Radiolung, a joint project of the Computer Vision Center and the Germans Trias Hospital, wins the Innovation Award from the Lung Ambition Alliance for lung cancer early detection

• Radiolung is aimed to develop an artificial intelligence system based on radiomic information to improve the malignancy detection of lung nodules.
• The research team is compound by members of the CVC research group Interactive Augmented Modeling for Biomedicine, led by Dr. Dèbora Gil, and various departments of the Hospital and Research Institute Germans Trias i Pujol, led by Dr. Antoni Rosell.

The Lung Ambition Alliance (LAA), a consortium recently created under the auspices of the International Association for the Study of Lung Cancer (IASLC), has recognized with the Innovation Award to the CVC and Germans Trias project “Radiomics and Radiogenomics in lung cancer screening – Radiolung”. The LAA Scientific Advisory Committee has distinguished the project led by Antoni Rosell, clinician of German Trias, for its scientific quality, novelty, potential application and contribution to the main goal of the alliance, double the lung cancer survival between 2020 and 2025.

Radiolung is aimed to develop a predictive model, based on artificial intelligence and radiomics, to improve the detection of malignancy of lung nodules and thus create a software with potential to be used with clinical purposes and improve the current model of lung cancer screening.

Radiomic for a more accurate diagnosis

Currently, lung cancer screening is performed by means of low-radiation computed tomography (CT-Chest) that detects lung nodules, but does not allow clinicians to assess, with enough accuracy, whether they are benign or malignant. Therefore, most patients have to undergo radiological follow-up, consisting of complementary radiological examinations and, sometimes, take of biopsy samples to refine the diagnosis.

In order to improve the diagnostic capacity of CT scans, researchers from the CVC and Germans Trias are working on an artificial intelligence algorithm that is able to analyse the mathematical characteristics of the images, a technique known as radiomics.

“Radiomics can extract a lot of 3D measurements of CT scans far beyond the visual capacity of the human eye, and combine them with histological and molecular characteristics of the lung tumours. In this way, you can systematically find, with a single CT image, the ranges that most correlate with the malignancy and/or severity of the nodule”, explains the principal investigator of the CVC research group Interactive Augmented Modeling for Biomedicine (IAM4B), Dr. Dèbora Gil. “This analysis of multiple data is impossible for a radiologist to perform it visually, so radiomics can be a very useful tool to help specialists to make more accurate diagnoses”, Dr. Gil continues.

For his part, Antoni Rosell, clinical director of the Thorax Area at Germans Trias and principal investigator of Radiolung, points out “being able to determine the degree of aggressiveness of the lung nodule and its mutational profile in its early stages by radiomics will allow to predict the long-term behaviour of the tumour, both in terms of evolution and the risk of recurrence. In this way, we will be able to provide targeted, specific and personalized treatments to patients, even in the initial state of the disease”.

With the Innovation Award, Radiolung has obtained 30.000 € for the development of this technology. “Thanks to this award, we will be able to transfer this innovation to the clinical practice,” concludes Rosell. The project has also been recognized by a Premi Talents grant, an initiative promoted by the Fundació Catalunya-La Pedrera and the Hospital Germans Trias, with the aim of funding research projects for health professionals who finished their residency.

In the media

Premian un proyecto de Can Ruti para la detección precoz del cáncer de pulmón – La Vanguardia (in spanish)

El projecte Radiolung de Germans Trias i del Centre de Visió per Computador guanya el Premi a la Innovació per a la detecció precoç de càncer – TOT Badalona (in catalan)