Tuesday, November 28 | 11:20 a.m.-11:30 a.m. | SSG11-06 | Room N229
In this study, Canadian researchers investigated the potential of analyzing dual-energy CT scans to determine the nodal status of squamous cell carcinoma (SCC) in the head and neck.The imaging approach to evaluate enlarged lymph nodes and detect possible metastasis of squamous cell carcinoma continues to be a substantial challenge, Dr. Reza Forghani, PhD, from Jewish General Hospital in Montreal told AuntMinnie.com. The limitations of the present methods for detecting metastases in the lymph nodes often lead to neck surgery, which ends up being unnecessary in up to 70% of patients.
The researchers conducted a texture analysis of tumors on dual-energy CT scans of 87 patients with head and neck SCC using artificial intelligence (AI) to facilitate their visualization. Applying AI to dual-energy CT scans improved the texture analysis of the scans and also predicted nodal metastases in the neck, they found.
This technique can form the basis of an AI-assisted tool for predicting early nodal metastases and reducing the occurrence of unnecessary neck dissections, according to Forghani.
"This has potential applications beyond the head and neck, including applications in radiomic models developed to evaluate pathology for other organ systems," he said.


















![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)

