Tentative diagnosis of meniscal pathology is very helpful for the successful operative management including arthroscopy. Conventional arthrography has long been used to meet this purpose, but it has some limitations due to complex views and some difficulties in imaging the whole anatomical structures. Recent diagnostic advancement produced post-arthrographic HRCT and MRI for the compensation of these problems of conventional arthrography. Arthrography and post-arthrographic HRCT has been carried out on 49 knees in 43 patients. 38 knees among them could be confirmed by arthroscopy and following results were obtained. 1. Diagnostic accuracies of post-arthrographic HRCT for medial and lateral meniscal injuries of knee were 94.7% and 92.1% repectively. 2. Shape, contour of torn meniscus and the relationships between the torn fragments were better demonstrated with HRCT. 3. Horizontal tear could also be suspected by HRCT. 4. Peripheral detachment could be differentiated with synovial recess on the HRCT, especially in the reformat view, but was more clearly visible in arthrography. 5. Arthrography and HRCT can be used as a good complementary study for the evaluation of meniscal injuries.
Arthrography ; Arthroscopy ; Compensation and Redress ; Diagnosis ; Humans ; Knee ; Magnetic Resonance Imaging ; Pathology ; Tears

No abstract available.
Spine*

No abstract available.
Debridement* ; Knee* ; Osteoarthritis* ; Osteotomy*

No abstract available.
Intervertebral Disc* ; Zygapophyseal Joint*

No abstract available.
Catheters ; Humans ; Stroke

OBJECTIVE: We describe a systematic review and meta-analysis of the performance of 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) for detecting metastasis in ovarian cancer. METHODS: MEDLINE and Embase were searched for diagnostic accuracy studies that used 18F-FDG PET or PET/CT for pre-treatment staging, using surgical findings as the reference standard. Sensitivities and specificities were pooled and plotted in a hierarchic summary receiver operating characteristic plot. Potential causes of heterogeneity were explored through sensitivity analyses. RESULTS: Eight studies with 594 patients were included. The overall pooled sensitivity and specificity for metastasis were 0.72 (95% confidence interval [CI]=0.61–0.81) and 0.93 (95% CI=0.85–0.97), respectively. There was considerable heterogeneity in sensitivity (I2=97.57%) and specificity (I2=96.74%). In sensitivity analyses, studies that used laparotomy as the reference standard showed significantly higher sensitivity and specificity (0.77; 95% CI=0.67–0.87 and 0.96; 95% CI=0.92–0.99, respectively) than those including diagnostic laparoscopy (0.62; 95% CI=0.46–0.77 and 0.84; 95% CI=0.69–0.99, respectively). Higher specificity was shown in studies that confirmed surgical findings by pathologic evaluation (0.95; 95% CI=0.90–0.99) than in a study without pathologic confirmation (0.69; 95% CI=0.24–1.00). Studies with a lower prevalence of the FDG-avid subtype showed higher specificity (0.97; 95% CI=0.94–1.00) than those with a greater prevalence (0.89; 95% CI=0.80–0.97). CONCLUSION: Pre-treatment 18F-FDG PET/CT shows moderate sensitivity and high specificity for detecting metastasis in ovarian cancer. With its low false-positive rate, it can help select surgical approaches or alternative treatment options.
Electrons* ; Fluorodeoxyglucose F18 ; Humans ; Laparoscopy ; Laparotomy ; Neoplasm Metastasis* ; Ovarian Neoplasms* ; Population Characteristics ; Positron-Emission Tomography ; Positron-Emission Tomography and Computed Tomography ; Prevalence ; ROC Curve ; Sensitivity and Specificity

No abstract available.
Methylmethacrylate*

In aging societies, incidences of neurodegenerative diseases such as Alzheimer’s disease and Parkinson’s disease are increasing. Neurodegenerative diseases are bringing main challenges to the healthcare system in today’s world. Analyzing characteristic imaging patterns of patients with neurodegenerative diseases is important. Since objective and reliable imaging assessments and precise analyses can lead to early diagnosis of neurodegenerative diseases, imaging patterns are being increasingly investigated. Artificial intelligence (AI) analyzing brain MRI has been applied to neurodegenerative diseases, providing added value in early diagnosis. MRI-based AI software has been developed and studied worldwide, with some AI-based software already being used in actual clinical care. Currently, there are MRI-based volumetry and segmentation software available. There is also an unmet demand for the application of AI in neurodegenerative diseases. Here, we review current status and unmet needs for application of AI in neurodegenerative diseases. We also discuss current limitations of AI, suggestion for AI-based software, and how it can be clinically applied in the future.

Objective: We aimed to evaluate the reporting quality of research articles that applied deep learning to medical imaging.Using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) guidelines and a journal with prominence in Asia as a sample, we intended to provide an insight into reporting quality in the Asian region and establish a journal-specific audit. Materials and Methods: A total of 38 articles published in the Korean Journal of Radiology between June 2018 and January 2023 were analyzed. The analysis included calculating the percentage of studies that adhered to each CLAIM item and identifying items that were met by ≤ 50% of the studies. The article review was initially conducted independently by two reviewers, and the consensus results were used for the final analysis. We also compared adherence rates to CLAIM before and after December 2020. Results: Of the 42 items in the CLAIM guidelines, 12 items (29%) were satisfied by ≤ 50% of the included articles. None of the studies reported handling missing data (item #13). Only one study respectively presented the use of de-identification methods (#12), intended sample size (#19), robustness or sensitivity analysis (#30), and full study protocol (#41). Of the studies, 35% reported the selection of data subsets (#10), 40% reported registration information (#40), and 50% measured inter and intrarater variability (#18). No significant changes were observed in the rates of adherence to these 12 items before and after December 2020. Conclusion The reporting quality of artificial intelligence studies according to CLAIM guidelines, in our study sample, showed room for improvement. We recommend that the authors and reviewers have a solid understanding of the relevant reporting guidelines and ensure that the essential elements are adequately reported when writing and reviewing the manuscripts for publication.

Histiocytic sarcoma is a type of lymphoma that rarely involves the central nervous system (CNS). Its rarity can easily lead to a misdiagnosis. We describe a patient with primary CNS histocytic sarcoma involving the cerebral hemisphere and spinal cord, who had been initially misdiagnosed as demyelinating disease. Two biopsies were necessary before a correct diagnosis was made. A histologic examination showed bizarre shaped histiocytes with larger nuclei and nuclear atypia. The cells were positive for CD68, CD163, and S-100 protein. As a resection was not feasible due to multifocality, he was treated with highdose methotrexate, but showed no response. As a result, he was switched to high dose cytarabine; but again, showed no response. The patient died 2 months from the start of chemotherapy and 8 months from the onset of symptoms. Since few patients with this condition have been described and histopathology is difficult to diagnose, suspicion of the disease is essential.
Biopsy ; Central Nervous System* ; Cerebrum ; Cytarabine ; Demyelinating Diseases ; Diagnosis ; Diagnostic Errors ; Drug Therapy ; Histiocytes ; Histiocytic Sarcoma* ; Humans ; Lymphoma ; Methotrexate ; S100 Proteins ; Sarcoma ; Spinal Cord