Single-dose palonosetron for prevention of chemotherapy-induced nausea and vomiting in patients with aggressive non-Hodgkin's lymphoma receiving moderately emetogenic chemotherapy containing steroids: results of a phase II study from the Gruppo Italiano per lo Studio dei Linfomi (GISL)

PURPOSE: The control of nausea and vomiting induced by chemotherapy is paramount for overall treatment success in cancer patients. Antiemetic therapy during chemotherapy in lymphoma patients generally consists of anti-serotoninergic drugs and dexamethasone. The aim of this trial was to evaluate the efficacy of a single dose of palonosetron, a second-generation serotonin type 3 (5-HT(3)) receptor antagonist, in patients with aggressive non-Hodgkin's lymphoma receiving moderately emetogenic chemotherapy (MEC) containing steroids. METHODS: Patients received a single intravenous bolus of palonosetron (0.25 mg) before administration of chemotherapy. Complete response (CR) defined as no vomiting and no rescue therapy during overall phase (0-120 h) was the primary endpoint. Complete control (CC) defined as CR and only mild nausea was a secondary endpoint. RESULTS: Eighty-six evaluable patients entered in the study. A CR was observed in 74 patients (86.0%) during the overall phase; the CR during the acute (0-24 h) and delayed (24-120 h) phases was 90.7% and 88.4%, respectively. CC was 89.5% during the acute and 84.9% during the delayed phase; the overall CC was 82.6%. CONCLUSIONS: This was the first trial, which demonstrated the efficacy of a single dose of palonosetron in control CINV in patients with aggressive non-Hodgkin's lymphoma receiving MEC regimen containing steroids

A Clinical Prognostic Model Based on Machine Learning from the Fondazione Italiana Linfomi (FIL) MCL0208 Phase III Trial

BACKGROUND Multicenter clinical trials are producing growing amounts of clinical data. Machine Learning (ML) might facilitate the discovery of novel tools for prognostication and disease-stratification. Taking advantage of a systematic collection of multiple variables, we developed a model derived from data collected on 300 patients with mantle cell lymphoma (MCL) from the Fondazione Italiana Linfomi-MCL0208 phase III trial (NCT02354313). METHODS We developed a score with a clustering algorithm applied to clinical variables. The candidate score was correlated to overall survival (OS) and validated in two independent data series from the European MCL Network (NCT00209222, NCT00209209); Results: Three groups of patients were significantly discriminated: Low, Intermediate (Int), and High risk (High). Seven discriminants were identified by a feature reduction approach: albumin, Ki-67, lactate dehydrogenase, lymphocytes, platelets, bone marrow infiltration, and B-symptoms. Accordingly, patients in the Int and High groups had shorter OS rates than those in the Low and Int groups, respectively (Int→Low, HR: 3.1, 95% CI: 1.0-9.6; High→Int, HR: 2.3, 95% CI: 1.5-4.7). Based on the 7 markers, we defined the engineered MCL international prognostic index (eMIPI), which was validated and confirmed in two independent cohorts; Conclusions: We developed and validated a ML-based prognostic model for MCL. Even when currently limited to baseline predictors, our approach has high scalability potential

A Clinical Prognostic Model Based on Machine Learning from the Fondazione Italiana Linfomi (FIL) MCL0208 Phase III Trial

SIMPLE SUMMARY: The interest in using Machine-Learning (ML) techniques in clinical research is growing. We applied ML to build up a novel prognostic model from patients affected with Mantle Cell Lymphoma (MCL) enrolled in a phase III open-labeled, randomized clinical trial from the Fondazione Italiana Linfomi (FIL)—MCL0208. This is the first application of ML in a prospective clinical trial on MCL lymphoma. We applied a novel ML pipeline to a large cohort of patients for which several clinical variables have been collected at baseline, and assessed their prognostic value based on overall survival. We validated it on two independent data series provided by European MCL Network. Due to its flexibility, we believe that ML would be of tremendous help in the development of a novel MCL prognostic score aimed at re-defining risk stratification. ABSTRACT: Background: Multicenter clinical trials are producing growing amounts of clinical data. Machine Learning (ML) might facilitate the discovery of novel tools for prognostication and disease-stratification. Taking advantage of a systematic collection of multiple variables, we developed a model derived from data collected on 300 patients with mantle cell lymphoma (MCL) from the Fondazione Italiana Linfomi-MCL0208 phase III trial (NCT02354313). Methods: We developed a score with a clustering algorithm applied to clinical variables. The candidate score was correlated to overall survival (OS) and validated in two independent data series from the European MCL Network (NCT00209222, NCT00209209); Results: Three groups of patients were significantly discriminated: Low, Intermediate (Int), and High risk (High). Seven discriminants were identified by a feature reduction approach: albumin, Ki-67, lactate dehydrogenase, lymphocytes, platelets, bone marrow infiltration, and B-symptoms. Accordingly, patients in the Int and High groups had shorter OS rates than those in the Low and Int groups, respectively (Int→Low, HR: 3.1, 95% CI: 1.0–9.6; High→Int, HR: 2.3, 95% CI: 1.5–4.7). Based on the 7 markers, we defined the engineered MCL international prognostic index (eMIPI), which was validated and confirmed in two independent cohorts; Conclusions: We developed and validated a ML-based prognostic model for MCL. Even when currently limited to baseline predictors, our approach has high scalability potential

MATRix-RICE therapy and autologous haematopoietic stem-cell transplantation in diffuse large B-cell lymphoma with secondary CNS involvement (MARIETTA): an international, single-arm, phase 2 trial.

BACKGROUND Secondary CNS lymphoma is a rare but potentially lethal event in patients with diffuse large B-cell lymphoma. We aimed to assess the activity and safety of an intensive, CNS-directed chemoimmunotherapy consolidated by autologous haematopoietic stem-cell transplantation (HSCT) in patients with secondary CNS lymphoma. METHODS This international, single-arm, phase 2 trial was done in 24 hospitals in Italy, the UK, the Netherlands, and Switzerland. Adults (aged 18-70 years) with histologically diagnosed diffuse large B-cell lymphoma and CNS involvement at the time of primary diagnosis or at relapse and Eastern Cooperative Oncology Group Performance Status of 3 or less were enrolled and received three courses of MATRix (rituximab 375 mg/m2, intravenous infusion, day 0; methotrexate 3·5 g/m2, the first 0·5 g/m2 in 15 min followed by 3 g/m2 in a 3 h intravenous infusion, day 1; cytarabine 2 g/m2 every 12 h, in 1 h intravenous infusions, days 2 and 3; thiotepa 30 mg/m2, 30 min intravenous infusion, day 4) followed by three courses of RICE (rituximab 375 mg/m2, day 1; etoposide 100 mg/m2 per day in 500-1000 mL over a 60 min intravenous infusion, days 1, 2, and 3; ifosfamide 5 g/m2 in 1000 mL in a 24 h intravenous infusion with mesna support, day 2; carboplatin area under the curve of 5 in 500 mL in a 1 h intravenous infusion, day 2) and carmustine-thiotepa and autologous HSCT (carmustine 400 mg/m2 in 500 mL glucose 5% solution in a 1-2 h infusion, day -6; thiotepa 5 mg/kg in saline solution in a 2 h infusion every 12 h, days -5 and -4). The primary endpoint was progression-free survival at 1 year. Overall and complete response rates before autologous HSCT, duration of response, overall survival, and safety were the secondary endpoints. Analyses were in the modified intention-to-treat population. This study is registered with ClinicalTrials.gov, NCT02329080. The trial ended after accrual completion; the database lock was Dec 31, 2019. FINDINGS Between March 30, 2015, and Aug 3, 2018, 79 patients were enrolled. 75 patients were assessable. 319 (71%) of the 450 planned courses were delivered. At 1 year from enrolment the primary endpoint was met, 42 patients were progression free (progression-free survival 58%; 95% CI 55-61). 49 patients (65%; 95% CI 54-76) had an objective response after MATRix-RICE, 29 (39%) of whom had a complete response. 37 patients who responded had autologous HSCT. At the end of the programme, 46 patients (61%; 95% CI 51-71) had an objective response, with a median duration of objective response of 26 months (IQR 16-37). At a median follow-up of 29 months (IQR 20-40), 35 patients were progression-free and 33 were alive, with a 2-year overall survival of 46% (95% CI 39-53). Grade 3-4 toxicity was most commonly haematological: neutropenia in 46 (61%) of 75 patients, thrombocytopenia in 45 (60%), and anaemia in 26 (35%). 79 serious adverse events were recorded in 42 (56%) patients; four (5%) of those 79 were lethal due to sepsis caused by Gram-negative bacteria (treatment-related mortality 5%; 95% CI 0·07-9·93). INTERPRETATION MATRix-RICE plus autologous HSCT was active in this population of patients with very poor prognosis, and had an acceptable toxicity profile. FUNDING Stand Up To Cancer Campaign for Cancer Research UK, the Swiss Cancer Research foundation, and the Swiss Cancer League

Use of hydroxychloroquine in hospitalised COVID-19 patients is associated with reduced mortality: Findings from the observational multicentre Italian CORIST study

Background: Hydroxychloroquine (HCQ) was proposed as potential treatment for COVID-19. Objective: We set-up a multicenter Italian collaboration to investigate the relationship between HCQ therapy and COVID-19 in-hospital mortality. Methods: In a retrospective observational study, 3,451 unselected patients hospitalized in 33 clinical centers in Italy, from February 19, 2020 to May 23, 2020, with laboratory-confirmed SARS-CoV-2 infection, were analyzed. The primary end-point in a time-to event analysis was in-hospital death, comparing patients who received HCQ with patients who did not. We used multivariable Cox proportional-hazards regression models with inverse probability for treatment weighting by propensity scores, with the addition of subgroup analyses. Results: Out of 3,451 COVID-19 patients, 76.3% received HCQ. Death rates (per 1,000 person-days) for patients receiving or not HCQ were 8.9 and 15.7, respectively. After adjustment for propensity scores, we found 30% lower risk of death in patients receiving HCQ (HR=0.70; 95%CI: 0.59 to 0.84; E-value=1.67). Secondary analyses yielded similar results. The inverse association of HCQ with inpatient mortality was particularly evident in patients having elevated C-reactive protein at entry. Conclusions: HCQ use was associated with a 30% lower risk of death in COVID-19 hospitalized patients. Within the limits of an observational study and awaiting results from randomized controlled trials, these data do not discourage the use of HCQ in inpatients with COVID-19

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

DMTs and Covid-19 severity in MS: a pooled analysis from Italy and France

We evaluated the effect of DMTs on Covid-19 severity in patients with MS, with a pooled-analysis of two large cohorts from Italy and France. The association of baseline characteristics and DMTs with Covid-19 severity was assessed by multivariate ordinal-logistic models and pooled by a fixed-effect meta-analysis. 1066 patients with MS from Italy and 721 from France were included. In the multivariate model, anti-CD20 therapies were significantly associated (OR = 2.05, 95%CI = 1.39–3.02, p < 0.001) with Covid-19 severity, whereas interferon indicated a decreased risk (OR = 0.42, 95%CI = 0.18–0.99, p = 0.047). This pooled-analysis confirms an increased risk of severe Covid-19 in patients on anti-CD20 therapies and supports the protective role of interferon

EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020

Welcome to EVALITA 2020! EVALITA is the evaluation campaign of Natural Language Processing and Speech Tools for Italian. EVALITA is an initiative of the Italian Association for Computational Linguistics (AILC, http://www.ai-lc.it) and it is endorsed by the Italian Association for Artificial Intelligence (AIxIA, http://www.aixia.it) and the Italian Association for Speech Sciences (AISV, http://www.aisv.it)