Median Technologies has released trial results showing that its artificial intelligence (AI) algorithm performed well in characterizing malignant and benign lung nodules in patients receiving low-dose chest CT lung cancer screening.
The company's new iBiopsy computer-aided diagnosis (CADx) algorithm was trained on a dataset from 1,224 patients with 11,392 nodules from the National Lung Screening Trial and then tested on CT images from 472 patients with 4,216 nodules. The application yielded 95.2% sensitivity and 95.7% specificity for lung nodule characterization, as well as an area under the curve of 0.991, Median said.
The company is planning an additional large-scale study for an end-to-end digital lung cancer screening application that will provide both nodule detection and characterization. Results are expected in the fourth quarter.
![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&dpr=2&fit=crop&h=167&q=70&w=250)
