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. The primary outcome was to find a correlation between basal volume and US pattern of the nodules and final shrinkage. The secondary outcome was to confirm the efficacy and safety of RFA in a large data set.
Results
The median basal volume was 20.7 mL, and this significantly decreased after RFA at 6 months (7.3 mL (−63.5%), P < 0.001) and at 12 months (6 mL (−70%), P vs 6 months = 0.009). A significant correlation was found for US structure (a spongiform pattern showing a 76% reduction vs 67 and 66% of mix and solid patterns respectively, P < 0.01) as well as for vascularity (intense peripheral and intranodal patterns showing 71 vs 68 and 67% of weak peripheral and intranodal and peripheral patterns respectively, P < 0.03), but not for macrocalcifications. A slight inverse correlation was found between nodule basal volume and shrinkage (Spearman: −0.23). Mean symptoms/cosmetic scores were significantly reduced. No major complications were encountered.
Conclusions
This multicentre study validated the efficacy and safety of RFA for treating BTN and showed a clear correlation between final shrinkage and some common US findings.
MR imaging with its multiplanar capabilities and high-contrast resolution has a high level of accuracy in characterising elastofibroma dorsi and may avoid the need for biopsy or surgical operation.
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. Methods: A machine learning model was trained with a dataset of 402 cytologically benign thyroid nodules subjected to RF at six Italian Institutions. The model was trained with the following variables: baseline nodule volume, echostructure, macrocalcalcifications, vascularity, and 12-month VRR. Results: After training, the model could distinguish between nodules having VRR < 50% from those having VRR ≥50% in 85% of cases (accuracy: 0.85; 95% confidence interval [CI]: 0.80-0.90; sensitivity: 0.70; 95% CI: 0.62-0.75; specificity: 0.99; 95% CI: 0.98-1.0; positive predictive value: 0.95; 95% CI: 0.92-0.98; negative predictive value: 0.95; 95% CI: 0.92-0.98). Conclusions: This study demonstrates that a machine learning model can reliably identify those nodules that will have VRR < or ≥50% at 12 months after one RF treatment session. Predicting which nodules will be poor or good responders represents valuable data that may help physicians and patients decide on the best treatment option between thermal ablation and surgery or in predicting if more than one session might be necessary to obtain a significant volume reduction.
Preoperative CT and MRI assessment of patients with sensorineural hearing loss is reliable. MRI provided additional information, identifying the possible absence of cochlear nerve and excluding other central nervous system (CNS) diseases.
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