Prostate cancer (PCa) is the second most diagnosed cancer in men. Patients with PCa often develop metastases, with more than 80% of this metastases occurring in bone. The most common imaging technique used for screening, diagnosis and follow-up of disease evolution is bone scintigraphy, due to its high sensitivity and widespread availability at nuclear medicine facilities. To date, the assessment of bone scans relies solely on the interpretation of an expert physician who visually assesses the scan. Besides this being a time consuming task, it is also subjective, as there is no absolute criteria neither to identify bone metastases neither to quantify them by a straightforward and universally accepted procedure. In this paper, a new algorithm for the false positives reduction of automatically detected hotspots in bone scintigraphy images is proposed. The motivation relies in the difficulty of building a fully annotated database. In this way, our algorithm is a semisupervised method that works in an iterative way. The ultimate goal is to provide the physician with a fast, precise and reliable tool to quantify bone scans and evaluate disease progression and response to treatment. The algorithm is tested in a set of bone scans manually labeled according to the patient’s medical record. The achieved classification sensitivity, specificity and false negative rate were 63%, 58% and 37%, respectively. Comparison with other state-of-the-art classification algorithms shows superiority of the proposed method.

The purpose of this study was to quantify any differences between the SUVs of 89 Zr immuno-PET scans obtained using a PET/CT system with a long axial field of view (LAFOV; Biograph Vision Quadra) compared to a PET/CT system with a short axial field of view (SAFOV; Biograph Vision) and to evaluate how LAFOV PET scan duration affects image noise and SUV metrics. Methods: Five metastatic breast cancer patients were scanned consecutively on SAFOV and LAFOV PET/CT scanners. Four additional patients were scanned using only LAFOV PET/CT. Scans on both systems lasted approximately 30 min and were acquired 4 d after injection of 37 MBq of 89 Zr-trastuzumab. LAFOV list-mode data were reprocessed to obtain images acquired using shorter scan durations (15, 10, 7.5, 5, and 3 min). Volumes of interest were placed in healthy tissues, and tumors were segmented semiautomatically to compare coefficients of variation and to perform Bland-Altman analysis on SUV metrics (SUV max , SUV peak , and SUVmean ). Results: Using 30-min images, 2 commonly used lesion SUV metrics were higher for SAFOV than for LAFOV PET (SUV max , 16.2% 6 13.4%, and SUV peak , 10.1% 6 7.2%), whereas the SUV mean of healthy tissues showed minimal differences (0.7% 6 5.8%). Coefficients of variation in the liver derived from 30-min SAFOV PET were between those of 3-and 5-min LAFOV PET. The smallest SUV max and SUV peak differences between SAFOV and LAFOV were found for 3-min LAFOV PET. Conclusion: LAFOV 89 Zr immuno-PET showed a lower SUV max and SUV peak than SAFOV because of lower image noise. LAFOV PET scan duration may be reduced at the expense of increasing image noise and bias in SUV metrics. Nevertheless, SUV peak showed only minimal bias when reducing scan duration from 30 to 10 min.