Primary spinal cord oligodendrogliomas are rare tumors comprising two percent of all spinal cord tumors. Although a treatment guideline has yet to be established, maximal surgical resection is primary in the treatment of spinal cord oligodendrogliomas. Adjuvant radiotherapy has remained controversial, and it is unclear whether chemotherapy adds any benefit. In this case report, the authors present a 24-year-old male who had a seven-year history of left leg weakness and a radiating pain in both legs. Magnetic resonance image (MRI) showed an intramedullary mass at the T4-T8 level. He underwent subtotal removal of the tumor and pathologic diagnosis revealed a WHO grade II oligodendroglioma. The patient was treated with radiotherapy postoperatively and followed up with MRI annually. Clinical and radiological status of the patient had been stationary for four years after the surgery. The five-year follow-up MRI showed an increase in the size and extent of the residual tumor. Despite radiological progression, considering that symptoms and the performance status of the patient had remained unchanged, further treatment has not been performed. Given the clinical outcome of this patient, close observation after subtotal removal with adjuvant radiotherapy is one of the acceptable treatment options for WHO grade II spinal cord oligodendrogliomas.
Diagnosis ; Drug Therapy ; Follow-Up Studies ; Humans ; Leg ; Magnetic Resonance Imaging ; Male ; Neoplasm, Residual ; Oligodendroglioma* ; Radiotherapy ; Radiotherapy, Adjuvant* ; Spinal Cord Neoplasms ; Spinal Cord* ; Young Adult

OBJECTIVE: The pathophysiology of idiopathic Chiari malformation (CM) type 1 is disturbance of free cerebrospinal fluid (CSF) flow and restoration of normal CSF flow is the mainstay of treatment. Additional migration of the medulla oblongata in pediatric patients is referred to as CM type 1.5, but its significance in adult patients is unknown. This study is to compare surgical outcomes of adult idiopathic CM type 1.5 with that of type 1. METHODS: Thirty-eight consecutive adult patients (M : F=11 : 27; median, 33.5; range, 18–63) with syringomyelia due to idiopathic CM type 1 were reviewed. Migration of the medulla oblongata was noted in 13 patients. The modified McCormick scale (MMS) was used to evaluate functional status before and one year after surgery. All patients underwent foramen magnum decompression and duroplasty. Factors related to radiological success (≥50% decrease in the diameter of the syrinx) were investigated. The follow-up period was 72.7±55.6 months. RESULTS: Preoperative functional status were MMS I in 11 patients and MMS II in 14 of CM type 1 and MMS I in 8 and II in 5 of CM type 1.5. Of patients with MMS II, 5/14 patients in group A and 3/5 patients in group B showed improvement and there was no case of deterioration. Radiological success was achieved in 32 (84%) patients and restoration of the cisterna magna (p=0.01; OR, 46.5) was the only significant factor. CONCLUSION: Migration of the medulla oblongata did not make a difference in the surgical outcome when the cisterna magna was restored.
Adult* ; Arachnoid ; Cerebrospinal Fluid ; Cisterna Magna ; Decompression ; Follow-Up Studies ; Foramen Magnum ; Humans ; Medulla Oblongata ; Rhombencephalon ; Syringomyelia

Objective: : Preoperative transarterial embolization (TAE) of tumor feeders in hypervascular spine metastasis is known to reduce intraoperative estimated blood loss (EBL) during surgery. The effect of TAE varies for several reasons, and one controllable factor is the timing between embolization and surgery. However, the adequate timing remains unclear. This study aimed to evaluate the timing and other factors that reduce EBL in spinal metastasis surgery through a meta-analysis. Methods: : A comprehensive database search was performed to identify direct comparative studies of EBL stratified by the timing of surgery after TAE for spinal metastasis. EBL was analyzed according to the timing of surgery and other factors. Subgroup analyses were also performed. The difference in EBL was calculated as the mean difference (MD) and 95% confidence interval (CI). Results: : Among seven studies, 196 and 194 patients underwent early and late surgery after TAE, respectively. The early surgery was defined as within 1–2 days after TAE, while the late surgery group received surgery at least 1 day after TAE. Overall, the MD in EBL was not different according to the timing of surgery (MD, 86.3 mL; 95% CI, -95.5 to 268.1 mL; p=0.35). A subgroup analysis of the complete embolization group demonstrated that patients who underwent early surgery within 24 hours after TAE had significantly less bleeding (MD, 233.3 mL; 95% CI, 76.0 to 390.5 mL; p=0.004). In cases of partial embolization, EBL was not significantly different regardless of the time interval. Conclusion : Complete embolization followed by early spinal surgery within 24 hours may reduce intraoperative bleeding for the patients with hypervascular spinal metastasis.

OBJECTIVE: A spinal cord subependymoma is an uncommon, indolent, benign spinal cord tumor. It is radiologically similar to a spinal cord ependymoma, but surgical findings and outcomes differ. Gross total resection of the tumor is not always feasible. The present study was done to determine the clinical, radiological and pathological characteristics of spinal cord subependymomas.METHODS: We retrospectively reviewed the medical records of ten spinal cord subependymoma patients (M : F=4 : 6; median 38 years; range, 21–77) from four institutions.RESULTS: The most common symptoms were sensory changes and/or pain in eight patients, followed by motor weakness in six. The median duration of symptoms was 9.5 months. Preoperative radiological diagnosis was ependymoma in seven and astrocytoma in three. The tumors were located eccentrically in six and were not enhanced in six. Gross total resection of the tumor was achieved in five patients, whereas subtotal or partial resection was inevitable in the other five patients due to a poor dissection plane. Adjuvant radiotherapy was performed in two patients. Neurological deterioration occurred in two patients; transient weakness in one after subtotal resection and permanent weakness after gross total resection in the other. Recurrence or regrowth of the tumor was not observed during the median 31.5 months follow-up period (range, 8–89).CONCLUSION: Spinal cord subependymoma should be considered when the tumor is located eccentrically and is not dissected easily from the spinal cord. Considering the rather indolent nature of spinal cord subependymomas, subtotal removal without the risk of neurological deficit is another option.
Astrocytoma ; Diagnosis ; Ependymoma ; Follow-Up Studies ; Glioma, Subependymal ; Humans ; Medical Records ; Radiotherapy, Adjuvant ; Recurrence ; Retrospective Studies ; Spinal Cord Neoplasms ; Spinal Cord ; Spine

Objective: This study aimed to develop and validate a deep learning (DL) algorithm for the quantitative measurement of thoracolumbar (TL) fracture features, and to evaluate its efficacy across varying levels of clinical expertise. Methods: Using the pretrained Mask Region-Based Convolutional Neural Networks model, originally developed for vertebral body segmentation and fracture detection, we fine-tuned the model and added a new module for measuring fracture metrics—compression rate (CR), Cobb angle (CA), Gardner angle (GA), and sagittal index (SI)—from lumbar spine lateral radiographs. These metrics were derived from six-point labeling by 3 radiologists, forming the ground truth (GT). Training utilized 1,000 nonfractured and 318 fractured radiographs, while validations employed 213 internal and 200 external fractured radiographs. The accuracy of the DL algorithm in quantifying fracture features was evaluated against GT using the intraclass correlation coefficient. Additionally, 4 readers with varying expertise levels, including trainees and an attending spine surgeon, performed measurements with and without DL assistance, and their results were compared to GT and the DL model. Results: The DL algorithm demonstrated good to excellent agreement with GT for CR, CA, GA, and SI in both internal (0.860, 0.944, 0.932, and 0.779, respectively) and external (0.836, 0.940, 0.916, and 0.815, respectively) validations. DL-assisted measurements significantly improved most measurement values, particularly for trainees. Conclusion The DL algorithm was validated as an accurate tool for quantifying TL fracture features using radiographs. DL-assisted measurement is expected to expedite the diagnostic process and enhance reliability, particularly benefiting less experienced clinicians.

Objective: Classification guides the surgical approach and predicts prognosis. However, existing classifications of spinal schwannomas often result in a high ‘unclassified’ rate. Here, we aim to develop a new comprehensive classification for spinal schwannomas based on their presumed origin. We compared the new classification with the existing classifications regarding the rate of ‘unclassified’. Finally, we assessed the surgical strategies, outcomes, and complications according to each type of the new classification. Methods: A new classification with 9 types was created by analyzing the anatomy of spinal nerves and the origin of significant tumor portions and cystic components in preoperative magnetic resonance images. A total of 482 patients with spinal schwannomas were analyzed to compare our new classification with the existing classifications. We defined ‘unclassified’ as the inability to classify a patient with spinal schwannoma using the classification criteria. Surgical approaches and outcomes were also aligned with our new classification. Results: Our classification uniquely reported no ‘unclassified’ cases, indicating full applicability. Also, the classification has demonstrated usefulness in predicting the surgical outcome with the approach planned. Gross total removal rates reached 88.0% overall, with type 1 and type 2 tumors at 95.3% and 96.0% respectively. The approach varied with tumor type, with laminectomy predominantly used for types 1, 2, and 9, and facetectomy with posterior fixation used for type 3 tumors. Conclusion The new classification for spinal schwannomas based on presumed origin is applicable to all spinal schwannomas. It could help plan a surgical approach and predict its outcome, compared with existing classifications.

Objective: This study aimed to develop and validate a deep learning (DL) algorithm for the quantitative measurement of thoracolumbar (TL) fracture features, and to evaluate its efficacy across varying levels of clinical expertise. Methods: Using the pretrained Mask Region-Based Convolutional Neural Networks model, originally developed for vertebral body segmentation and fracture detection, we fine-tuned the model and added a new module for measuring fracture metrics—compression rate (CR), Cobb angle (CA), Gardner angle (GA), and sagittal index (SI)—from lumbar spine lateral radiographs. These metrics were derived from six-point labeling by 3 radiologists, forming the ground truth (GT). Training utilized 1,000 nonfractured and 318 fractured radiographs, while validations employed 213 internal and 200 external fractured radiographs. The accuracy of the DL algorithm in quantifying fracture features was evaluated against GT using the intraclass correlation coefficient. Additionally, 4 readers with varying expertise levels, including trainees and an attending spine surgeon, performed measurements with and without DL assistance, and their results were compared to GT and the DL model. Results: The DL algorithm demonstrated good to excellent agreement with GT for CR, CA, GA, and SI in both internal (0.860, 0.944, 0.932, and 0.779, respectively) and external (0.836, 0.940, 0.916, and 0.815, respectively) validations. DL-assisted measurements significantly improved most measurement values, particularly for trainees. Conclusion The DL algorithm was validated as an accurate tool for quantifying TL fracture features using radiographs. DL-assisted measurement is expected to expedite the diagnostic process and enhance reliability, particularly benefiting less experienced clinicians.

Objective: Classification guides the surgical approach and predicts prognosis. However, existing classifications of spinal schwannomas often result in a high ‘unclassified’ rate. Here, we aim to develop a new comprehensive classification for spinal schwannomas based on their presumed origin. We compared the new classification with the existing classifications regarding the rate of ‘unclassified’. Finally, we assessed the surgical strategies, outcomes, and complications according to each type of the new classification. Methods: A new classification with 9 types was created by analyzing the anatomy of spinal nerves and the origin of significant tumor portions and cystic components in preoperative magnetic resonance images. A total of 482 patients with spinal schwannomas were analyzed to compare our new classification with the existing classifications. We defined ‘unclassified’ as the inability to classify a patient with spinal schwannoma using the classification criteria. Surgical approaches and outcomes were also aligned with our new classification. Results: Our classification uniquely reported no ‘unclassified’ cases, indicating full applicability. Also, the classification has demonstrated usefulness in predicting the surgical outcome with the approach planned. Gross total removal rates reached 88.0% overall, with type 1 and type 2 tumors at 95.3% and 96.0% respectively. The approach varied with tumor type, with laminectomy predominantly used for types 1, 2, and 9, and facetectomy with posterior fixation used for type 3 tumors. Conclusion The new classification for spinal schwannomas based on presumed origin is applicable to all spinal schwannomas. It could help plan a surgical approach and predict its outcome, compared with existing classifications.

Objective: This study aimed to develop and validate a deep learning (DL) algorithm for the quantitative measurement of thoracolumbar (TL) fracture features, and to evaluate its efficacy across varying levels of clinical expertise. Methods: Using the pretrained Mask Region-Based Convolutional Neural Networks model, originally developed for vertebral body segmentation and fracture detection, we fine-tuned the model and added a new module for measuring fracture metrics—compression rate (CR), Cobb angle (CA), Gardner angle (GA), and sagittal index (SI)—from lumbar spine lateral radiographs. These metrics were derived from six-point labeling by 3 radiologists, forming the ground truth (GT). Training utilized 1,000 nonfractured and 318 fractured radiographs, while validations employed 213 internal and 200 external fractured radiographs. The accuracy of the DL algorithm in quantifying fracture features was evaluated against GT using the intraclass correlation coefficient. Additionally, 4 readers with varying expertise levels, including trainees and an attending spine surgeon, performed measurements with and without DL assistance, and their results were compared to GT and the DL model. Results: The DL algorithm demonstrated good to excellent agreement with GT for CR, CA, GA, and SI in both internal (0.860, 0.944, 0.932, and 0.779, respectively) and external (0.836, 0.940, 0.916, and 0.815, respectively) validations. DL-assisted measurements significantly improved most measurement values, particularly for trainees. Conclusion The DL algorithm was validated as an accurate tool for quantifying TL fracture features using radiographs. DL-assisted measurement is expected to expedite the diagnostic process and enhance reliability, particularly benefiting less experienced clinicians.

Objective: Classification guides the surgical approach and predicts prognosis. However, existing classifications of spinal schwannomas often result in a high ‘unclassified’ rate. Here, we aim to develop a new comprehensive classification for spinal schwannomas based on their presumed origin. We compared the new classification with the existing classifications regarding the rate of ‘unclassified’. Finally, we assessed the surgical strategies, outcomes, and complications according to each type of the new classification. Methods: A new classification with 9 types was created by analyzing the anatomy of spinal nerves and the origin of significant tumor portions and cystic components in preoperative magnetic resonance images. A total of 482 patients with spinal schwannomas were analyzed to compare our new classification with the existing classifications. We defined ‘unclassified’ as the inability to classify a patient with spinal schwannoma using the classification criteria. Surgical approaches and outcomes were also aligned with our new classification. Results: Our classification uniquely reported no ‘unclassified’ cases, indicating full applicability. Also, the classification has demonstrated usefulness in predicting the surgical outcome with the approach planned. Gross total removal rates reached 88.0% overall, with type 1 and type 2 tumors at 95.3% and 96.0% respectively. The approach varied with tumor type, with laminectomy predominantly used for types 1, 2, and 9, and facetectomy with posterior fixation used for type 3 tumors. Conclusion The new classification for spinal schwannomas based on presumed origin is applicable to all spinal schwannomas. It could help plan a surgical approach and predict its outcome, compared with existing classifications.