Reticulohistiocytoma is a rare, benign histiocytic proliferation of the skin or soft tissue. A 5-month-old healthy girl visited our clinic for an enlarging nodule on the center of her right palm. The clinical differential diagnosis included xanthogranuloma and primary cutaneous CD4 positive small/medium T-cell lymphoma. Histopathology of the nodule showed abundant eosinophilic and glassy cytoplasm. The nuclei were round to oval shaped, with focal irregular nuclear membrane, and mitotic figures were absent. Immunohistochemical study determined that the cells were positive for CD68 but negative for CD1a. She was finally diagnosed with reticulohistiocytoma (solitary epithelioid histiocytoma).
Cytoplasm ; Diagnosis, Differential ; Eosinophils ; Female ; Histiocytosis, Non-Langerhans-Cell ; Humans ; Infant ; Lymphoma, T-Cell ; Nuclear Envelope ; Skin

Background/Aims: Veno-occlusive disease/sinusoidal obstruction syndrome (VOD/SOS) is one of the most fatal complications of hematopoietic cell transplantation (HCT), and defibrotide is the only curative drug. We conducted this study to confirm the survival rate of VOD/SOS patients diagnosed in Korea and assess the efficacy of defibrotide. Methods: Patients diagnosed with VOD/SOS after allogenic HCT between 2003 and 2020 were enrolled. We investigated day +100 survival rates and associated risk factors in patients who satisfied the modified Seattle criteria within 50 days of HCT. Results: A total of 110 patients satisfied the modified Seattle criteria, of which 65.5% satisfied the Baltimore criteria. Thirty-seven patients were treated with defibrotide. The day +100 survival rate of the 110 patients was 65.3%. The survival rates in patients who did not meet the Baltimore criteria and in those who did were 86.8% and 53.7%, respectively (p = 0.001). The day +100 survival rate of patients treated with defibrotide was 50.5%. Among the patients receiving defibrotide, those whose creatinine levels were more than 1.2 times the baseline had a significantly lower survival rate at 26.7% (p = 0.014). On multivariate regression analysis, the hazard ratio of satisfaction of the Baltimore criteria was 4.54 (95% confidence interval [CI], 1.69 to 12.21; p = 0.003). In patients treated with defibrotide, the hazard ratio was 8.70 (95% CI, 2.26 to 33.45; p = 0.002), when creatinine was more than 1.2 times the baseline on administration. Conclusions The day +100 survival rate was significantly lower when the Baltimore criteria were satisfied, and when there was an increase in creatinine at the time of defibrotide administration.

PURPOSE: The present study explores the expression rate of p53 mutation and the correlation between the expression of p53 protein and prognostic factors in medulloblastoma/ PNET (primitive neuroectodermal tumor). MATERIALS AND METHODS: We studied retrospectively 24 patients with medulloblastoma/ PNET, who were admitted in Dong-A University Hospital, Pusan National University Hospital and Inje University Pusan Paik Hospital from 1988 to 1995. Detection of p53 mutations was made by immunohistochemical staining of p53 protein on paraffin- embedded tissues. The correlation between the expression of p53 protein and prognostic factors was evaluated by the Spearman correlation analysis. RESULTS: p53 protein was expressed in 6 of 24 patients (25%). In 20 patients who could be evaluated for metastasis, 16 patients of M0, 1 patient of M1 and 3 patients of M2 were grouped by M stage, and the expression of p53 was detected in 1 of 16 M0 group (6.3%) and 3 of 3 M2 group (100%). p53 expression was significantly related to the M stage of medulloblastoma/PNET (r=0.73, p<0.001). The detection of p53 was not significantly associated with T stage, cellular differentiation and the relapse rate of medulloblastoma/ PNET. CONCLUSION: The immunohistochemical detection rate of p53 protein in medulloblastoma/ PNET was 25%. The expression of p53 protein was significantly related to the M stage, with higher expression rate in M2 group of medulloblsatoma/PNET.
Busan ; Humans ; Medulloblastoma ; Neoplasm Metastasis ; Neural Plate ; Neuroectodermal Tumors, Primitive ; Recurrence ; Retrospective Studies

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: 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: 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: 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: 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.

STUDY DESIGN: This is a prospective study. PURPOSE: We compared the outcomes of segmental decompression and wide decompression in patients who had multilevel lumbar foraminal stenosis with back pain. OVERVIEW OF LITERATURE: Wide decompression and fusion in patients with multilevel lumbar foraminal stenosis may increase the risk of perioperative complications. METHODS: From March 2005 to December 2007, this study prospectively examined 87 patients with multilevel lumbar foraminal stenosis and who were treated by segmental or wide decompression along with posterior fusion using pedicle screw fixation, and these patients could be followed-up for a minimum of 2 years. Of the 87 patients, 45 and 42 patients were assigned to the segmental decompression group (group 1) and the wide decompression group (group 2), respectively. We compared the clinical and radiological outcomes of the patients in these two groups. RESULTS: There were no significant differences between groups 1 and 2 in terms of the levels of postoperative pain based on the visual analogue scale, the Oswestry Disability Score, the clinical results based on the Kirkaldy-Willis Criteria, the complication rate or the posterior fusion rate. On the other hand, the mean operating times in groups 1 and 2 were 153 +/- 32 minutes and 187 +/- 36 minutes, respectively (p < 0.05). The amount of blood loss during surgery and on the first postoperative day was 840 +/- 236 ml and 1,040 +/- 301 ml in groups 1 and 2, respectively (p < 0.05). CONCLUSIONS: These results suggest that segmental decompression offers promising and reproducible clinical and radiological results for patients suffering from multilevel lumbar foraminal stenosis.
Constriction, Pathologic ; Decompression ; Hand ; Humans ; Pain, Postoperative ; Prospective Studies ; Stress, Psychological

Malignant peripheral nerve sheath tumor (MPNST) is a rare neoplasm and the main cause of the mortality in neurofibromatosis type 1 (NF 1). MPNSTs have been found mostly in the head and neck and the upper or lower extremities with intrathoracic MPNSTs being uncommon. PET has been a useful diagnostic modality of MPNSTs in NF 1. We present a 17-year-old girl patient with NF 1. She was admitted with chronic cough and shortness of breath caused by a huge mediastinal mass. An 18FDG-PET study revealed intense uptake at the mediastinal mass. She underwent surgery to lessen respiratory symptoms, and the mass was histologically diagnosed as an intrathoracic MPNST.
Adolescent ; Cough ; Dyspnea ; Head ; Humans ; Lower Extremity ; Neck ; Nerve Sheath Neoplasms ; Neurofibromatoses ; Neurofibromatosis 1 ; Peripheral Nerves