Transforaminal endoscopic decompression for thoracic spinal stenosis under local anesthesia.

BACKGROUND: Thoracic spinal stenosis is a common vertebral degenerative disease, and treatment remains challenging. In recent years, transforaminal endoscopic decompression has been widely used for treating lumbar degenerative diseases. However, the efficacy of this procedure for thoracic spinal stenosis has yet to be established. Herein, we report a case of thoracic spinal stenosis treated with transforaminal endoscopic decompression under local anesthesia. CASE REPORT: An 88-year-old man presented with a 1-month history of progressive paralysis and dysesthesia in the bilateral lower extremities. A diagnosis of thoracic spinal stenosis was made, based on physical examination. A two-step percutaneous transforaminal endoscopic thoracic decompression was performed for spinal canal decompression. Over a follow-up of 1 year, a favorable outcome was noted. CONCLUSION: Transforaminal endoscopic decompression is a safe and an effective surgical approach for the treatment of thoracic spinal stenosis. For patients with thoracic spinal stenosis, accurate diagnosis and elaborate surgical planning should be highlighted, and the surgical outcome can be favorable.

What benefit can be obtained from magnetic resonance imaging diagnosis with artificial intelligence in prostate cancer compared with clinical assessments?

The present study aimed to explore the potential of artificial intelligence (AI) methodology based on magnetic resonance (MR) images to aid in the management of prostate cancer (PCa). To this end, we reviewed and summarized the studies comparing the diagnostic and predictive performance for PCa between AI and common clinical assessment methods based on MR images and/or clinical characteristics, thereby investigating whether AI methods are generally superior to common clinical assessment methods for the diagnosis and prediction fields of PCa. First, we found that, in the included studies of the present study, AI methods were generally equal to or better than the clinical assessment methods for the risk assessment of PCa, such as risk stratification of prostate lesions and the prediction of therapeutic outcomes or PCa progression. In particular, for the diagnosis of clinically significant PCa, the AI methods achieved a higher summary receiver operator characteristic curve (SROC-AUC) than that of the clinical assessment methods (0.87 vs. 0.82). For the prediction of adverse pathology, the AI methods also achieved a higher SROC-AUC than that of the clinical assessment methods (0.86 vs. 0.75). Second, as revealed by the radiomics quality score (RQS), the studies included in the present study presented a relatively high total average RQS of 15.2 (11.0-20.0). Further, the scores of the individual RQS elements implied that the AI models in these studies were constructed with relatively perfect and standard radiomics processes, but the exact generalizability and clinical practicality of the AI models should be further validated using higher levels of evidence, such as prospective studies and open-testing datasets.