Park CW, et al. J Korean Med Sci. 2020 Oct;35(42):e379. https://doi.org/10.3346/jkms.2020.35.e379

Purpose: To compare the efficacy and complication rates of radiofrequency ablation (RFA) and repeat surgery in the treatment of locally recurrent thyroid cancers. Materials and methods: A total of 221 patients with locally recurrent thyroid cancers who underwent either RFA (n = 96) or repeat surgery (n = 125) between March 2008 and March 2017 were retrospectively enrolled (range of follow-up, 1–10 years). Each cohort consisted of 70 patients after propensity score adjustment. Patients with more than three recurrent lesions were excluded. The primary and secondary end points were recurrence-free survival and complication rates, respectively. Recurrence-free survival curves were compared via the log-rank test. The complications—voice changes, hypocalcemia, and immediate procedural complications—were compared between the groups. In addition, pretreatment serum thyroglobulin (T_g) levels and those at the last follow-up were also compared between the two groups to examine therapeutic efficacy. Results: After propensity score matching, both groups showed no significant differences in baseline characteristics. The recurrence-free survival rates were comparable between the RFA and surgery groups (p = .2). There were no significant differences in mean serum T_g levels and their mean decrease after treatment between the groups (p = .891 and p = .963, respectively). Immediate procedural complications and voice changes also showed no significant between-group differences (p = .316, p = .084, respectively). Hypocalcemia occurred only in the repeat surgery group (n = 18). Overall complications were significantly more frequent in the repeat surgery group (RFA, n = 7; surgery, n = 27; p < .001). Conclusion: RFA may be an effective and safe alternative to repeat surgery in the treatment of a small number of locally recurrent thyroid cancers.

The χ-separation imaging findings helped differentiate multiple sclerosis from neuromyelitis optica spectrum disorder, potentially related to histopathologic differences in the brain lesions betwee... Background Use of χ-separation imaging can provide surrogates for iron and myelin that relate closely to abnormal changes in multiple sclerosis (MS) lesions. Purpose To evaluate the appearances of ...

Jang J, et al. J Clin Neurol. 2020 Oct;16(4):562-572. https://doi.org/10.3988/jcn.2020.16.4.562

In MRI, deep neural networks have been proposed to reconstruct diffusion model parameters. However, the inputs of the networks were designed for a specific diffusion gradient scheme (i.e., diffusion gradient directions and numbers) and a specific b-value that are the same as the training data. In this study, a new deep neural network, referred to as DIFFnet, is developed to function as a generalized reconstruction tool of the diffusion-weighted signals for various gradient schemes and b-values. For generalization, diffusion signals are normalized in a q-space and then projected and quantized, producing a matrix (Qmatrix) as an input for the network. To demonstrate the validity of this approach, DIFFnet is evaluated for diffusion tensor imaging (DIFFnet_DTI) and for neurite orientation dispersion and density imaging (DIFFnet_NODDI). In each model, two datasets with different gradient schemes and b-values are tested. The results demonstrate accurate reconstruction of the diffusion parameters at substantially reduced processing time (approximately 8.7 times and 2240 times faster processing time than conventional methods in DTI and NODDI, respectively; less than 4% mean normalized root-mean-square errors (NRMSE) in DTI and less than 8% in NODDI). The generalization capability of the networks was further validated using reduced numbers of diffusion signals from the datasets and a public dataset from Human Connection Project. Different from previously proposed deep neural networks, DIFFnet does not require any specific gradient scheme and b-value for its input. As a result, it can be adopted as an online reconstruction tool for various complex diffusion imaging.