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

Wound monitoring: towards an intelligent platform

Wound repair is one of the most complex biological processes that occur during a human lifetime. Apart from negatively impacting patient well-being and jeopardizing human life, wounds represent a great burden to the healthcare costs both in developed and developing countries and even more in remote areas and rural communities. Interestingly, those latter regions share commonalities – remoteness, communications’ lags - with the conditions experienced in Space that could be then used as a testing environment for developing effective solutions on Earth. Reliable and wearable monitoring systems could help in reducing these problems. However, despite the significant progress on this topic, critical limitations exist, and the domain is still lacking a firm and complete understanding of the complex network of the involved biophysical phenomena. So far, most of the studies are focusing on the development of wearable sensing device, with limited information on real values and without a clear long-term vision. With the emerging field of Health 4.0, the paradigm is shifted, and the effective use of the wound data should become the primary focus. This study represents a contribution in tackling the conceptual gaps for developing advanced and effective wound monitoring systems. To do so, this thesis consists of two parts dedicated to an innovative configuration to host a wound temperature sensor and a new concept for using actual wound data to foster diagnostics prediction, respectively. In the first part of this thesis a multilayer temperature sensing concept is proposed, consisting of a hydrogel layer acting as a transfer layer for the biophysical signals coming from the wounds and a liquid crystal temperature sensing layer, the temperature being one of the most important biomarkers to determine the wound status. For the hydrogel layer, synthetic and double network hydrogels incorporated with graphene oxide were explored. Hydrogels have been tested on the ground and in hypergravity and microgravity conditions during a parabolic flight. Cholesteric liquid crystals represent the backbone of the first sensing concept that has been developed to create temperature maps of the wound area. This concept targets an easy-to-read, wearable, colorimetric system, having resolution ( −1.2°C burn will heal in less than 21 days. Results from this study suggest the use of temperature as a reliable wound monitoring marker. Moreover, a real-hospital data was used to train a deep learning algorithm that successfully performed wound image segmentation, which presents a first step in developing a fully automated diagnostic system.Doctorat en Sciences de l'ingénieur et technologieinfo:eu-repo/semantics/nonPublishe

Pervasive Personal Healthcare Service Designed as Mobile Social Network

A global phenomenon of population ageing and an increasing number of patients with chronic diseases place substantial additional pressure on healthcare systems. A possible solution for this problem is a new emerging sort of pervasive personal healthcare service that is focused on the patient and allows the patient to be actively involved in his or her own health care. In this paper, we propose the architecture of the pervasive personal healthcare service which is based on the existing technologies available to almost everyone. Along with the conventional request-response synchronous communication, the proposed system features asynchronous communication based on publish-subscribe-notify model. In order to perform asynchronous communication, a web application server is integrated with the Google Cloud Messaging service. The communication between mobile device and servers is carried out through the available Wi-Fi or mobile networks, whereas Bluetooth protocol is conventional for Body Sensor Networks consisting of wearable sensor devices. We also present a mobile application which has been developed with use-case driven approach for both patients and medical personnel. The introduced application has a form of a nonintrusive customized mobile social network. We explain usage scenarios that clarify the required functions and present conclusions based on the system test

Self-assembly of carbon nanotube-based composites by means of evaporation-assisted depositions: importance of drop-by-drop self-assembly on material properties

Carbon nanotubes and silica nanoparticles are allowed to self-assemble into a nanocomposite by first forming an aqueous suspension, then depositing one drop after the other and finally letting them evaporate. Two types of composites are prepared. One by forming alternate layers and the other by forming several layers of a pre-mixed suspension. The thickness, thermal and electrical conductivity of the composites are measured versus the number of depositions. The pre-mixed composites showed an increase in the values in both the parallel and perpendicular directions of both the electrical and thermal conductivities, making them suitable for electrodes or battery-like applications. The values of the electrical and thermal conductivities in the perpendicular direction for the first composite decrease and increase, respectively, while for the parallel direction the values are significantly constant. As such, they would be useful as electrical insulators for optimal cooling. Thickness measurements showed that the pre-mixed composite is the denser one, due to a better alignment of the carbon nanotubes.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Printability of Double Network Alginate-Based Hydrogel for 3D Bio-Printed Complex Structures

Three-dimensional (3D) bio-printing has recently emerged as a crucial technology in tissue engineering, yet there are still challenges in selecting materials to obtain good print quality. Therefore, it is essential to study the influence of the chosen material (i.e. bio-ink) and the printing parameters on the final result. The “printability” of a bio-ink indicates its suitability for bio-printing. Hydrogels are a great choice because of their biocompatibility, but their printability is crucial for exploiting their properties and ensuring high printing accuracy. However, the printing settings are seldom addressed when printing hydrogels. In this context, this study explored the printability of double network (DN) hydrogels, from printing lines (1D structures) to lattices (2D structures) and 3D tubular structures, with a focus on printing accuracy. The DN hydrogel has two entangled cross-linked networks and a balanced mechanical performance combining high strength, toughness, and biocompatibility. The combination of poly (ethylene glycol)-diacrylate (PEDGA) and sodium alginate (SA) enables the qualities mentioned earlier to be met, as well as the use of UV to prevent filament collapse under gravity. Critical correlations between the printability and settings, such as velocity and viscosity of the ink, were identified. PEGDA/alginate-based double network hydrogels were explored and prepared, and printing conditions were improved to achieve 3D complex architectures, such as tubular structures. The DN solution ink was found to be unsuitable for extrudability; hence, glycerol was added to enhance the process. Different glycerol concentrations and flow rates were investigated. The solution containing 25% glycerol and a flow rate of 2 mm/s yielded the best printing accuracy. Thanks to these parameters, a line width of 1 mm and an angle printing inaccuracy of less than 1° were achieved, indicating good shape accuracy. Once the optimal parameters were identified, a tubular structure was achieved with a high printing accuracy. This study demonstrated a 3D printing hydrogel structure using a commercial 3D bio-printer (REGEMAT 3D BIO V1) by synchronizing all parameters, serving as a reference for future more complex 3D structures.info:eu-repo/semantics/publishe

A Deep Learning Image-to-Image Translation Approach for a More Accessible Estimator of the Healing Time of Burns

Objective: An accurate and timely diagnosis of burn severity is critical to ensure a positive outcome. Laser Doppler imaging (LDI) has become a very useful tool for this task. It measures the perfusion of the burn and estimates its potential healing time. LDIs generate a 6-color palette image, with each color representing a healing time. This technique has very high costs associated. In resource-limited areas, such as low- and middle-income countries or remote locations like space, where access to specialized burn care is inadequate, more affordable and portable tools are required. This study proposes a novel image-to-image translation approach to estimate burn healing times, using a digital image to approximate the LDI. Methods: This approach consists of a U-net architecture with a VGG-based encoder and applies the concept of ordinal classification. Paired digital and LDI images of burns were collected. The performance was evaluated with 10-fold cross-validation, mean absolute error (MAE), and color distribution differences between the ground truth and the estimated LDI. Results: Results showed a satisfactory performance in terms of low MAE (0.2370 +/- 0.0086). However, the unbalanced distribution of colors in the data affects this performance. Significance: This novel and unique approach serves as a basis for developing more accessible support tools in the burn care environment in resource-limited areas

MetaLung: Towards a Secure Architecture for Lung Cancer Patient Care on the Metaverse

The interest in metaverse applications by existing industries has seen massive growth thanks to the accelerated pace of research in key technological fields and the shift towards virtual interactions fueled by the Covid-19 pandemic. One key industry that can benefit from the integration into the metaverse is healthcare. The potential to provide enhanced care for patients affected by multiple health issues, from standard afflictions to more specialized pathologies, is being explored through the fabrication of architectures that can support metaverse applications. In this paper, we focus on the persistent issues of lung cancer detection, monitoring, and treatment, to propose MetaLung, a privacy and integrity-preserving architecture on the metaverse. We discuss the use cases to enable remote patient-doctor interactions, patient constant monitoring, and remote care. By leveraging technologies such as digital twins, edge computing, explainable AI, IoT, and virtual/augmented reality, we propose how the system could provide better assistance to lung cancer patients and suggest individualized treatment plans to the doctors based on their information. In addition, we describe the current implementation state of the AI-based Decision Support System for treatment selection, I3LUNG, and the current state of patient data collection