Monday, November 29 | 9:30 a.m.-10:30 a.m. | SSGI06-5 | Room TBA
A deep learning-based software application can improve the objectivity of treatment response evaluation in patients with hepatocellular carcinoma (HCC), according to this presentation.Researchers led by presenter Matt Kelly, PhD, of artificial intelligence (AI) firm Perspectum developed a 3D convolutional neural network (CNN) in an effort aimed at improving on the intrareader and interreader variability often experienced when radiologists utilize the modified Response Evaluation Criteria in Solid Tumors (mRECIST) criteria to assess HCC treatment response.
Using dual-phase contrast-enhanced abdominal CT exams from a multicenter drug trial, the researchers trained the CNN to automatically segment both the liver and lesions, as well as to estimate diameter. In testing, the algorithm's automatically generated lesion segmentation masks showed high agreement with manually drawn 3D masks. In addition, the model's automatic diameter measurements were deemed to be in close agreement with the interpretation of a central radiologist in 81.8% of the cases.
"This deep-learning based lesion detection and diameter measurement tool can support radiologists by minimizing subjectivity in mRECIST measurements and reducing time per analysis," the authors wrote. "This has the potential to improve both routine care and clinical trial workflow."
Want to learn more? You may want to sit in on this Monday session.
![Axial images from unenhanced calcium score cardiac CT (left) and curved planar reformation images from CT angiography (right) show that higher long-term exposure to air pollution is associated with greater coronary artery calcium and more obstructive coronary artery disease (CAD). Top row: Images in a 68-year-old male patient with higher 10-year mean ambient air pollution exposure (7.9 μg/m3 for particulate matter measuring ≤2.5 μm in diameter [PM2.5] and 17.4 parts per billion [ppb] for NO2) with extensive CAD (coronary artery calcium score [CACS] >1,000 and obstructive CAD [≥70% diameter stenosis]). Bottom row: Images in a 57-year-old female patient with lower 10-year mean ambient air pollution exposure (6.3 μg/m3 for PM2.5 and 4.6 ppb for NO2) with no CAD (CACS = 0 and no obstructive stenosis).](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/06/hanneman.r6SMLzkezo.png?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
