Lymph Node Staging with Choline PET/CT in Patients with Prostate Cancer: A Review

Due to its prevalence, prostate cancer represents a serious health problem. The treatment, when required, may be local in case of limited disease, locoregional if lymph nodes are involved, and systemic when distant metastases are present. In order to choose the best treatment regimen, an accurate disease staging is mandatory. However, the accuracy of conventional imaging modalities in detecting lymph node and bone metastases is low. In the last decade, molecular imaging, particularly, choline PET-CT has been evaluated in this setting. Choline PET represents the more accurate exam to stage high-risk prostate cancer, and it is useful in staging patients with biochemical relapse, in particular when PSA kinetics is high and/or PSA levels are more than 2 pg/ml. The present paper reports results of available papers on these issues, with particular attention to lymph node staging

Fluorodeoxyglucose-positron emission tomography/computed tomography in the staging and evaluation of treatment response in a patient with Castleman's disease: a case report

<p>Abstract</p> <p>Introduction</p> <p>Castleman's disease is a rare lymphatic polyclonal disorder that is characterised by unicentric or multicentric lymph node hyperplasia and non-specific symptoms and signs including fever, asthenia, weight loss, enlarged liver and abnormally high blood levels of antibodies.</p> <p>Case presentation</p> <p>We present the case of a 74-year-old man with Castleman's disease. The disease was detected with a contrast-enhanced computed tomography (CT) scan and a fluorodeoxyglucose (FDG)-positron emission tomography (PET)/CT study; diagnosis was made with histopathology. After treatment with surgical excision followed by chemotherapy, the disease response was evaluated using both diagnostic techniques. However, only the PET study was able to identify the spread of the disease to the abdominal lymph nodes, which were both enlarged and normal size, and, after treatment, to evaluate the disease response.</p> <p>Conclusion</p> <p>Based on the results of previous case reports and on those of the present study, it seems that Castleman's disease has a high glucose metabolic activity. Therefore, the use of PET can be considered appropriate in order to stage or restage the disease and to evaluate the response of the disease to treatment.</p

a simple and accurate dosimetry protocol to estimate activity for hyperthyroidism treatment

BACKGROUND: Aim of the study was to evaluate accuracy of different dosimetry protocols in estimating the required 131I activity to treat hyperthyroid patients. MATERIALS AND METHODS: Forty consecutive patients were analysed: twenty-eight Graves' disease; twelve autonomous thyroid nodule (ATN). Maximum-uptake, effective half-time and residence-time were estimated from Radioiodine Uptake Test. Residence-time was estimated using a bi-compartmental model. For 131I activity calculation, algorithms laid down in European Association of Nuclear Medicine (EANM) guidelines, ICRP 53 approach and a mono-exponential formula (ME), were compared with OLINDA/EXM results. RESULTS: Based on EANM guidelines, activities to be administered were 3% higher in Graves' disease (p = 0.001) and 3% higher in ATN (p = 0.046). Calculated activities using ICRP 53 approach were significantly lower compared to OLINDA/EXM: 33% in Graves' disease; 17% in ATN. Activities recommended by ME, were significantly higher: in Graves' disease 20%; 42% in ATN. CONCLUSIONS: Only EANM algorithm predict quite well, compared to OLINDA/EXM, the required activity to treat hyperthyroid patients

Role of fluorine-18 fluorodeoxyglucose PET/CT in head and neck oncology: the point of view of the radiation oncologist

Squamous cell carcinoma is the most common malignant tumour of the head and neck. The initial TNM staging, the evaluation of the tumour response during treatment, and the long-term surveillance are crucial moments in the approach to head and neck squamous cell carcinoma (HNSCC). Thus, at each of these moments, the choice of the best diagnostic tool providing the more precise and larger information is crucial. Positron emission tomography with fluorine-18 fludeoxyglucose integrated with CT (F-18-FDG-PET/CT) rapidly gained clinical acceptance, and it has become an important imaging tool in routine clinical oncology. However, controversial data are currently available, for example, on the role of F-18-FDG-PET/CT imaging during radiotherapy planning, the prognostic value or its real clinical impact on treatment decisions. In this article, the role of F-18-FDG-PET/CT imaging in HNSCC during pre-treatment staging, radiotherapy planning, treatment response assessment, prognosis and follow-up is reviewed focusing on current evidence and controversial issues. A proposal on how to integrate F-18-FDG-PET/CT in daily clinical practice is also described

Computer-aided diagnosis for (123I)FP-CIT imaging: impact on clinical reporting

BACKGROUND: For (123I)FP-CIT imaging, a number of algorithms have shown high performance in distinguishing normal patient images from those with disease, but none have yet been tested as part of reporting workflows. This study aims to evaluate the impact on reporters' performance of a computer-aided diagnosis (CADx) tool developed from established machine learning technology. Three experienced (123I)FP-CIT reporters (two radiologists and one clinical scientist) were asked to visually score 155 reconstructed clinical and research images on a 5-point diagnostic confidence scale (read 1). Once completed, the process was then repeated (read 2). Immediately after submitting each image score for a second time, the CADx system output was displayed to reporters alongside the image data. With this information available, the reporters submitted a score for the third time (read 3). Comparisons between reads 1 and 2 provided evidence of intra-operator reliability, and differences between reads 2 and 3 showed the impact of the CADx. RESULTS: The performance of all reporters demonstrated a degree of variability when analysing images through visual analysis alone. However, inclusion of CADx improved consistency between reporters, for both clinical and research data. The introduction of CADx increased the accuracy of the radiologists when reporting (unfamiliar) research images but had less impact on the clinical scientist and caused no significant change in accuracy for the clinical data. CONCLUSIONS: The outcomes for this study indicate the value of CADx as a diagnostic aid in the clinic and encourage future development for more refined incorporation into clinical practice

Comparison of machine learning and semi-quantification algorithms for (I123)FP-CIT classification: the beginning of the end for semi-quantification?

Background Semi-quantification methods are well established in the clinic for assisted reporting of (I123) Ioflupane images. Arguably, these are limited diagnostic tools. Recent research has demonstrated the potential for improved classification performance offered by machine learning algorithms. A direct comparison between methods is required to establish whether a move towards widespread clinical adoption of machine learning algorithms is justified. This study compared three machine learning algorithms with that of a range of semi-quantification methods, using the Parkinson’s Progression Markers Initiative (PPMI) research database and a locally derived clinical database for validation. Machine learning algorithms were based on support vector machine classifiers with three different sets of features: Voxel intensities Principal components of image voxel intensities Striatal binding radios from the putamen and caudate. Semi-quantification methods were based on striatal binding ratios (SBRs) from both putamina, with and without consideration of the caudates. Normal limits for the SBRs were defined through four different methods: Minimum of age-matched controls Mean minus 1/1.5/2 standard deviations from age-matched controls Linear regression of normal patient data against age (minus 1/1.5/2 standard errors) Selection of the optimum operating point on the receiver operator characteristic curve from normal and abnormal training data Each machine learning and semi-quantification technique was evaluated with stratified, nested 10-fold cross-validation, repeated 10 times. Results The mean accuracy of the semi-quantitative methods for classification of local data into Parkinsonian and non-Parkinsonian groups varied from 0.78 to 0.87, contrasting with 0.89 to 0.95 for classifying PPMI data into healthy controls and Parkinson’s disease groups. The machine learning algorithms gave mean accuracies between 0.88 to 0.92 and 0.95 to 0.97 for local and PPMI data respectively. Conclusions Classification performance was lower for the local database than the research database for both semi-quantitative and machine learning algorithms. However, for both databases, the machine learning methods generated equal or higher mean accuracies (with lower variance) than any of the semi-quantification approaches. The gain in performance from using machine learning algorithms as compared to semi-quantification was relatively small and may be insufficient, when considered in isolation, to offer significant advantages in the clinical context