A threshold-based method to predict thyroid nodules on scintigraphy scans.

Nuclear Medicine imaging is an important modality to follow up abnormalities of thyroid function tests and to uncover and characterize thyroid nodules either de novo or as previously seen on other imaging modalities, namely ultrasound. In general, the hypofunctioning 'cold' nodules pose a higher malignancy potential than hyperfunctioning 'hot' nodules, for which the risk is <1%. Hot nodules are detected by the radiologist as a region of focal increased radiotracer uptake, which appears as a density of pixels that is higher than surrounding normal thyroid parenchyma. Similarly, cold nodules show decreased density of pixels, corresponding to their decreased uptake of radiotracer, and are photopenic. Partly because Nuclear Medicine images have poor resolution, these density variations can sometimes be subtle, and a second reader computer-aided detection (CAD) scheme that can highlight hot/cold nodules has the potential to reduce false negatives by bringing the radiologists' attention to the occasional overlooked nodules. Our approach subdivides thyroid images into small regions and employs a set of pixel density cutoffs, marking regions that fulfill density criteria. Thresholding is a fundamental tool in image processing. In nuclear medicine, scroll bars to adjust standardized uptake value cutoffs are already in wide commercial use in PET/CT display systems. A similar system could be used for planar thyroid images, whereby the user varies threshold and highlights suspect regions after an initial reader survey of the images. We hypothesized that a thresholding approach would accurately detect both hot and cold thyroid nodules relative to expert readers. Analyzing 22 nodules, half of them hot and the other half cold, we found good agreement between highlighted candidate nodules and the consensus selections of two expert readers, with nonzero overlap between expert and CAD selections in all cases.