Prostate-specific membrane antigen (PSMA) is overexpressed in prostate cancer epithelium, making it a promising target for molecular imaging and therapy. Recently, several studies found unexpected PSMA radiotracer uptake by thyroid tumors, including radioiodine-refractory (RAIR) cancers. PSMA expression was reported in tumor-associated endothelium of various malignancies, however it has not been systematically addressed in thyroid tumors. We found that PSMA was frequently expressed in microvessels of thyroid tumors (120/267), but not in benign thyroid tissue. PSMA expression in neovasculature was highly irregular ranging from 19% in benign tumors to over 50% in thyroid cancer. Such heterogeneity was not directly attributed to endothelial cell proliferation as confirmed by immunostaining with proliferation-associated endothelial marker CD105. PSMA expression was associated with tumor size (p = 0.02) and vascular invasion in follicular carcinoma (p = 0.03), but not with other baseline histological, and clinical parameters. Significant translational implication is that RAIR tumors and high-grade cancers maintain high level of PSMA expression, and can be targeted by PSMA ligand radiopharmaceuticals. Our study predicts several pitfalls potentially associated with PSMA imaging of the thyroid, such as low expression in oncocytic tumors, absence of organ specificity, and PSMA-positivity in dendritic cells of chronic thyroiditis, which is described for the first time.Prostate specific membrane antigen (PSMA) is a type II transmembrane glycoprotein highly restricted to prostate epithelium 1, 2 . It is also known as FOLH1 (folate hydrolase 1) or glutamate carboxypeptidase II. Immunohistochemical studies reported that PSMA is strongly expressed by normal and neoplastic prostatic epithelium, along with the epithelium of other genitourinary organs (bladder, kidney, fallopian tubes) and intestine [3][4][5] . Several recent studies found that PSMA could be expressed not only by epithelial cells, but also by vascular endothelium of various malignancies including oral 6 , gastric and colorectal 7 , lung 8 , breast 9 , endometrial and ovarian 10 , renal 11 , urothelial 12 , and glial tumors 13,14 .PSMA is an integral membrane protein, anchored to the epithelial cells. This makes an important advantage of being a targeting marker over prostate-related secretory antigens released into bloodstream, such as prostate specific antigen, prostatic acid phosphatase and prostate secretory protein 15 . Molecular imaging of PSMA is now being widely adopted in prostate cancer diagnostics [16][17][18] . 68 Ga-PSMA PET/CT is a novel imaging modality based on 68 Ga conjugated with anti-PSMA monoclonal antibody, which is highly accurate in detecting prostate cancer 19 . It also has promising therapeutic potential, being a carrier for radionuclides directed against cancer cells. One of such theranostic example is 177 Lu-labelled tracer PSMA-DKFZ-617, which demonstrated radiological response in preclinical model and clinical studies of prostate canc...

One challenge in applying deep learning to medical imaging is the lack of labeled data. Although large amounts of clinical data are available, acquiring labeled image data is difficult, especially for bone scintigraphy (i.e., 2D bone imaging) images. Bone scintigraphy images are generally noisy, and ground-truth or gold standard information from surgical or pathological reports may not be available. We propose a novel neural network model that can segment abnormal hotspots and classify bone cancer metastases in the chest area in a semisupervised manner. Our proposed model, called MaligNet, is an instance segmentation model that incorporates ladder networks to harness both labeled and unlabeled data. Unlike deep learning segmentation models that classify each instance independently, MaligNet utilizes global information via an additional connection from the core network. To evaluate the performance of our model, we created a dataset for bone lesion instance segmentation using labeled and unlabeled example data from 544 and 9,280 patients, respectively. Our proposed model achieved mean precision, mean sensitivity, and mean F1-score of 0.852, 0.856, and 0.848, respectively, and outperformed the baseline mask region-based convolutional neural network (Mask R-CNN) by 3.92%. Further analysis showed that incorporating global information also helps the model classify specific instances that require information from other regions. On the metastasis classification task, our model achieves a sensitivity of 0.657 and a specificity of 0.857, demonstrating its great potential for automated diagnosis using bone scintigraphy in clinical practice.

Purpose To improve the performance for localizing epileptic foci, we have developed a joint ictal/inter-ictal SPECT reconstruction method in which ictal and inter-ictal SPECT projections are simultaneously reconstructed to obtain the differential image. Methods We have developed a SPECT reconstruction method that jointly reconstructs ictal and inter-ictal SPECT projection data. We performed both phantom and patient studies to evaluate the performance of our joint method for epileptic foci localization as compared with the conventional subtraction method in which the differential image is obtained by subtracting the inter-ictal image from the co-registered ictal image. Two low-noise SPECT projection data sets were acquired using 99mTc and a Hoffman head phantom at two different positions and orientations. At one of the two phantom locations, a low-noise data set was also acquired using a 99mTc-filled 3.3-cm sphere with a cold attenuation background identical to the Hoffman phantom. These three datasets were combined and scaled to mimic low-noise clinical ictal (three different lesion-to-background contrast levels: 1.25, 1.55 and 1.70) and inter-ictal scans. For each low-noise data set, twenty-five noise realizations were generated by adding Poisson noise to the projections. The mean and standard deviation (SD) of lesion contrast in the differential images were computed using both the conventional subtraction and our joint methods. We also applied both methods to the 35 epileptic patient datasets. Each differential image was presented to two nuclear medicine physicians to localize a lesion and specify a confidence level. The readers’ data were analyzed to obtain the localized-response receiver operating characteristic (LROC) curves for both the subtraction and joint methods. Results For the phantom study, the difference between the mean lesion contrast in the differential images obtained using the conventional subtraction versus our joint method decreases as the iteration number increases. Compared with the conventional subtraction approach, the SD reduction of lesion contrast at the 10th iteration using our joint method ranges from 54.7% to 68.2% (p<0.0005), and 33.8% to 47.9% (p<0.05) for 2 and 4 million total inter-ictal counts, respectively. In the patient study, our joint method increases the area under LROC from 0.24 to 0.34 and from 0.15 to 0.20 for the first and second reader, respectively. We have demonstrated improved performance of our method as compared to the standard subtraction method currently used in clinical practice. Conclusion The proposed joint ictal/inter-ictal reconstruction method yields better performance for epileptic foci localization than the conventional subtraction method.

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