Dual time-point FDG PET/CT for differentiating benign from malignant solitary pulmonary nodules in a TB endemic area

Objective. Fluorodeoxyglucose (FDG)-positron emission tomography (PET) is an accurate non-invasive imaging test for differentiating benign from malignant solitary pulmonary nodules (SPNs). We aimed to assess its diagnostic accuracy for differentiating benign from malignant SPNs in a tuberculosis (TB)-endemic area. Methods. Thirty patients, 22 men and 8 women, mean age 60 years, underwent dual time point FDG-PET/computed tomography (CT) imaging, followed by histological examination of the SPN. Maximum standard uptake values (SUVmax) with the greatest uptake in the lesion were calculated for two time points (SUV1 and SUV2), and the percentage change over time per lesion was calculated (%DSUV). Routine histological findings served as the gold standard. Results. Histological examination showed that 14 lesions were malignant and 16 benign, 12 of which were TB. SUVmax for benign and malignant lesions were 11.02 (standard deviation (SD) 6.6) v. 10.86 (SD 8.9); however, when tuberculomas were excluded from the analysis, a significant difference in mean SUV1max values between benign and malignant lesions was observed (p=0.0059). Using an SUVmax cut-off value of 2.5, a sensitivity of 85.7% and a specificity of 25% was obtained. Omitting the TB patients from analysis resulted in a sensitivity of 85.7% and a specificity of 100%. Mean %DSUV of benign lesions did not differ significantly from mean %DSUV of malignant lesions (17.1% (SD 16.3%) v. 19.4% (SD 23.7%)). Using a cut-off of %DSUV >10% as indicative of malignancy, a sensitivity of 85.7% and a specificity of 50% was obtained. Omitting the TB patients from the analysis yielded a sensitivity of 85.7% and a specificity of 75%. Conclusion. Our findings suggest that FDG-PET cannot distinguish malignancy from tuberculoma and therefore cannot reliably be used to reduce futile biopsy/thoracotomy

Development of a clinical decision model for thyroid nodules

<p>Abstract</p> <p>Background</p> <p>Thyroid nodules represent a common problem brought to medical attention. Four to seven percent of the United States adult population (10–18 million people) has a palpable thyroid nodule, however the majority (>95%) of thyroid nodules are benign. While, fine needle aspiration remains the most cost effective and accurate diagnostic tool for thyroid nodules in current practice, over 20% of patients undergoing FNA of a thyroid nodule have indeterminate cytology (follicular neoplasm) with associated malignancy risk prevalence of 20–30%. These patients require thyroid lobectomy/isthmusectomy purely for the purpose of attaining a definitive diagnosis. Given that the majority (70–80%) of these patients have benign surgical pathology, thyroidectomy in these patients is conducted principally with diagnostic intent. Clinical models predictive of malignancy risk are needed to support treatment decisions in patients with thyroid nodules in order to reduce morbidity associated with unnecessary diagnostic surgery.</p> <p>Methods</p> <p>Data were analyzed from a completed prospective cohort trial conducted over a 4-year period involving 216 patients with thyroid nodules undergoing ultrasound (US), electrical impedance scanning (EIS) and fine needle aspiration cytology (FNA) prior to thyroidectomy. A Bayesian model was designed to predict malignancy in thyroid nodules based on multivariate dependence relationships between independent covariates. Ten-fold cross-validation was performed to estimate classifier error wherein the data set was randomized into ten separate and unique train and test sets consisting of a training set (90% of records) and a test set (10% of records). A receiver-operating-characteristics (ROC) curve of these predictions and area under the curve (AUC) were calculated to determine model robustness for predicting malignancy in thyroid nodules.</p> <p>Results</p> <p>Thyroid nodule size, FNA cytology, US and EIS characteristics were highly predictive of malignancy. Cross validation of the model created with Bayesian Network Analysis effectively predicted malignancy [AUC = 0.88 (95%CI: 0.82–0.94)] in thyroid nodules. The positive and negative predictive values of the model are 83% (95%CI: 76%–91%) and 79% (95%CI: 72%–86%), respectively.</p> <p>Conclusion</p> <p>An integrated predictive decision model using Bayesian inference incorporating readily obtainable thyroid nodule measures is clinically relevant, as it effectively predicts malignancy in thyroid nodules. This model warrants further validation testing in prospective clinical trials.</p

Nuclear Medicine in South Africa.

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A case for the provision of positron emission tomography (PET) in South African public hospitals: nuclear medicine

No Abstract. South African Medical Journal Vol. 96(7) 2006: 598-60