The impact of decision tools during oncological consultation with lung cancer patients: A systematic review within the I3LUNG project.

INTRODUCTION: To date, lung cancer is one of the most lethal diagnoses worldwide. A variety of lung cancer treatments and modalities are available, which are generally presented during the patient and doctor consultation. The implementation of decision tools to facilitate patient's decision-making and the management of their healthcare process during medical consultation is fundamental. Studies have demonstrated that decision tools are helpful to promote health management and decision-making of lung cancer patients during consultations. The main aim of the present work within the I3LUNG project is to systematically review the implementation of decision tools to facilitate medical consultation about oncological treatments for lung cancer patients. METHODS: In the present study, we conducted a systematic review following the PRISMA guidelines. We used an electronic computer-based search involving three databases, as follows: Embase, PubMed, and Scopus. 10 articles met the inclusion criteria and were included. They explicitly refer to decision tools in the oncological context, with lung cancer patients. RESULTS: The discussion highlights the most encouraging results about the positive role of decision aids during medical consultations about oncological treatments, especially regarding anxiety, decision-making, and patient knowledge. However, no one main decision aid tool emerged as essential. Opting for a more recent timeframe to select eligible articles might shed light on the current array of decision aid tools available. CONCLUSION: Future review efforts could utilize alternative search strategies to explore other lung cancer-specific outcomes during medical consultations for treatment decisions and the implementation of decision aid tools. Engaging with experts in the fields of oncology, patient decision-making, or health communication could provide valuable insights and recommendations for relevant literature or research directions that may not be readily accessible through traditional search methods. The development of guidelines for future research were provided with the aim to promote decision aids focused on patients' needs.

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.

PEOPLE (NTC03447678), a phase II trial to test pembrolizumab as first-line treatment in patients with advanced NSCLC with PD-L1 <50%: a multiomics analysis.

BACKGROUND: Chemoimmunotherapy represents the standard of care for patients with advanced non-small cell lung cancer (NSCLC) and programmed death-ligand 1 (PD-L1) <50%. Although single-agent pembrolizumab has also demonstrated some activity in this setting, no reliable biomarkers yet exist for selecting patients likely to respond to single-agent immunotherapy. The main purpose of the study was to identify potential new biomarkers associated with progression-free-survival (PFS) within a multiomics analysis. METHODS: PEOPLE (NTC03447678) was a prospective phase II trial evaluating first-line pembrolizumab in patients with advanced EGFR and ALK wild type treatment-naïve NSCLC with PD-L1 <50%. Circulating immune profiling was performed by determination of absolute cell counts with multiparametric flow cytometry on freshly isolated whole blood samples at baseline and at first radiological evaluation. Gene expression profiling was performed using nCounter PanCancer IO 360 Panel (NanoString) on baseline tissue. Gut bacterial taxonomic abundance was obtained by shotgun metagenomic sequencing of stool samples at baseline. Omics data were analyzed with sequential univariate Cox proportional hazards regression predicting PFS, with Benjamini-Hochberg multiple comparisons correction. Biological features significant with univariate analysis were analyzed with multivariate least absolute shrinkage and selection operator (LASSO). RESULTS: From May 2018 to October 2020, 65 patients were enrolled. Median follow-up and PFS were 26.4 and 2.9 months, respectively. LASSO integration analysis, with an optimal lambda of 0.28, showed that peripheral blood natural killer cells/CD56dimCD16+ (HR 0.56, 0.41-0.76, p=0.006) abundance at baseline and non-classical CD14dimCD16+monocytes (HR 0.52, 0.36-0.75, p=0.004), eosinophils (CD15+CD16-) (HR 0.62, 0.44-0.89, p=0.03) and lymphocytes (HR 0.32, 0.19-0.56, p=0.001) after first radiologic evaluation correlated with favorable PFS as well as high baseline expression levels of CD244 (HR 0.74, 0.62-0.87, p=0.05) protein tyrosine phosphatase receptor type C (HR 0.55, 0.38-0.81, p=0.098) and killer cell lectin like receptor B1 (HR 0.76, 0.66-0.89, p=0.05). Interferon-responsive factor 9 and cartilage oligomeric matrix protein genes correlated with unfavorable PFS (HR 3.03, 1.52-6.02, p 0.08 and HR 1.22, 1.08-1.37, p=0.06, corrected). No microbiome features were selected. CONCLUSIONS: This multiomics approach was able to identify immune cell subsets and expression levels of genes associated to PFS in patients with PD-L1 <50% NSCLC treated with first-line pembrolizumab. These preliminary data will be confirmed in the larger multicentric international I3LUNG trial (NCT05537922). TRIAL REGISTRATION NUMBER: 2017-002841-31.

IWDA: Importance Weighting for Drift Adaptation in Streaming Supervised Learning Problems.

Distribution drift is an important issue for practical applications of machine learning (ML). In particular, in streaming ML, the data distribution may change over time, yielding the problem of concept drift, which affects the performance of learners trained with outdated data. In this article, we focus on supervised problems in an online nonstationary setting, introducing a novel learner-agnostic algorithm for drift adaptation, namely importance weighting for drift adaptation (IWDA), with the goal of performing efficient retraining of the learner when drift is detected. IWDA incrementally estimates the joint probability density of input and target for the incoming data and, as soon as drift is detected, retrains the learner using importance-weighted empirical risk minimization. The importance weights are computed for all the samples observed so far, employing the estimated densities, thus, using all available information efficiently. After presenting our approach, we provide a theoretical analysis in the abrupt drift setting. Finally, we present numerical simulations that illustrate how IWDA competes and often outperforms state-of-the-art stream learning techniques, including adaptive ensemble methods, on both synthetic and real-world data benchmarks.

The EU-funded I3LUNG Project: Integrative Science, Intelligent Data Platform for Individualized LUNG Cancer Care With Immunotherapy.

Although immunotherapy (IO) has changed the paradigm for the treatment of patients with advanced non-small cell lung cancers (aNSCLC), only around 30% to 50% of treated patients experience a long-term benefit from IO. Furthermore, the identification of the 30 to 50% of patients who respond remains a major challenge, as programmed Death-Ligand 1 (PD-L1) is currently the only biomarker used to predict the outcome of IO in NSCLC patients despite its limited efficacy. Considering the dynamic complexity of the immune system-tumor microenvironment (TME) and its interaction with the host's and patient's behavior, it is unlikely that a single biomarker will accurately predict a patient's outcomes. In this scenario, Artificial Intelligence (AI) and Machine Learning (ML) are becoming essential to the development of powerful decision-making tools that are able to deal with this high-complexity and provide individualized predictions to better match treatments to individual patients and thus improve patient outcomes and reduce the economic burden of aNSCLC on healthcare systems. I3LUNG is an international, multicenter, retrospective and prospective, observational study of patients with aNSCLC treated with IO, entirely funded by European Union (EU) under the Horizon 2020 (H2020) program. Using AI-based tools, the aim of this study is to promote individualized treatment in aNSCLC, with the goals of improving survival and quality of life, minimizing or preventing undue toxicity and promoting efficient resource allocation. The final objective of the project is the construction of a novel, integrated, AI-assisted data storage and elaboration platform to guide IO administration in aNSCLC, ensuring easy access and cost-effective use by healthcare providers and patients.

Machine Learning Using Real-World and Translational Data to Improve Treatment Selection for NSCLC Patients Treated with Immunotherapy.

(1) Background: In advanced non-small cell lung cancer (aNSCLC), programmed death ligand 1 (PD-L1) remains the only biomarker for candidate patients to immunotherapy (IO). This study aimed at using artificial intelligence (AI) and machine learning (ML) tools to improve response and efficacy predictions in aNSCLC patients treated with IO. (2) Methods: Real world data and the blood microRNA signature classifier (MSC) were used. Patients were divided into responders (R) and non-responders (NR) to determine if the overall survival of the patients was likely to be shorter or longer than 24 months from baseline IO. (3) Results: One-hundred sixty-four out of 200 patients (i.e., only those ones with PD-L1 data available) were considered in the model, 73 (44.5%) were R and 91 (55.5%) NR. Overall, the best model was the linear regression (RL) and included 5 features. The model predicting R/NR of patients achieved accuracy ACC = 0.756, F1 score F1 = 0.722, and area under the ROC curve AUC = 0.82. LR was also the best-performing model in predicting patients with long survival (24 months OS), achieving ACC = 0.839, F1 = 0.908, and AUC = 0.87. (4) Conclusions: The results suggest that the integration of multifactorial data provided by ML techniques is a useful tool to select NSCLC patients as candidates for IO.

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

Introduction: Artificial 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. Methods: We 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. Results: Of 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. Conclusions: In 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.

Integrating clinical and biological prognostic biomarkers in patients with advanced NSCLC treated with immunotherapy: the DEMo score system.

BACKGROUND: Several biomarkers have been separately described to select patients for immunotherapy (IO), but few studies integrate these markers. Di Maio, EPSILoN and the plasma microRNA signature classifier (MSC), are three different clinico, biochemical and molecular markers able to independently predict prognosis in non-small cell lung cancer (NSCLC). METHODS: Complete data such as sex, histology, ECOG-PS, stage, smoking status, presence of liver metastasis, LDH and neutrophils-to-lymphocyte ratio were collected to generate Di Maio and EPSILoN. The MSC risk level was prospectively assessed in plasma samples collected at baseline IO. The 3 markers were integrated into the DEMo score system prospectively tested in a cohort of 200 advanced NSCLC patients treated with IO. Endpoints were overall survival (OS), progression-free survival (PFS) and overall response rate (ORR). RESULTS: DEMo separated patients in 7-risk groups whose median OS had a trend ranging from 29.7 to 1.5 months (P<0.0001). When comparing patients with the lowest (n=29) and the highest (n=35) DEMo scores ORR was 45% and 3%, respectively (P<0.0001). Considering the 53 PD-L1 ≥50% patients, DEMo identified a group of 13 (25%) patients who benefit less from IO in terms of both OS (HR: 8.81; 95% CI: 2.87-20.01) and PFS (HR: 6.82; 95% CI: 2.57-18.10). Twelve out of 111 (11%) patients who most benefit from IO according to OS (HR: 0.21; 95% CI: 0.07-0.62) and PFS (HR: 0.28; 95% CI: 0.12-0.65) were identified by DEMo in the PD-L1 <50% group. CONCLUSIONS: The DEMo prognostic score system stratified NSCLC patients treated with IO better than each single marker. The proper use of DEMo according to PD-L1 could improve selection in IO regimens.

An Ensemble Approach for Cognitive Fault Detection and Isolation in Sensor Networks.

Cognitive fault detection and diagnosis systems are systems able to provide timely information about possibly occurring faults without requiring any a priori knowledge about the process generating the data or the possible faults. This ability is crucial in sensor network scenarios where a priori information about the data generating process, the noise level or the dictionary of the possibly occurring faults is generally hard to obtain. We here present a novel cognitive fault detection and isolation system for sensor networks. The proposed solution relies on the modeling of spatial and temporal relationships present in the acquired datastreams and an ensemble of Hidden Markov Model change-detection tests working in the space of estimated parameters for fault detection and isolation purposes. The effectiveness of the proposed solution has been evaluated on both synthetically generated and real datasets.