We are pleased to announce that the study design of the I3LUNG project has just been published in the journal Clinical Lung Cancer.

The publication explains the I3LUNG study design and marks the beginning of a series of publications that will dig into the project’s groundbreaking work. Subsequent articles will detail the specific areas within the I3LUNG consortium and provide updates on results related to this comprehensive project’s different aspects.

The I3LUNG initiative is led by Dr. Arsela Prelaj, Medical Oncologist at Istituto Nazionale dei Tumori of Milan, and is a collaborative effort involving 16 partners located worldwide. Funded under the Horizon Europe Program framework, the project is focused on “Ensuring access to innovative, sustainable, and high-quality healthcare.” It will cover a 5-year timeframe, from 1st June 2022 to May 2027.

Due to its poor prognosis and limited therapeutic options, there is a large unmet need for patients with metastatic non-small cell lung cancer (mNSCLC). I3LUNG aims to develop a platform that utilizes artificial intelligence and machine learning tools to predict an mNSCLC patient’s response to immunotherapy according to their clinical characteristics. The platform will combine data collected from the medical records of patients who are candidates for immunotherapy. It will then study each individual’s response to treatment while leveraging the expertise of the different partners, ranging from imaging to omics sciences.

The I3LUNG project represents a major stride towards personalized medicine and, therefore, the integrated management of mNSCLC patients. While immunotherapy can harness the power of the immune system to fight cancer, it is a costly treatment that, unfortunately, may not benefit all patients especially considering the frequency of adverse events registered. This project aims to better assist mNSCLC patients and physicians in selecting the right treatment by developing an algorithm capable of predicting the patient’s response to immunotherapy and integrating those medical considerations as well as the individual’s treatment preferences. More efficient treatment of mNSCLC patients will enhance their quality of life and alleviate the economic strain on the European healthcare system.

The I3LUNG project sets hope on the horizon for mNSCLC patients, as it aims to deliver the right therapy to the right patient at the right time, thereby assisting physicians in making therapeutic decisions and patients in undergoing more conscious treatment journeys. We are enthusiastic about our involvement in this remarkable initiative, which has the potential to revolutionize lung cancer therapy.

For more information about I3LUNG, visit the Project website or contact us.

Artificial Intelligence in our daily lives

Artificial Intelligence (AI), despite being perceived as something straight out of science fiction, is present in our daily lives more than we think. From unlocking our phones with just a glance, to when we ask our virtual assistant for tomorrow’s weather, to using our favorite navigation app to find the less congested route… AI is there to make our lives easier and more efficient.

AI refers to the simulation of human intelligence in machines that are designed to perform tasks that would typically require human intelligence. AI systems are trained and improved over time to perform these tasks with a vast amount of data. The ultimate goal is to be able to get machines to perform certain tasks at a higher level, more accurately and efficiently.

Online Chat Bot assistant answering questions


Artificial Intelligence in the healthcare system

AI, thanks to the evolution of computer science, has made its way into the healthcare field, changing the way clinics and hospitals work.

One of the primary ways AI is supporting medical professionals is in diagnosis. With the help of AI algorithms, healthcare providers can quickly and accurately diagnose diseases, helping them to make more informed treatment decisions. AI can also assist in identifying patterns and correlations in medical data, identifying the symptoms of some diseases and matching them with specific conditions.

Another way AI is being used in clinics is for treatment planning. With AI algorithms, patient data, such as medical records, lab results and imaging studies can quickly be analyzed to determine the best treatment possibilities for a specific patient. This can help healthcare providers to personalize patient care and optimize the use of resources, improving patient experiences.

AI can also be used for predictive analytics, which involves using AI to analyze patient data and make predictions about future health outcomes. This can be particularly useful in identifying patients who are more prone to developing a particular condition, allowing intervention before the condition becomes serious.

Other ways in which AI has revolutionized the healthcare industry include:

In conclusion, the integration of AI technology into the healthcare system has allowed the field of medicine to receive new and better tools to improve accuracy, efficiency and patient outcomes.

Medical technology presented on a virtual screen


I3LUNG and how AI can be used in the treatment of lung cancer

The I3LUNG project aims to provide reliable tools to patients suffering from metastatic non-small cell lung cancer (mNSCLC) through the integration of vast amounts of data from 2000 patients. This integration will be achieved by the usage of AI and Machine Learning (ML), a subfield of AI.

The project involves close collaboration with physicians and AI/ML experts with the aim of changing clinical practice in the NSCLC setting and beyond.

The ultimate goal of the I3LUNG project is to study the individual response of patients to immunotherapy, a cancer treatment that utilizes the body’s own immune system to fight and eliminate the tumor.

Not all patients respond favorably to immunotherapy, and in some cases, it can lead to toxic side effects while representing a substantial cost to the health system.

In this context, I3LUNG arises, together with AI and ML to help predict patients’ individual responses to immunotherapy, considering their specific needs, and providing them with tailored therapies that represent the best possible option for their specific situation. This will not only improve the patient’s well-being and quality of life but also help reduce the economic impact of the treatment.

Patients are the focus of I3LUNG, and as such, they will take part in the decision-making of their treatment and will be followed by psychologists during the study to collect their feedback. Understanding their thoughts on the use of AI for their cases will allow to steer the project in the best possible direction.

I3LUNG platform, tools, and overall structure. The AVATAR based platform and the AI/ML based tools


For more information, please visit the Project section on our website and for any doubts, contact us!

Lung cancer

Cancer is an omnipresent reality in our lives. It affects countless individuals and their families, leaving a devastating impact on all those involved. Despite its prevalence, there is still much to be learned about this disease and how to find better treatments.

The lungs are essential organs of the human body, playing a critical role in keeping us alive by controlling respiration, providing oxygen to the bloodstream, and removing carbon dioxide from the body. Unfortunately, these vital organs are also susceptible to the development of lung cancer.

Lung cancer, a malignant disease of the lung tissue, is a major global health concern. According to the World Health Organization, lung cancer has been the second most common type of cancer in 2022 and the leading cause of cancer-related deaths worldwide.


Types of lung cancer

There are two primary types of lung cancer: non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC).

NSCLC, the type of lung cancer we focus on in I3LUNG, is the most common type and is less aggressive than SCLC. It includes several subtypes, such as adenocarcinoma, squamous cell carcinoma, and large cell carcinoma.

SCLC is a less common type of lung cancer and unfortunately, it’s not detected until it has spread to other parts of the body through a process known as metastasis.

NSCLC can also metastasize, which is why early detection is critical to prevent its spread.

Demography and statistics of lung cancer

Cancer is a disease that affects people of all ages, genders, ethnicities, and races. It does not discriminate. Genetic and hormonal differences, along with various environmental exposures and other factors, can lead to disparities in cancer risk among different populations. Fortunately, the number of cases is decreasing due to people quitting smoking and advances in early detection. The majority of lung cancer cases are located in Europe, particularly in Eastern Europe (according to the World Cancer Research Fund International).

Lung cancer rates by World Cancer Research Fund International

Lung cancer rates by World Cancer Research Fund International

The incidence of lung cancer increases with age, with most cases occurring in individuals around 65 years old. However, a smaller number of patients may be diagnosed at the age of 40 to 45 years old.

In contrast to decades past, when lung cancer was predominantly seen as a men’s disease, the incidence of lung cancer has risen among women. The difference in their incidence can be attributed to a variety of factors, including different smoking habits, occupational exposure to carcinogens, and genetic differences. Despite this, according to the American Cancer Society, the mortality rate for women is still lower than that of men.

The American Cancer Society reports that black men are 12% more likely to develop lung cancer than white men, while black women are 16% less likely to develop it than white women. An article by Matthew Schabath on “Racial and Ethnic Differences in the Epidemiology and Genomics of Lung Cancer” further explores the matter, mentioning that the survival rate of lung cancer patients differs according to their race and ethnicity, with white individuals having a higher survival rate than the black population. However, for small cell lung cancer (SCLC), black men are less likely to develop it than white men.

Survival rates for lung cancer vary depending on the type (SCLC being more severe than NSCLC) and the stage at which it is detected, as well as other factors. Therefore, early detection of the disease is essential to provide the patient with an effective treatment from the start.


Lung cancer prevention

The best way to prevent lung cancer is to never start smoking, or to quit it as soon as possible if you already do. Quitting smoking can drastically reduce your chances of developing the disease and improve your overall health.

Other ways to reduce the risk of having lung cancer include:

It’s also worth noting that early detection and treatment of lung cancer can improve outcomes, so it’s important to be aware of the symptoms of lung cancer and to see a doctor if you have any concerns.


I3LUNG and lung cancer

At I3LUNG, we are dedicated to providing personalized treatment for patients with metastatic non-small cell lung cancer (NSCLC) by using artificial intelligence. We are committed to ensuring that patients receive the care and treatment tailored to their individual needs, to improve their quality of life during this difficult time.

For more information, please visit the Project section in our website.

After the summary done for the first day of the kick-off, let’s dive into the second and last day of the event!


Saturday, July 2nd

On the second day of the kick-off meeting the rest of the WPs were presented. The morning started with the description about the creation of the AI integrative I3LUNG platform (WP8, Integrative Platform Construction). This interface will be designed and developed to make the data in the platform available to physicians, patients and researchers. James Dolezal (University of Chicago) and Alessandro Nuara (ML Cube) have been exposing their expertise in this first morning session.

The day continued with the Economic Impact Assessment and the Ethical, Privacy and Data Protection of WP9 (Economic Impact Assessment). The objective is to analyze the socio-economic impact of I3LUNG technology and construct a user-friendly model that captures the short-term affordability and long-run cost-effectiveness. Also, the legal and ethical implication of the I³LUNG platform will be evaluated, with a particular focus on EU data protection and privacy legislation, ethics and regulations applied to AI technologies, clinical trials legislation and the medical devices legislation. Michael Willis from Swedish Institute for Health Economics and Ana Maria Correa from KU Leuven Centre for IT & IP Law (CiTiP) were the leading speaker of this session. KUL is also leading partners of WP2 (Ethical, Privacy, and Data Protection).

The final session focused on the last WP10 (Dissemination, Communication and Exploitation). Natacha Bonnet explained how MEDSIR will promote, circulate, and integrate all relevant aspects and findings from the project to the scientific and non-scientific communities. This will include participation in scientific meetings, publications on specialized scientific and technical peer-review journals, attendance to trade fairs/exhibitions, direct engagement with I³LUNG platform users, synergies with other organizations for collaborations and knowledge transfer. Another team from Italy will be in charge of the marketing and business, covering IP agreement, a market study analysis and the development of a business plan.

Finally, the I³LUNG kick-off meeting was wrapped up with a description regarding the Project Management Guidelines, a talk explaining the Next Steps by Arsela Prelaj and a final presentation about the Executive Board members.


From now on, a yearly event will be held to analyze the progression made. Feel free to reach out to the people involved to know better about the I3LUNG!

As mentioned in our previous article, the kick-off meeting on July 1-2 officially started the EU I3LUNG Project. Let’s dive deep into the first day!

Friday, July 1st

The meeting started with the introduction of the project background and an update by the principal investigator (PI) of the I3LUNG, Arsela Prelaj (Istituto Nazionale Tumori of Milan – INT). Then Melissa Fernandez from MEDSIR, the company leading WP1 (Project Management and Coordination) and managing the trial coordination, explained MEDSIR’s initial support to the I3LUNG project, drafting the proposal and its submission to the EU portal.


(From left to right: Arsela Prelaj and Melissa Fernández)


For the development of this patient decision-making treatment AI-technology, a wide range of information from around 2,200 patients, retrospective and prospective, will be captured and integrated in the AI-system (WP3, Data Capturing and Integration). Different partners from Greece (Nikolas Spathas, Metropolitan Hospital), Germany (Lung Clinic Grosshansdorf; Guido Sauter from University Medical Center Hamburg Eppendorf), Spain (Ramon Amat, Vall d’Hebron Institute of Oncology), Italy (Monica Ganzinelli and Elsa Tagliabue from Istituto Nazionale dei Tumori; Laura Brunelli from Istituto di Ricerche Farmacologiche Mario Negri) and Israel (Laila Riosman from Shaare Zedek Medical Center) explained how molecular data will be collected and analyzed in this work package. This includes collection of clinical data, pathological analysis and molecular profiling of the cancer immune environment, characterization of genomics and immune related transcriptomics, metabolomics, circulating biomarkers of response or progression, microbiome profiling and radiomics.

Patient centricity is one of the key novelties in the I3LUNG project, and for the creation of the IPDAS, different partners involved in WP4 (Patient’s quality of life – QoL – and  Psychological  Impact  of  AI  on Medical Decision-Making) discussed about their specific implication in this part of the project:

The first day continued with more technical sessions. All the different patient-level data generated and gathered during the project have to be processed (WP5, Knowledge Extraction). The evaluable features will be extracted and delivered to the next collaborative groups for the AI algorithm design describing the real-world landscape and final model building.

Francesco Trovó and Laura Pedrocchi from Politecnico di Milano, leading WP6 (Learning and Reasoning) and WP7 (Explainable AI), finally introduced the concept of AI/ML modeling and model explainability, respectively, highlighting the methods that enables humans to understand, appropriately trust, and effectively manage machine learning models while maintaining a high level of learning performance.

This intense first day ended with a pleasant dinner at a beautiful restaurant in Milan next to Duomo, where the attending representatives from the 10 WP had the chance to chat and get to know each other. 




Let’s see what happened in Day 2 of the kick-off meeting! 

I3LUNG is a collaborative, 5-year, 10M€ EU-funded project aiming to create an artificial intelligence (AI)-based personalized decision-making tool to aid both clinicians and patients in selecting the best lung cancer treatment plan.

The consortium behind this project comprises 16 international partners, recognized companies and institutions located worldwide, whose miscellaneous expertise in clinical trial, molecular analysis, biology, AI, psychology, ethical and business will permit the realization of this ambitious project.

The partners will collaborate on different steps for the generation of a patient avatar, able to predict progression and outcome of each individual patients, accomplished through the following:



Following contract signature, the 16 partners met for a kick-off meeting on July 1 and 2 in Milan, in order to know each other face-to-face and organize the next steps and upcoming milestones of the project. This two-days intensive session was part of the project initiation phase, that brought together all the interdisciplinary teams involved to share information, align objectives and get to know each other.

Leading persons of each work package (WP) introduced their research centres, project teams, role, and responsibilities, as well as the individual and common project goals and objectives.

Let’s read in the next article how July 1st and 2nd went in details!