Background: Radiofrequency (RF) is a therapeutic modality for reducing the volume of large benign thyroid nodules. If thermal therapies are interpreted as an alternative strategy to surgery, critical issues in their use are represented by the extent of nodule reduction and by the durability of nodule reduction over a long period of time.
Objective: To assess the ability of machine learning to discriminate nodules with volume reduction rate (VRR) < or ≥50% at 12 months following RF treatment.
Background The purpose of this study was to confirm the generalisation of radiofrequency ablation (RFA) in the treatment of benign thyroid nodules (BTN) and to look for a correlation between final shrinkage and some ultrasound (US) findings in a large Italian population data set. Methods This prospective study included 337 patients with solid cold BTN from six Italian institutions. Nodule volume, US pattern, thyroid function, symptom/cosmetic scores and complications were evaluated before treatment and at 6 and 12 months.
View Article and Find Full Text PDFFocal liver lesions (FLLs) are frequently discovered during ultrasound examinations either in healthy subjects without a clinical history of cancer or during staging or follow-up procedures in oncologic patients or in routine surveillance of hepatopathic patients. In oncologic patients, the liver is the most common target of metastatic disease and accurate detection and characterisation of FLLs is prognostically fundamental during the initial staging as well as before and after pre-operative chemotherapy, as it can help to identify patients who are most likely to benefit from liver surgery. Moreover, early detection of primary or secondary liver malignancies increases the possibility of curative surgical resection or successful percutaneous ablation.
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