Friday, December 2 | 10:40 a.m.-10:50 a.m. | SST03-02 | Room E451B
Is there a better tool than the radiologist's eye to determine whether a CT-detected lung nodule is benign or malignant? There might be. In this study, researchers put a promising nodule discrimination model in a controlled observer study.A presentation scheduled to take place immediately preceding this one will reveal the high discriminant value of a predication model applied to a National Lung Screening Trial (NLST) dataset. The Brock or PanCan model showed impressive performance when applied to a subset of NLST data, with sensitivity of 85% and specificity in the 90s.
Could it work as well in a controlled setting? The researchers will describe their experiences in this presentation.
"We conducted an observer performance test to compare the accuracy of the model with that of radiologists in a simulated clinical environment," wrote Dr. Heber MacMahon, a professor of radiology at the University of Chicago, in an email to AuntMinnie.com.
The investigators tested the model using both automated and manual feature extraction on 100 NLST cases that included 20 proven cancers and 80 matched benign nodules.
The model worked well -- but not quite as well as experienced radiologists and trainees, who were significantly more accurate in estimating the risk of malignancy in size-matched, screen-detected nodules.
Experienced radiologists performed better than trainees and were less influenced by the model. The session will explain which nodule characteristics informed the human readers.
![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=100&q=70&w=100)
![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)
