IMPORTANCE Carbon dioxide laser tonsillotomy performed under local anesthesia may be an effective and less invasive alternative than dissection tonsillectomy for treatment of tonsil-related... Show moreIMPORTANCE Carbon dioxide laser tonsillotomy performed under local anesthesia may be an effective and less invasive alternative than dissection tonsillectomy for treatment of tonsil-related afflictions.OBJECTIVE To compare functional recovery and symptom relief among adults undergoing tonsillectomy or tonsillotomy.DESIGN, SETTING, AND PARTICIPANTS This randomized clinical trial was conducted at 5 secondary and tertiary hospitals in the Netherlands from January 2018 to December 2019. Participants were 199 adult patients with an indication for surgical tonsil removal randomly assigned to either the tonsillectomy or tonsillotomy group.INTERVENTIONS For tonsillotomy, the crypts of the palatine tonsil were evaporated using a carbon dioxide laser under local anesthesia, whereas tonsillectomy consisted of total tonsil removal performed under general anesthesia.MAIN OUTCOMES AND MEASURES The primary outcome was time to functional recovery measured within 2 weeks after surgery assessed for a modified intention-to-treat population. Secondary outcomes were time to return to work after surgery, resolution of primary symptoms, severity of remaining symptoms, surgical complications, postoperative pain and analgesics use, and overall patient satisfaction assessed for the intention-to-treat population.RESULTS Of 199 patients (139 (70%] female; mean [SD] age, 29 [9] years), 98 were randomly assigned to tonsillotomy and 101v were randomly assigned to tonsillectomy. Recovery within 2 weeks after surgery was significantly shorter after tonsillotomy than after tonsillectomy (hazard ratio for recovery after tonsillectomy vs tonsillotomy, 0.3; 95% CI, 0.2-0.5). Two weeks after surgery, 72 (77%) patients in the tonsillotomy group were fully recovered compared with 26 (57%) patients in the tonsillectomy group. Time until return to work within 2 weeks was also shorter after tonsillotomy (median [IQR], 4.5 [3.0-7.0] days vs 12.0 [9.0-14.0] days; hazard ratio for return after tonsillectomy vs tonsillotomy, 0.3; 95% CI, 0.2-0.4.). Postoperative hemorrhage occurred in 2 patients (2%) in the tonsillotomy group and 8 patients (12%) in the tonsillectomy group. At 6 months after surgery, fewer patients in the tonsillectomy group (25; 35%) than in the tonsillotomy group (54; 57%) experienced persistent symptoms (difference of 22%; 95% CI, 7%-37%). Most patients with persistent symptoms in both the tonsillotomy (32 of 54; 59%) and tonsillectomy (16 of 25; 64%) groups reported mild symptoms 6 months after surgery.CONCLUSIONS AND RELEVANCE This randomized clinical trial found that compared with tonsillectomy performed under general anesthesia, laser tonsillotomy performed under local anesthesia had a significantly shorter and less painful recovery period. A higher percentage of patients had persistent symptoms after tonsillotomy, although the intensity of these symptoms was lower than before surgery. These results suggest that laser tonsillotomy performed under local anesthesia may be a feasible alternative to conventional tonsillectomy in this population. Show less
Lubbe, M.F.J.A. van der; Vaidyanathan, A.; Wit, M. de; Burg, E.L. van den; Postma, A.A.; Bruintjes, T.D.; ... ; Berg, R. van de 2021
Purpose This study investigated the feasibility of a new image analysis technique (radiomics) on conventional MRI for the computer-aided diagnosis of Meniere's disease. Materials and methods A... Show morePurpose This study investigated the feasibility of a new image analysis technique (radiomics) on conventional MRI for the computer-aided diagnosis of Meniere's disease. Materials and methods A retrospective, multicentric diagnostic case-control study was performed. This study included 120 patients with unilateral or bilateral Meniere's disease and 140 controls from four centers in the Netherlands and Belgium. Multiple radiomic features were extracted from conventional MRI scans and used to train a machine learning-based, multi-layer perceptron classification model to distinguish patients with Meniere's disease from controls. The primary outcomes were accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the classification model. Results The classification accuracy of the machine learning model on the test set was 82%, with a sensitivity of 83%, and a specificity of 82%. The positive and negative predictive values were 71%, and 90%, respectively. Conclusion The multi-layer perceptron classification model yielded a precise, high-diagnostic performance in identifying patients with Meniere's disease based on radiomic features extracted from conventional T2-weighted MRI scans. In the future, radiomics might serve as a fast and noninvasive decision support system, next to clinical evaluation in the diagnosis of Meniere's disease. Show less
Vaidyanathan, A.; Lubbe, M.F.J.A. van der; Leijenaar, R.T.H.; Hoof, M. van; Zerka, F.; Miraglio, B.; ... ; Lambin, P. 2021
Segmentation of anatomical structures is valuable in a variety of tasks, including 3D visualization, surgical planning, and quantitative image analysis. Manual segmentation is time-consuming and... Show moreSegmentation of anatomical structures is valuable in a variety of tasks, including 3D visualization, surgical planning, and quantitative image analysis. Manual segmentation is time-consuming and deals with intra and inter-observer variability. To develop a deep-learning approach for the fully automated segmentation of the inner ear in MRI, a 3D U-net was trained on 944 MRI scans with manually segmented inner ears as reference standard. The model was validated on an independent, multicentric dataset consisting of 177 MRI scans from three different centers. The model was also evaluated on a clinical validation set containing eight MRI scans with severe changes in the morphology of the labyrinth. The 3D U-net model showed precise Dice Similarity Coefficient scores (mean DSC-0.8790) with a high True Positive Rate (91.5%) and low False Discovery Rate and False Negative Rates (14.8% and 8.49% respectively) across images from three different centers. The model proved to perform well with a DSC of 0.8768 on the clinical validation dataset. The proposed auto-segmentation model is equivalent to human readers and is a reliable, consistent, and efficient method for inner ear segmentation, which can be used in a variety of clinical applications such as surgical planning and quantitative image analysis. Show less
