A novel AI approach can accurately detect six different types of cancer on whole-body PET/CT scans, according to research presented at the 2024 Society of Nuclear Medicine and Molecular Imaging Annual Meeting. By automatically quantifying tumor burden, the new tool can be useful for assessing patient risk, predicting treatment response, and estimating survival. Automatic detection and characterization of cancer are important clinical needs to enable early treatment.
Most AI models that aim to detect cancer are built on small to moderately sized datasets that usually encompass a single malignancy and/or radiotracer. This represents a critical bottleneck in the current training and evaluation paradigm for AI applications in medical imaging and radiology." Kevin H.
Leung, PhD, research associate at Johns Hopkins University School of Medicine in Baltimore, Maryland To address this issue, researchers developed a deep transfer learning approach (a type of AI) for fully automated, whole-body tumor segmentation and prognosis on PET/CT scans. Data from 611 FDG PET/CT scans of patients with lung cancer, melanoma , lymphoma, head and neck cancer, and breast cancer, as well as 408 PSMA PET/CT scans of prostate cancer patients were analyzed in the study. The AI approach automatically extracted radiomic features and whole-body imaging measures from the predicted tumor segmentations to quantify molecular tumor burden and uptake across all cancer types.
Quantitative features and imaging meas.
