Negative-Pressure Wound Therapy for Prevention of Sternal Wound Infection after Adult Cardiac Surgery : Systematic Review and Meta-Analysis

The results of current studies are not conclusive on the efficacy of incisional negative-pressure wound therapy (NPWT) for the prevention of sternal wound infection (SWI) after adult cardiac surgery. A systematic review of the literature was performed through PubMed, Scopus and Google to identify studies which investigated the efficacy of NPWT to prevent SWI after adult cardiac surgery. Available data were pooled using RevMan and Meta-analyst with random effect models. Out of 191 studies retrieved from the literature, ten fulfilled the inclusion criteria and were included in this analysis. The quality of these studies was judged fair for three of them and poor for seven studies. Only one study was powered to address the efficacy of NPWT for the prevention of postoperative SWI. Pooled analysis of these studies showed that NPWT was associated with lower risk of any SWI (4.5% vs. 9.0%, RR 0.54, 95% CI 0.34-0.84, I-2 48%), superficial SWI (3.8% vs. 4.4%, RR 0.63, 95% CI 0.29-1.36, I-2 65%), and deep SWI (1.8% vs. 4.7%, RR 0.46, 95% CI 0.26-0.74, I-2 0%), but such a difference was not statistically significant for superficial SWI. When only randomized and alternating allocated studies were included, NPWT was associated with a significantly lower risk of any SWI (3.3% vs. 16.5%, RR 0.22, 95% CI 0.08-0.62, I-2 0%), superficial SWI (2.6% vs. 12.4%, RR 0.21, 95% CI 0.06-0.69, I-2 0%), and deep SWI (1.2% vs. 4.8%, RR 0.17, 95% CI 0.03-0.95, I-2 0%). This pooled analysis showed that NPWT may prevent postoperative SWI after adult cardiac surgery. NPWT is expected to be particularly useful in patients at risk for surgical site infection and may significantly reduce the burden of resources needed to treat such a complication. However, the methodology of the available studies was judged as poor for most of them. Further studies are needed to obtain conclusive results on the potential benefits of this preventative strategy.Peer reviewe

Identification of asymptomatic Leishmania infection in patients undergoing kidney transplant using multiple tests

Objectives: In immunocompromised patients, asymptomatic Leishmania infection can reactivate, and evolve to severe disease. To date, no test is considered the gold standard for the identification of asymptomatic Leishmania infection. A combination of methods was employed to screen for Leishmania infection in patients undergoing kidney transplant (KT). Methods: We employed polymerase chain reaction for the detection of parasitic DNA in peripheral blood, Western blot to identify serum immunoglobulin G and whole blood assay to detect cytokines/chemokines after stimulation of whole blood with parasitic antigen. Results: One-hundred twenty patients residing in Italy were included in the study at the time of KT. Each patient that tested positive to at least one test was considered as Leishmania positive. Fifty out of 120 patients (42%) tested positive for one or more tests. The detection of specific cell-mediated response (32/111, 29%) was the most common marker of Leishmania infection, followed by a positive serology (24/120, 20%). Four patients (3%) harbored parasitic DNA in the blood. Conclusion: Our findings underline the high prevalence of asymptomatic Leishmania infection in patients undergoing KT in Italy, who are potentially at-risk for parasite reactivation and can benefit from an increased vigilance. Understanding the clinical relevance of these findings deserves further studies.This work was supported by the Italian Ministry of Health (grant number RF-2016-02361931)S

Hyperpolarized 13C-MRI of Tumor Metabolism Demonstrates Early Metabolic Response to Neoadjuvant Chemotherapy in Breast Cancer

Purpose: To compare hyperpolarized carbon-13 (13C)-MRI with dynamic contrast-enhanced MRI (DCE-MRI) for detecting early treatment response in breast cancer. Materials and Methods: In this institutional review board-approved prospective study, one woman with triple-negative breast cancer (age 49) underwent 13C-MRI following injection of hyperpolarized [1-13C]pyruvate and DCE-MRI at 3 T at baseline and after a single cycle of neoadjuvant therapy. The 13C-lactate/13C-pyruvate ratio derived from hyperpolarized 13C-MRI and the pharmacokinetic parameters Ktrans and kep derived from DCE-MRI were compared, before and after treatment. Results: Exchange of the 13C-label between injected hyperpolarized [1-13C]pyruvate and the endogenous lactate pool was demonstrated, catalyzed by the enzyme lactate dehydrogenase. After one cycle of neoadjuvant chemotherapy, a 34% reduction in the 13C-lactate/13C-pyruvate ratio was shown to correctly identify the patient as a responder to therapy, which was subsequently confirmed by a complete pathologic response. However, DCE-MRI showed an increase in the pharmacokinetic parameters Ktrans (132%) and kep (31%), which could be incorrectly interpreted as a poor response to treatment. Conclusion: Hyperpolarized 13C-MRI successfully identified response in breast cancer after a single cycle of neoadjuvant chemotherapy and may improve response prediction when used in conjunction with multiparametric proton MRI.This work was supported by a Wellcome Trust Strategic Award, Cancer Research UK (CRUK; Grants C8742/A18097, C19212/ A16628, C19212/A911376, and C197/A16465), the Austrian Science Fund (Grant J4025-B26), the CRUK Cambridge Centre, the CRUK & Engineering and Physical Sciences Research Council Cancer Imaging Centre in Cambridge and Manchester, the Mark Foundation for Cancer Research and Cancer Research UK Cambridge Centre (Grant C9685/A25177), CRUK National Cancer Imaging Translational Accelerator Award, Addenbrooke’s Charitable Trust, the National Institute for Health Research Cambridge Biomedical Research Centre, Cambridge Experimental Cancer Medicine Centre, and Cambridge University Hospitals National Health Service Foundation Trust

Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients

IntroductionArtificial Intelligence (AI) methods are being increasingly investigated as a means to generate predictive models applicable in the clinical practice. In this study, we developed a model to predict the efficacy of immunotherapy (IO) in patients with advanced non-small cell lung cancer (NSCLC) using eXplainable AI (XAI) Machine Learning (ML) methods. MethodsWe prospectively collected real-world data from patients with an advanced NSCLC condition receiving immune-checkpoint inhibitors (ICIs) either as a single agent or in combination with chemotherapy. With regards to six different outcomes - Disease Control Rate (DCR), Objective Response Rate (ORR), 6 and 24-month Overall Survival (OS6 and OS24), 3-months Progression-Free Survival (PFS3) and Time to Treatment Failure (TTF3) - we evaluated five different classification ML models: CatBoost (CB), Logistic Regression (LR), Neural Network (NN), Random Forest (RF) and Support Vector Machine (SVM). We used the Shapley Additive Explanation (SHAP) values to explain model predictions. ResultsOf 480 patients included in the study 407 received immunotherapy and 73 chemo- and immunotherapy. From all the ML models, CB performed the best for OS6 and TTF3, (accuracy 0.83 and 0.81, respectively). CB and LR reached accuracy of 0.75 and 0.73 for the outcome DCR. SHAP for CB demonstrated that the feature that strongly influences models' prediction for all three outcomes was Neutrophil to Lymphocyte Ratio (NLR). Performance Status (ECOG-PS) was an important feature for the outcomes OS6 and TTF3, while PD-L1, Line of IO and chemo-immunotherapy appeared to be more important in predicting DCR. ConclusionsIn this study we developed a ML algorithm based on real-world data, explained by SHAP techniques, and able to accurately predict the efficacy of immunotherapy in sets of NSCLC patients

APOLLO 11 Project, Consortium in Advanced Lung Cancer Patients Treated With Innovative Therapies: Integration of Real-World Data and Translational Research

Introduction: Despite several therapeutic efforts, lung cancer remains a highly lethal disease. Novel therapeutic approaches encompass immune-checkpoint inhibitors, targeted therapeutics and antibody-drug conjugates, with different results. Several studies have been aimed at identifying biomarkers able to predict benefit from these therapies and create a prediction model of response, despite this there is a lack of information to help clinicians in the choice of therapy for lung cancer patients with advanced disease. This is primarily due to the complexity of lung cancer biology, where a single or few biomarkers are not sufficient to provide enough predictive capability to explain biologic differences; other reasons include the paucity of data collected by single studies performed in heterogeneous unmatched cohorts and the methodology of analysis. In fact, classical statistical methods are unable to analyze and integrate the magnitude of information from multiple biological and clinical sources (eg, genomics, transcriptomics, and radiomics). Methods and objectives: APOLLO11 is an Italian multicentre, observational study involving patients with a diagnosis of advanced lung cancer (NSCLC and SCLC) treated with innovative therapies. Retrospective and prospective collection of multiomic data, such as tissue- (eg, for genomic, transcriptomic analysis) and blood-based biologic material (eg, ctDNA, PBMC), in addition to clinical and radiological data (eg, for radiomic analysis) will be collected. The overall aim of the project is to build a consortium integrating different datasets and a virtual biobank from participating Italian lung cancer centers. To face with the large amount of data provided, AI and ML techniques will be applied will be applied to manage this large dataset in an effort to build an R-Model, integrating retrospective and prospective population-based data. The ultimate goal is to create a tool able to help physicians and patients to make treatment decisions. Conclusion: APOLLO11 aims to propose a breakthrough approach in lung cancer research, replacing the old, monocentric viewpoint towards a multicomprehensive, multiomic, multicenter model. Multicenter cancer datasets incorporating common virtual biobank and new methodologic approaches including artificial intelligence, machine learning up to deep learning is the road to the future in oncology launched by this project

Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients

IntroductionArtificial Intelligence (AI) methods are being increasingly investigated as a means to generate predictive models applicable in the clinical practice. In this study, we developed a model to predict the efficacy of immunotherapy (IO) in patients with advanced non-small cell lung cancer (NSCLC) using eXplainable AI (XAI) Machine Learning (ML) methods.MethodsWe prospectively collected real-world data from patients with an advanced NSCLC condition receiving immune-checkpoint inhibitors (ICIs) either as a single agent or in combination with chemotherapy. With regards to six different outcomes - Disease Control Rate (DCR), Objective Response Rate (ORR), 6 and 24-month Overall Survival (OS6 and OS24), 3-months Progression-Free Survival (PFS3) and Time to Treatment Failure (TTF3) - we evaluated five different classification ML models: CatBoost (CB), Logistic Regression (LR), Neural Network (NN), Random Forest (RF) and Support Vector Machine (SVM). We used the Shapley Additive Explanation (SHAP) values to explain model predictions.ResultsOf 480 patients included in the study 407 received immunotherapy and 73 chemo- and immunotherapy. From all the ML models, CB performed the best for OS6 and TTF3, (accuracy 0.83 and 0.81, respectively). CB and LR reached accuracy of 0.75 and 0.73 for the outcome DCR. SHAP for CB demonstrated that the feature that strongly influences models’ prediction for all three outcomes was Neutrophil to Lymphocyte Ratio (NLR). Performance Status (ECOG-PS) was an important feature for the outcomes OS6 and TTF3, while PD-L1, Line of IO and chemo-immunotherapy appeared to be more important in predicting DCR.ConclusionsIn this study we developed a ML algorithm based on real-world data, explained by SHAP techniques, and able to accurately predict the efficacy of immunotherapy in sets of NSCLC patients