Artificial intelligence (AI) software developer Optellum said it will showcase a prototype of its deep learning-based lung nodule risk stratification software at this week's World Congress of Thoracic Imaging (WCTI) in Boston.
The AI software is designed to help radiologists and pulmonologists in managing patients with nodules detected incidentally or at lung cancer screening. It assesses chest CT studies and other patient metadata to provide an objective quantitative score related to the malignancy of a nodule, according to the U.K.-based company. In collaboration with partners at the University of Oxford, the software was trained and tested on curated databases that included thousands of patients with nodules and ground-truth outcomes, Optellum said.
The technology won the 2015 LungX automatic nodule classification challenge sponsored by the U.S. National Cancer Institute, according to the company. Optellum is currently preparing a multicenter, prospective study to validate the performance of the software.


















![Images show the pectoralis muscles of a healthy male individual who never smoked (age, 66 years; height, 178 cm; body mass index [BMI, calculated as weight in kilograms divided by height in meters squared], 28.4; number of cigarette pack-years, 0; forced expiratory volume in 1 second [FEV1], 97.6% predicted; FEV1: forced vital capacity [FVC] ratio, 0.71; pectoralis muscle area [PMA], 59.4 cm2; pectoralis muscle volume [PMV], 764 cm3) and a male individual with a smoking history and chronic obstructive pulmonary disorder (COPD) (age, 66 years; height, 178 cm; BMI, 27.5; number of cigarette pack-years, 43.2, FEV1, 48% predicted; FEV1:FVC, 0.56; PMA, 35 cm2; PMV, 480.8 cm3) from the Canadian Cohort Obstructive Lung Disease (i.e., CanCOLD) study. The CT image is shown in the axial plane. The PMV is automatically extracted using the developed deep learning model and overlayed onto the lungs for visual clarity.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/03/genkin.25LqljVF0y.jpg?auto=format%2Ccompress&crop=focalpoint&fit=crop&h=112&q=70&w=112)

