EPSILoN score: Validation cohort of a prognostic score in advanced non-small cell lung cancer (aNSCLC) patients treated with immunotherapy

n/

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

Transport Infrastructure Surveillance and Monitoring by Electromagnetic Sensing: The ISTIMES Project

The ISTIMES project, funded by the European Commission in the frame of a joint Call “ICT and Security” of the Seventh Framework Programme, is presented and preliminary research results are discussed. The main objective of the ISTIMES project is to design, assess and promote an Information and Communication Technologies (ICT)-based system, exploiting distributed and local sensors, for non-destructive electromagnetic monitoring of critical transport infrastructures. The integration of electromagnetic technologies with new ICT information and telecommunications systems enables remotely controlled monitoring and surveillance and real time data imaging of the critical transport infrastructures. The project exploits different non-invasive imaging technologies based on electromagnetic sensing (optic fiber sensors, Synthetic Aperture Radar satellite platform based, hyperspectral spectroscopy, Infrared thermography, Ground Penetrating Radar-, low-frequency geophysical techniques, Ground based systems for displacement monitoring). In this paper, we show the preliminary results arising from the GPR and infrared thermographic measurements carried out on the Musmeci bridge in Potenza, located in a highly seismic area of the Apennine chain (Southern Italy) and representing one of the test beds of the project

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

Transport Infrastructure Surveillance and Monitoring by Electromagnetic Sensing: The ISTIMES Project

The ISTIMES project, funded by the European Commission in the frame of a joint Call “ICT and Security” of the Seventh Framework Programme, is presented and preliminary research results are discussed. The main objective of the ISTIMES project is to design, assess and promote an Information and Communication Technologies (ICT)-based system, exploiting distributed and local sensors, for non-destructive electromagnetic monitoring of critical transport infrastructures. The integration of electromagnetic technologies with new ICT information and telecommunications systems enables remotely controlled monitoring and surveillance and real time data imaging of the critical transport infrastructures. The project exploits different non-invasive imaging technologies based on electromagnetic sensing (optic fiber sensors, Synthetic Aperture Radar satellite platform based, hyperspectral spectroscopy, Infrared thermography, Ground Penetrating Radar-, low-frequency geophysical techniques, Ground based systems for displacement monitoring). In this paper, we show the preliminary results arising from the GPR and infrared thermographic measurements carried out on the Musmeci bridge in Potenza, located in a highly seismic area of the Apennine chain (Southern Italy) and representing one of the test beds of the project

Regioselective Ring-Opening of Glycidol to Monoalkyl Glyceryl Ethers Promoted by an [OSSO]-FeIII Triflate Complex

A Fe-III-triflate complex, bearing a bis-thioether-di-phenolate [OSSO]-type ligand, was discovered to promote the ring-opening of glycidol with alcohols under mild reaction conditions (0.05 mol % catalyst and 80 degrees C). The reaction proceeded with high activity (initial turnover frequency of 1680 h(-1) for EtOH) and selectivity (>95 %) toward the formation of twelve monoalkyl glyceryl ethers (MAGEs) in a regioselective fashion (84-96 % yield of the non-symmetric regioisomer). This synthetic approach allows the conversion of a glycerol-derived platform molecule (i.e., glycidol) to high-value-added products by using an Earth-crust abundant metal-based catalyst

Sensitivity of trends to estimation methods and quantification of subsampling effects in global radiosounding temperature and humidity time series

Climate trends estimated using historicalradiosoundingtime series may be significantly affected by the choice of the regression method to use, as well as by a subsampling of the dataset often adopted in specific applications. These are contributions to the uncertainty of trend estimations, which have been quantified in literature, although on specific pairs of regression methods, and in the not very recent past characterized by smaller trends in temperature than those observed over the last two decades. This paper investigates the sensitivity of trend estimations to four linear regression methods (parametric and nonparametric) and to the artificial subsampling of the same dataset using historical radiosounding time series from 1978 onwards, available in the version 2 of the Integrated Global Radiosonde Archive (IGRA). Results show that long-term decadal trends may have not negligible uncertainties related to the choice of the regression method, the percentage of data available, the amount of missing data and the number of stations selected in the dataset. The choice of the regression methods increases uncertainties in the decadal trends ranging from -0.10 to -0.01 K.da(-1)for temperature in the lower stratosphere at 100 hPa and from 0.2 to 0.8% da(-1)for relative humidity (RH) in the middle troposphere at 300 hPa. Differences can also increase up to 0.4 K.da(-1)at 300 hPa when the amount of missing data exceeds 50% of the original dataset for temperature, while for RH, significant differences are observed in the lower troposphere at 925 hPa for almost all datasets. Finally, subsampling effects on trend estimation are quantified by artificially reducing the size of the IGRA dataset: Results show that subsampling effects on trend estimations when at least 60 stations, up to 76% of data available, are considered for temperature and at least 40 stations for RH

Toward More Sustainable Elastomers: Stereoselective Copolymerization of Linear Terpenes with Butadiene

The copolymerization of renewable monomers such as ocimene (O), myrcene (M), and farnesene (F) with butadiene (B), promoted by dichloro{1,4-dithiabutanediyl-2,2′-bis[4,6-bis(2-phenyl-2-propyl)phenoxy]}titanium (1) activated by modified methylalumoxane (m-MAO) under mild reaction conditions, was investigated. Copolymers in a wide range of compositions were obtained through a judicious control of the alimentation feed (up to 85% of terpene incorporated in the case of poly(ocimene-butadiene) (POB)). Analysis of POB, poly(myrcene-butadiene) (PMB), and poly(farnesene-butadiene) (PFB) microstructures revealed the good stereoselectivity of 1, both in the butadiene (up to 95%) and in the terpene (up to 92%, 71%, and 86% for O, M, and F, respectively) insertion. For all these new materials, a complete 13C NMR assignment was performed, revealing a multiblock structure. A sample of POB was also evaluated as a component in a model tread compound leading to improved mechanical properties with respect to the corresponding plain butadiene rubbers

A new combined wavelet methodology: implementation to GPR and ERT data obtained in the Montagnole experiment

International audienceGround penetrating radar (GPR) and electric resistivity tomography (ERT) are well assessed and accurate geophysical methods for the investigation of subsurface geological sections. In this paper, we present the joint exploitation of these methods at the Montagnole (French Alps) experimental site with the final aim to study and monitor effects of possible catastrophic rockslides in transport infrastructures. The overall goal of the joint GPR–ERT deployment considered here is the careful monitoring of the subsurface structure before and after a series of high energetic mechanical impacts at ground level. It is known that factors such as the ambiguity of geophysical field examination, the complexity of geological scenarios and the low signal-to-noise ratio affect the possibility of building reliable physical–geological models of subsurface structure. Here, we applied to the GPR and ERT methods at the Montagnole site, recent advances in wavelet theory and data mining. The wavelet approach was specifically used to obtain enhanced images (e.g. coherence portraits) resulting from the integration of the different geophysical fields. This methodology, based on the matching pursuit combined with wavelet packet dictionaries, permitted us to extract desired signals under different physical–geological conditions, even in the presence of strongly noised data. Tools such as complex wavelets employed for the coherence portraits, and combined GPR–ERT coherency orientation angle, to name a few, enable non-conventional operations of integration and correlation in subsurface geophysics to be performed. The estimation of the above-mentioned parameters proved useful not only for location of buried inhomogeneities but also for a rough estimation of their electromagnetic and related properties. Therefore, the combination of the above approaches has allowed us to set up a novel methodology, which may enhance the reliability and confidence of each separate geophysical method and their integration