; Porro E.; Mismas V.; Vivaldi C.; Bolondi L.; Zoli M.; Granito A.; Malagotti D.; Tovoli F.; Trevisani F.; Venerandi L.; Brandi G.; Cucchetti A.; Bugianesi E.; Vanni E.; Mezzabotta L.; Cabibbo G.; Petta S.; Fracanzani A.; Fargion S.; Marra F.; Fani B.; Biasini E.; Sacco R.; Morisco F.; Caporaso N.; Colombo M.; D'ambrosio R.; Croce L.S.; Patti R.; Giannini E.G.; Loria P.; Lonardo A.; Baldelli E.; Miele L.; Farinati F.; Borzio M.; Dionigi E.; Soardo G.; Caturelli E.; Ciccarese F.; Virdone R.; Affronti A.; Foschi F.G.; Borzio F.Piscaglia, F.; Svegliati-Baroni, G.; Barchetti, A.; Pecorelli, A.; Marinelli, S.; Tiribelli, C.; Bellentani, S.; Bernardi, M.; Biselli, M.; Caraceni, P.; Domenicali, M.; Garuti, F.; Gramenzi, A.; Lenzi, B.; Magalotti, D.; Cescon, M.; Ravaioli, M.; Del Poggio, P.; Olmi, S.; Rapaccini, G. L.; Balsamo, C.; Di Nolfo, M. A.; Vavassori, E.; Alberti, A.; Benvegnau, L.; Gatta, A.; Giacomin, A.; Vanin, V.; Pozzan, C.; Maddalo, G.; Giampalma, E.; Cappelli, A.; Golfieri, R.; Mosconi, C.; Renzulli, M.; Roselli, P.; Dell'Isola, S.; Ialungo, A. M.; Risso, D.; Marenco, S.; Sammito, G.; Bruzzone, L.; Bosco, G.; Grieco, A.; Pompili, M.; Rinninella, E.; Siciliano, M.; Chiaramonte, M.; Guarino, M.; Camma, C.; Maida, M.; Costantino, A.; Barcellona, M. R.; Schiada, L.; Gemini, S.; Lanzi, A.; Stefanini, G. F.; Dall'Aglio, A. C.; Cappa, F. M.; Suzzi, A.; Mussetto, A.; Treossi, O.; Missale, G.; Porro, E.; Mismas, V.; Vivaldi, C.; Bolondi, L.; Zoli, M.; Granito, A.; Malagotti, D.; Tovoli, F.; Trevisani, F.; Venerandi, L.; Brandi, G.; Cucchetti, A.; Bugianesi, E.; Vanni, E.; Mezzabotta, L.; Cabibbo, G.; Petta, S.; Fracanzani, A.; Fargion, S.; Marra, F.; Fani, B.; Biasini, E.; Sacco, R.; Morisco, F.; Caporaso, N.; Colombo, M.; D'Ambrosio, R.; Croce, L. S.; Patti, R.; Giannini, E. G.; Loria, P.; Lonardo, A.; Baldelli, E.; Miele, L.; Farinati, F.; Borzio, M.; Dionigi, E.; Soardo, G.; Caturelli, E.; Ciccarese, F.; Virdone, R.; Affronti, A.; Foschi, F. G.; Borzio, F

Identification of hemodynamically stable patients with acute pulmonary embolism at high risk for death: external validation of different models

Background: The optimal strategy for identification of hemodynamically stable patients with acute pulmonary embolism (PE) at risk for death and clinical deterioration remains undefined. Objectives: We aimed to assess the performances of currently available models/scores for identifying hemodynamically stable patients with acute, symptomatic PE at risk of death and clinical deterioration. Methods: This was a prospective multicenter cohort study including patients with acute PE (NCT03631810). Primary study outcome was in-hospital death within 30 days or clinical deterioration. Other outcomes were in-hospital death, death, and PE-related death, all at 30 days. We calculated positive and negative predictive values, c-statistics of European Society of Cardiology (ESC)-2014, ESC-2019, Pulmonary Embolism Thrombolysis (PEITHO), Bova, Thrombo-embolism lactate outcome study (TELOS), fatty acid binding protein, syncope and tachicardia (FAST), and National Early Warning Scale 2 (NEWS2) for the study outcomes. Results: In 5036 hemodynamically stable patients with acute PE, positive predictive values for the evaluated models/scores were all below 10%, except for TELOS and NEWS2; negative predictive values were above 98% for all the models/scores, except for FAST and NEWS2. ESC-2014 and TELOS had good performances for in-hospital death or clinical deterioration (c-statistic of 0.700 and 0.722, respectively), in-hospital death (c-statistic of 0.713 and 0.723, respectively), and PE-related death (c-statistic of 0.712 and 0.777, respectively); PEITHO, Bova, and NEWS2 also had good performances for PE-related death (c-statistic of 0.738, 0.741, and 0.742, respectively). Conclusion: In hemodynamically stable patients with acute PE, the accuracy for identification of hemodynamically stable patients at risk for death and clinical deterioration varies across the available models/scores; TELOS seems to have the best performance. These data can inform management studies and clinical practice

Transcriptional Regulation of PIK3CA Oncogene by NF-kappa B in Ovarian Cancer Microenvironment

Peer reviewe

Conjugated linoleic acid alters lipid metabolism in hepatocytes

not availabl

Figure 1: High Level View Example

The proposed language is based on two levels: one that allows to de¯ne, using the state machine, how the collaborative metaphor are organized (in a virtual environment a collaborative metaphor de¯ne how several objects, both avatars and objects, interact each other). Each collaborative metaphor is represented as a state of a state machine. It is possible to de¯ne the condition of input and output of each state and, in another detail level, it is possible to change the point of view describing how the single object participate to the metaphor. This representation, easy to understand from the user involved in the design, it is useful to provide a clear and complete idea of the collaborative metaphor of the e-learning environment to design (Figure 1).</p