Intraoperative radio-guided localization of parathyroid adenomas using 3D freehand SPECT technology.

Parathyroidectomy for primary hyperparathyroidism (PHPT) could have poor outcomes, even with accurate preoperative localization of the adenomas, because their intraoperative localization can be challenging. Freehand single photon emission computed tomography (fhSPECT) is a new technique for radio-guided intraoperative navigation. Its use during parathyroidectomy could be useful and such data are limited. We herein present our experience on the feasibility of fhSPECT for intraoperative detection of abnormal parathyroid glands. We retrospectively reviewed the clinical data of 55 patients (30-77 years old) with PHPT due to parathyroid adenomas, that were subjected to parathyroidectomy from 12/2017 to 7/2022. In average, 111 ± 74 MBq of Tc-99 m Sestamibi were injected intravenously, approximately 2 h before the operation and fhSPECT was used to generate 3D images during parathyroidectomy. Measurements of PTH and calcium levels were performed preoperatively, postoperatively and 4-6 months after the procedure. FhSPECT successfully identified the parathyroid adenoma in all the patients. It took 3 min (median time) for fhSPECT to detect at least one radioactive spot in all patients. The mean duration of the operation was 66.6 ± 7.3 min. Forty-nine patients out of 55 had solitary and 6/55 had multiple adenomas, whereas 6/55 had ectopic abnormal parathyroid glands. None of the patients had persistent hyperparathyroidism during follow-up. To the best of our knowledge, this is the largest series of patients with PHPT that underwent fhSPECT assisted parathyroidectomy. Our data suggest that this navigation system is helpful in identifying parathyroid adenomas intraoperatively.

Fuzzy Cognitive Map Applications in Medicine over the Last Two Decades: A Review Study.

Fuzzy Cognitive Maps (FCMs) have become an invaluable tool for healthcare providers because they can capture intricate associations among variables and generate precise predictions. FCMs have demonstrated their utility in diverse medical applications, from disease diagnosis to treatment planning and prognosis prediction. Their ability to model complex relationships between symptoms, biomarkers, risk factors, and treatments has enabled healthcare providers to make informed decisions, leading to better patient outcomes. This review article provides a thorough synopsis of using FCMs within the medical domain. A systematic examination of pertinent literature spanning the last two decades forms the basis of this overview, specifically delineating the diverse applications of FCMs in medical realms, including decision-making, diagnosis, prognosis, treatment optimisation, risk assessment, and pharmacovigilance. The limitations inherent in FCMs are also scrutinised, and avenues for potential future research and application are explored.

Comparison of 18F-PSMA-1007 and 18F-Choline PET/CT in prostate cancer patients with biochemical recurrence: a phase 3, prospective, multicenter, randomized study.

OBJECTIVES: This prospective, multicenter, open-label, randomized, crossover trial study was to evaluate the diagnostic performance of 18F-PSMA-1007 (PSMA) vs. 18F-Choline PET/CT (FCH) in prostate cancer (PCa) patients (pts) with biochemical recurrence (BCR). METHODS: One hundred eighty-six pts, who have undergone primary definitive treatment for PCa with BCR, were recruited to this prospective study. All pts underwent one PSMA and one FCH PET/CT examination in randomized order within a time frame of 8 days and were followed up for at least 6 months (182 ±â€…10 days). RESULTS: Recurrence of PCa was observed in 176 out of 186 pts. The overall correct detection rate (DR) was 84% (95% CI 0.7967-0.8830) for PSMA and 69% (95% CI 0.6191-0.7489) for FCH, yielding a difference in proportion of 16% ( P  < 0.0001). PSMA had a sensitivity of 0.8464 and FCH 0.6857 with an odds ratio of 2.5259 ( P  < 0.0001), with statistically significant greater sensitivity of PSMA (ORs, 2.7877 and 2.1283 respectively) ( P  < 0.0001). PET/CT imaging led to a more accurate diagnosis in 166 (89.2%) pts, of which PSMA had contributed more than FCH in 91 (54.8%) of them. The DR for cutoff point PSA ≤ 1 ng/ml was higher for PSMA compared to FCH (61.8% vs. 39.5%). DR value of 51.6% for PSMA reached at PSA ≤ 0.3 ng/ml, while FCH reached that DR value with PSA ≤ 2.2 ng/ml. CONCLUSION: 18F-PSMA-1007 is more efficacious than 18F-Choline for the identification metastatic lesions both in patient and in regional level analysis in PCa patients with BCR.

A radiomic- and dosiomic-based machine learning regression model for pretreatment planning in 177 Lu-DOTATATE therapy.

BACKGROUND: Standardized patient-specific pretreatment dosimetry planning is mandatory in the modern era of nuclear molecular radiotherapy, which may eventually lead to improvements in the final therapeutic outcome. Only a comprehensive definition of a dosage therapeutic window encompassing the range of absorbed doses, that is, helpful without being detrimental can lead to therapy individualization and improved outcomes. As a result, setting absorbed dose safety limits for organs at risk (OARs) requires knowledge of the absorbed dose-effect relationship. Data sets of consistent and reliable inter-center dosimetry findings are required to characterize this relationship. PURPOSE: We developed and standardized a new pretreatment planning model consisting of a predictive dosimetry procedure for OARs in patients with neuroendocrine tumors (NETs) treated with 177 Lu-DOTATATE (Lutathera). In the retrospective study described herein, we used machine learning (ML) regression algorithms to predict absorbed doses in OARs by exploiting a combination of radiomic and dosiomic features extracted from patients' imaging data. METHODS: Pretreatment and posttreatment data for 20 patients with NETs treated with 177 Lu-DOTATATE were collected from two clinical centers. A total of 3412 radiomic and dosiomic features were extracted from the patients' computed tomography (CT) scans and dose maps, respectively. All dose maps were generated using Monte Carlo simulations. An ML regression model was designed based on ML algorithms for predicting the absorbed dose in every OAR (liver, left kidney, right kidney, and spleen) before and after the therapy and between each therapy session, thus predicting any possible radiotoxic effects. RESULTS: We evaluated nine ML regression algorithms. Our predictive model achieved a mean absolute dose error (MAE, in Gy) of 0.61 for the liver, 1.58 for the spleen, 1.30 for the left kidney, and 1.35 for the right kidney between pretherapy 68 Ga-DOTATOC positron emission tomography (PET)/CT and posttherapy 177 Lu-DOTATATE single photon emission (SPECT)/CT scans. Τhe best predictive performance observed was based on the gradient boost for the liver, the left kidney and the right kidney, and on the extra tree regressor for the spleen. Evaluation of the model's performance according to its ability to predict the absorbed dose in each OAR in every possible combination of pretherapy 68 Ga-DOTATOC PET/CT and any posttherapy 177 Lu-DOTATATE treatment cycle SPECT/CT scans as well as any 177 Lu-DOTATATE SPECT/CT treatment cycle and the consequent 177 Lu-DOTATATE SPECT/CT treatment cycle revealed mean absorbed dose differences ranges from -0.55 to 0.68 Gy. Incorporating radiodosiomics features from the 68 Ga-DOTATOC PET/CT and first 177 Lu-DOTATATE SPECT/CT treatment cycle scans further improved the precision and minimized the standard deviation of the predictions in nine out of 12 instances. An average improvement of 57.34% was observed (range: 17.53%-96.12%). However, it's important to note that in three instances (i.e., Ga,C.1 â†’ C3 in spleen and left kidney, and Ga,C.1 â†’ C2 in right kidney) we did not observe an improvement (absolute differences of 0.17, 0.08, and 0.05 Gy, respectively). Wavelet-based features proved to have high correlated predictive value, whereas non-linear-based ML regression algorithms proved to be more capable than the linear-based of producing precise prediction in our case. CONCLUSIONS: The combination of radiomics and dosiomics has potential utility for personalized molecular radiotherapy (PMR) response evaluation and OAR dose prediction. These radiodosiomic features can potentially provide information on any possible disease recurrence and may be highly useful in clinical decision-making, especially regarding dose escalation issues.

PET/CT: Clinical role in lymphomas.

18F-FDG PET/CT is the imaging modality of choice for the accurate initial staging of most lymphomas. Hodgkin's, Diffuse Large B-cell and follicular lymphomas show avid FDG uptake, while a minority of Non-Hodgkin lymphoma subtypes namely MALT, marginal and small lymphocytic lymphoma demonstrate low or moderate avidity. As a rule of thumb, indolent lymphomas show lower FDG activity than aggressive ones. PET/CT has increased sensitivity in the detection of nodal involvement even in small or normal-sized nodes. It shows higher sensitivity than CT in the detection of extra-nodal disease, most often in the spleen and bone marrow. PET/CT leads to upstaging in up to 25% of Hodgkin lymphomas, paving the way to intensified therapy. It has excellent Negative Predictive Value (NPV>95%) in the detection of bone marrow involvement in Hodgkin's rendering bone marrow biopsy not absolutely necessary: a negative PET rules out bone marrow disease in Hodgkin's patients, yet this does not universally apply in Non-Hodgkin lymphomas. PET/CT is superior to other imaging modalities in the initial stating of aggressive Non-Hodgkin lymphomas detecting disease in previously not suspected or occult sites. 18F-FDG PET/CT is applied in the early therapeutic evaluation of Hodgkin's by means of interim PET performed after 2-3 initial cycles of chemotherapy. Patients with negative interim PET and no hypermetabolic disease identified may continue with the same effective treatment or switch to less aggressive therapy. On the other hand, patients who do not show PET response may be subjected to more intensified treatment to eradicate hypermetabolic active disease. Randomized controlled trials have proven that interim PET/CT shows high NPV for final treatment response and for increased progression free survival in Hodgkin's. The accuracy in reporting and interpretating interim and post-treatment PET/CT has increased by applying specific objective criteria: Deauville 5-score scale. Deauville's uptake scores of 4-5, more intense than liver activity, correspond to active lymphomatous disease. 18F-FDG uptake in lesions, equal or lower than mediastinal blood-pool, is interpreted as negative: Deauville scores of 1-2. Role of interim PET is also investigated in Non-Hodgkin Lymphomas, especially nowadays with more effective treatments being applied. PET/CT is highly recommended for post-treatment assessment of lymphomas with excellent NPV and superior diagnostic accuracy compared with CT. After treatment, a significant proportion of patients show residual anatomic lesions on CT f.e. residual mediastinal soft-tissue; yet, in the minority of cases, these lesions correspond to active disease. PET/CT has high diagnostic accuracy in the assessment of residual tissue and may distinguish between PET-negative fibrotic or necrotic tissue and PET-positive, active residual disease. The modality also has high NPV in the evaluation of megatherapy before stem cell transplantation: a favorable PET response is associated with better progression free survival and overall survival. To sum up, PET/CT has evolved as an established method in lymphoma patients being incorporated into clinical algorithms and guidelines altering therapeutic decisions.

Deep Learning Assessment for Mining Important Medical Image Features of Various Modalities.

Deep learning (DL) is a well-established pipeline for feature extraction in medical and nonmedical imaging tasks, such as object detection, segmentation, and classification. However, DL faces the issue of explainability, which prohibits reliable utilisation in everyday clinical practice. This study evaluates DL methods for their efficiency in revealing and suggesting potential image biomarkers. Eleven biomedical image datasets of various modalities are utilised, including SPECT, CT, photographs, microscopy, and X-ray. Seven state-of-the-art CNNs are employed and tuned to perform image classification in tasks. The main conclusion of the research is that DL reveals potential biomarkers in several cases, especially when the models are trained from scratch in domains where low-level features such as shapes and edges are not enough to make decisions. Furthermore, in some cases, device acquisition variations slightly affect the performance of DL models.

A Deep Learning Methodology for the Detection of Abnormal Parathyroid Glands via Scintigraphy with 99mTc-Sestamibi.

BACKGROUND: Parathyroid proliferative disorder encompasses a wide spectrum of diseases, including parathyroid adenoma (PTA), parathyroid hyperplasia, and parathyroid carcinoma. Imaging modalities that deliver their results preoperatively help in the localisation of parathyroid glands (PGs) and assist in surgery. Artificial intelligence and, more specifically, image detection methods, can assist medical experts and reduce the workload in their everyday routine. METHODS: The present study employs an innovative CNN topology called ParaNet, to analyse early MIBI, late MIBI, and TcO4 thyroid scan images simultaneously to perform first-level discrimination between patients with abnormal PGs (aPG) and patients with normal PGs (nPG). The study includes 632 parathyroid scans. RESULTS: ParaNet exhibits a top performance, reaching an accuracy of 96.56% in distinguishing between aPG and nPG scans. Its sensitivity and specificity are 96.38% and 97.02%, respectively. PPV and NPV values are 98.76% and 91.57%, respectively. CONCLUSIONS: The proposed network is the first to introduce the automatic discrimination of PG and nPG scans acquired by scintigraphy with 99mTc-sestamibi (MIBI). This methodology could be applied to the everyday routine of medics for real-time evaluation or educational purposes.

Diagnostic and Therapeutic Challenges in a Patient with Ureteral Metastases from a Triple Negative Breast Cancer.

Metastatic ureteral tumors arising from a primary breast carcinoma are extremely rare. They present with hematuria and radiological findings compatible with obstructive ureteral phenomena. We present a case of an 87-year-old woman with a history of lymphoma and triple negative breast cancer (TNBC), during an emergency admission for peptic ulcer, developed macroscopic hematuria. Radiologic and endoscopic investigations revealed a remarkable stenosis at the lower segment of the right ureter, attributed to metastases from her breast carcinoma. We report this case with the aim to make both oncologists and urologists aware of this rare condition.

Applications of Generative Adversarial Networks (GANs) in Positron Emission Tomography (PET) imaging: A review.

PURPOSE: This paper reviews recent applications of Generative Adversarial Networks (GANs) in Positron Emission Tomography (PET) imaging. Recent advances in Deep Learning (DL) and GANs catalysed the research of their applications in medical imaging modalities. As a result, several unique GAN topologies have emerged and been assessed in an experimental environment over the last two years. METHODS: The present work extensively describes GAN architectures and their applications in PET imaging. The identification of relevant publications was performed via approved publication indexing websites and repositories. Web of Science, Scopus, and Google Scholar were the major sources of information. RESULTS: The research identified a hundred articles that address PET imaging applications such as attenuation correction, de-noising, scatter correction, removal of artefacts, image fusion, high-dose image estimation, super-resolution, segmentation, and cross-modality synthesis. These applications are presented and accompanied by the corresponding research works. CONCLUSION: GANs are rapidly employed in PET imaging tasks. However, specific limitations must be eliminated to reach their full potential and gain the medical community's trust in everyday clinical practice.

A deep-learning-based prediction model for the biodistribution of 90 Y microspheres in liver radioembolization.

BACKGROUND: Radioembolization with 90 Y microspheres is a treatment approach for liver cancer. Currently, employed dosimetric calculations exhibit low accuracy, lacking consideration of individual patient, and tissue characteristics. PURPOSE: The purpose of the present study was to employ deep learning (DL) algorithms to differentiate patterns of pretreatment distribution of 99m Tc-macroaggregated albumin on SPECT/CT and post-treatment distribution of 90 Y microspheres on PET/CT and to accurately predict how the 90 Y-microspheres will be distributed in the liver tissue by radioembolization therapy. METHODS: Data for 19 patients with liver cancer (10 with hepatocellular carcinoma, 5 with intrahepatic cholangiocarcinoma, 4 with liver metastases) who underwent radioembolization with 90 Y microspheres were used for the DL training. We developed a 3D voxel-based variation of the Pix2Pix model, which is a special type of conditional GANs designed to perform image-to-image translation. SPECT and CT scans along with the clinical target volume for each patient were used as inputs, as were their corresponding post-treatment PET scans. The real and predicted absorbed PET doses for the tumor and the whole liver area were compared. Our model was evaluated using the leave-one-out method, and the dose calculations were measured using a tissue-specific dose voxel kernel. RESULTS: The comparison of the real and predicted PET/CT scans showed an average absorbed dose difference of 5.42% ± 19.31% and 0.44% ± 1.64% for the tumor and the liver area, respectively. The average absorbed dose differences were 7.98 ± 31.39 Gy and 0.03 ± 0.25 Gy for the tumor and the non-tumor liver parenchyma, respectively. Our model had a general tendency to underpredict the dosimetric results; the largest differences were noticed in one case, where the model underestimated the dose to the tumor area by 56.75% or 72.82 Gy. CONCLUSIONS: The proposed deep-learning-based pretreatment planning method for liver radioembolization accurately predicted 90 Y microsphere biodistribution. Its combination with a rapid and accurate 3D dosimetry method will render it clinically suitable and could improve patient-specific pretreatment planning.

Neglected Posterior Dislocation Septic Loosening and Prolonged Flexion Contracture of Total Knee Arthroplasty: A Case Report.

BACKGROUND Chronic posterior knee dislocation after total knee arthroplasty (TKA) is a rare but serious complication, especially when it coexists with septic implant loosening, flexion contracture, and extensive comorbidities. Although the severity is comparable to that in the native knee dislocation, there are few reports in the literature. When TKA dislocation is complicated with infection, bone defect, and patient's comorbidity, treatment becomes even more difficult. For these complex complications, in order to provide stable and mobile knees, constrained total knee prostheses are used. CASE REPORT We present the case of a 63-year-old, non-ambulatory man, with mental retardation and multiple comorbidities, who had a 9-year history of neglected posterior TKA dislocation, which later became complicated with septic loosening and productive fistula for 4 years. The patient required use of a wheel-chair for several years, was obese with a body mass index (BMI) of 34.3, and the affected knee was in a prolonged flexion contracture at 90°. The posterior TKA dislocation was later infected by Staphylococcus hominis and Staphylococcus epidermidis. He was successfully treated with two-stage revision surgery and managed to become ambulatory after 6 years of requiring use of a wheel-chair. CONCLUSIONS Neglected posterior dislocation of TKA is a rare and potentially limp-causing complication, especially when accompanied with chronic infection, implant loosening, severe bone loss, flexion contracture, and extensive comorbidities. A multidisciplinary approach with careful preoperative planning, exceptional surgical technique, and prolonged supervised physiotherapy are the keystones for a successful outcome.

Automatic classification of solitary pulmonary nodules in PET/CT imaging employing transfer learning techniques.

Early and automatic diagnosis of Solitary Pulmonary Nodules (SPN) in Computed Tomography (CT) chest scans can provide early treatment for patients with lung cancer, as well as doctor liberation from time-consuming procedures. The purpose of this study is the automatic and reliable characterization of SPNs in CT scans extracted from Positron Emission Tomography and Computer Tomography (PET/CT) system. To achieve the aforementioned task, Deep Learning with Convolutional Neural Networks (CNN) is applied. The strategy of training specific CNN architectures from scratch and the strategy of transfer learning, by utilizing state-of-the-art pre-trained CNNs, are compared and evaluated. To enhance the training sets, data augmentation is performed. The publicly available database of CT scans, named as Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), is also utilized to further expand the training set and is added to the PET/CT dataset. The results highlight the effectiveness of transfer learning and data augmentation for the classification task of small datasets. The best accuracy obtained on the PET/CT dataset reached 94%, utilizing a modification proposal of a state-of-the-art CNN, called VGG16, and enhancing the training set with LIDC-IDRI dataset. Besides, the proposed modification outperforms in terms of sensitivity several similar researches, which exploit the benefits of transfer learning. Overview of the experiment setup. The two datasets containing nodule representations are combined to evaluate the effectiveness of transfer learning over the traditional approach of training Convolutional Neural Networks from scratch.

Multi-input deep learning approach for Cardiovascular Disease diagnosis using Myocardial Perfusion Imaging and clinical data.

PURPOSE: Accurate detection and treatment of Coronary Artery Disease is mainly based on invasive Coronary Angiography, which could be avoided provided that a robust, non-invasive detection methodology emerged. Despite the progress of computational systems, this remains a challenging issue. The present research investigates Machine Learning and Deep Learning methods in competing with the medical experts' diagnostic yield. Although the highly accurate detection of Coronary Artery Disease, even from the experts, is presently implausible, developing Artificial Intelligence models to compete with the human eye and expertise is the first step towards a state-of-the-art Computer-Aided Diagnostic system. METHODS: A set of 566 patient samples is analysed. The dataset contains Polar Maps derived from scintigraphic Myocardial Perfusion Imaging studies, clinical data, and Coronary Angiography results. The latter is considered as reference standard. For the classification of the medical images, the InceptionV3 Convolutional Neural Network is employed, while, for the categorical and continuous features, Neural Networks and Random Forest classifier are proposed. RESULTS: The research suggests that an optimal strategy competing with the medical expert's accuracy involves a hybrid multi-input network composed of InceptionV3 and a Random Forest. This method matches the expert's accuracy, which is 79.15% in the particular dataset. CONCLUSION: Image classification using deep learning methods can cooperate with clinical data classification methods to enhance the robustness of the predicting model, aiming to compete with the medical expert's ability to identify Coronary Artery Disease subjects, from a large scale patient dataset.

Radiotherapy and Renal Cell Carcinoma: A Continuing Saga.

Renal cell carcinoma (RCC) is one of the most aggressive malignancies of the genito-urinary tract, having a poor prognosis especially in patients with metastasis. Surgical resection remains the gold standard for localized renal cancer disease, with radiotherapy (RT) receiving much skepticism during the last decades. However, many studies have evaluated the role of RT, and although renal cancer is traditionally considered radio-resistant, technological advances in the RT field with regards to modern linear accelerators, as well as advanced RT techniques have resulted in breakthrough therapeutic outcomes. Additionally, the combination of RT with immune checkpoint inhibitors and targeted agents may maximize the clinical benefit. This review article focuses on the role of RT in the therapeutic management of renal cell carcinoma.

Clinical Evaluation of a Three-Dimensional Internal Dosimetry Technique for Liver Radioembolization with 90Y Microspheres Using Dose Voxel Kernels.

Background: The purpose of this study was to develop a rapid, reliable, and efficient tool for three-dimensional (3D) dosimetry treatment planning and post-treatment evaluation of liver radioembolization with 90Y microspheres, using tissue-specific dose voxel kernels (DVKs) that can be used in everyday clinical practice. Materials and Methods: Two tissue-specific DVKs for 90Y were calculated through Monte Carlo (MC) simulations. DVKs for the liver and lungs were generated, and the dose distribution was compared with direct MC simulations. A method was developed to produce a 3D dose map by convolving the calculated DVKs with the activity biodistribution derived from clinical single-photon emission computed tomography (SPECT) or positron emission tomography (PET) images. Image registration for the SPECT or PET images with the corresponding computed tomography scans was performed before dosimetry calculation. The authors first compared the DVK convolution dosimetry with a direct full MC simulation on an XCAT anthropomorphic phantom. They then tested it in 25 individual clinical cases of patients who underwent 90Y therapy. All MC simulations were carried out using the GATE MC toolkit. Results: Comparison of the measured absorbed dose using tissue-specific DVKs and direct MC simulation on 25 patients revealed a mean difference of 1.07% ± 1.43% for the liver and 1.03% ± 1.21% for the tumor tissue, respectively. The largest difference between DVK convolution and full MC dosimetry was observed for the lung tissue (10.16% ± 1.20%). The DVK statistical uncertainty was <0.75% for both media. Conclusions: This semiautomatic algorithm is capable of performing rapid, accurate, and efficient 3D dosimetry. The proposed method considers tissue and activity heterogeneity using tissue-specific DVKs. Furthermore, this method provides results in <1 min, making it suitable for everyday clinical practice.

COVID-19 in the act: incidental 18F-FDG PET/CT findings in asymptomatic patients and those with symptoms not primarily correlated with COVID-19 during the United Kingdom coronavirus lockdown.

PURPOSE: The emergence of the novel SARS-CoV-2 pathogen and lethal COVID-19 disease pandemic poses major diagnostic challenges. The study aims to describe the spectrum and prevalence of thoracic and extrathoracic incidental findings in patients who have undergone 18F-FDG PET/CT during the first 3 weeks of the COVID-19 UK lockdown. METHODS: This is a single-centre retrospective controlled observational study. 18F-FDG PET/CT scans (n = 160) acquired from 23/3/2020 to 9/4/2020 were retrospectively reviewed for incidental findings in the lungs and extrapulmonary sites (heart, nasal sinuses, parotid and salivary glands, colon, large vessels, renal cortex, brain, spleen and testes). A date-matched control group (n = 205) of patients from 2019 was used for comparison. RESULTS: The total prevalence of suspicious findings was 26/160 (16.25%). Fifteen patients presented with incidental findings in the lungs, while eleven patients had only non-pulmonary incidental findings. There was a significant increase in the appearance of incidental 18F-FDG PET/CT findings during the 2nd week (OR = 3.8) and 3rd week (OR = 7.6) in relation to the 1st week. There was a significant increase in the average maximum standardised uptake values (SUVmax) in the parotid/salivary glands of patients scanned during week 2 in relation to week 1 (p = 0.036). There was no significant difference in the prevalence of incidental findings compared to the control group, but the number of pulmonary vs. extrathoracic findings was different between the two populations. CONCLUSION: The study provides a novel base of evidence to identify asymptomatic patients and those without symptoms strongly associated with COVID-19 with incidental 18F-FDG PET/CT findings suspicious of SARS-CoV-2 infection during the initial stages of the pandemic.

90Y-microsphere radioembolization: The method, clinical evidence and perspective.

The current review unfolds the procedural steps and the clinical evidence for yttrium-90 (90Y)-microspheres radioembolization. Radioembolization is part of the loco-regional therapeutic spectrum for liver malignancy and involves the invasive, intra-arterial delivery of microspheres carrying ß-emitter isotopes in order to destroy cancerous tissue via ionizing radiation. The main steps of the therapeutic process are selection of eligible patients, angiographic workup, simulation scintigraphy, pre-treatment dosimetry, actual treatment and post-treatment imaging/dosimetry. Radioembolization is routinely applied in advanced stage hepatocellular carcinoma (HCC), yet its role is being investigated even in earlier stages. Prospective, randomized controlled trials did not verify increased overall survival of radioembolization over systemic treatment with sorafenib in HCC; however, it showed survival benefit in certain sub-groups and a favorable toxicity profile with fewer adverse events. Radioembolization is also applied in metastatic colon cancer showing tumoral liver responses, which however did not translate into an overall survival benefit. Data regarding applications of this method in other neoplasms, such as neuroendocrine tumors, breast cancer and melanoma are also presented. There are ongoing clinical trials to define the role of radioembolization within recent treatments algorithms, to determine optimal combinations of this treatment with systemic and targeted therapies and to decide the patients' sub-groups, who will benefit the most.

Adult Medulloblastoma Depicted With 99mTc-HYNIC-[D-Phe1, Tyr3-Octreotide] SPECT/CT.

Although medulloblastoma constitutes the second most common malignant brain tumor in children, it encountered very rarely in adults. The incidence of adult medulloblastoma is approximately 0.6 to 1 case per million. It accounts for less than 1% of adult brain tumors and is located most commonly at the posterior cranial fossa. We present an extremely rare case of a 67-year-old man with cerebellar medulloblastoma with residual disease after surgery, chemotherapy, and radiation therapy, depicted with Tc-HYNIC-[D-Phe, Tyr-Octreotide] SPECT/CT. Somatostatin receptor scintigraphy was performed for staging and for the possibility of treatment with somatostatin analogs or peptide radionuclide therapy.