Brain aspergillosis has been increasing remarkably. They are known to occur commonly in immunocompromised individuals by hematogenous spread from other primary sites or by direct extension from adjacent structures to central nervous system. We report a rare case of a 29-year-old male without any known medical history, who had isolated brain lesion and the pathology from stereotactic biopsy confirmed cerebral aspergillosis.
Adult ; Aspergillosis ; Biopsy ; Brain ; Central Nervous System ; Humans ; Male

Brain aspergillosis has been increasing remarkably. They are known to occur commonly in immunocompromised individuals by hematogenous spread from other primary sites or by direct extension from adjacent structures to central nervous system. We report a rare case of a 29-year-old male without any known medical history, who had isolated brain lesion and the pathology from stereotactic biopsy confirmed cerebral aspergillosis.
Adult ; Aspergillosis ; Biopsy ; Brain ; Central Nervous System ; Humans ; Male

PURPOSE: To evaluate the negative predictive value (NPV) of ultrasound (US) elastography for non-palpable Breast Imaging Reporting and Data System (BI-RADS) category 3 lesions on ultrasonography and to determine whether US elastography is helpful in reducing the number of BI-RADS category 3 lesions on ultrasonography. METHODS: Two hundred seventy-six consecutive, non-palpable BI-RADS category 3 lesions in 256 women who underwent US elastography and US-guided core biopsy, and who had at least 12 months of follow-up data, comprised our study group. The BI-RADS final assessment category and elasticity score were prospectively and independently classified. The rate of malignancy and NPV according to the elasticity score were analysed. We also investigated whether there was a subset of BI-RADS category 3 lesions that were of benign histology but negative on elastography. RESULTS: Of the 276 non-palpable BI-RADS category 3 lesions, three lesions (1.0%) were finally confirmed as ductal carcinomas in situ. No cancers were found in the remaining 273 lesions with benign biopsy histology at a mean follow-up of 39.4 months (range, 12 to 72 months). The NPV of a negative elasticity score (elasticity score of 1) was 99.3% (165 of 166). If BI-RADS category 3 lesions showing a negative elasticity score were downgraded to BI-RADS category 2, 60.4% (165 of 273) of them with benign histology could have been safely followed without biopsy with an increased malignancy rate from 1% (3 of 276) to 1.8% (2 of 110), which is not significantly higher (P=0.626). CONCLUSION: US elastography has the potential to reduce the number of BI-RADS category 3 lesions on ultrasonography.
Biopsy ; Breast Neoplasms ; Breast* ; Carcinoma, Ductal ; Elasticity ; Elasticity Imaging Techniques* ; Female ; Follow-Up Studies ; Humans ; Information Systems* ; Prospective Studies ; Ultrasonography*

Tuberculosis remains one of the most prevalent and fatal infectious diseases in spite of considerable improvements in medical science. The diagnosis and treatment of extrapulmonary tuberculosis involving the abdomen is still complicated owing to vague or non-specific clinical features. Although rare, isolated splenic involvement is one of the important manifestations of extrapulmonary tuberculosis, and imaging suspicion of the disease is essential. We report a case of surgically confirmed mass-forming splenic tuberculosis showing a layered pattern consisting of caseous necrosis with profound restriction of water molecules surrounded by an irregular rind of granulation tissue with less diffusion restriction on diffusion-weighted magnetic resonance imaging (DWI). In the differential diagnosis of neoplastic or non-neoplastic mass-forming lesions involving the spleen, this unique DWI feature could be helpful in characterizing splenic tuberculosis. The patient has been in clinically disease free status for nearly 20 months after splenectomy.
Diffusion Magnetic Resonance Imaging/*methods ; Female ; Humans ; Middle Aged ; Necrosis ; Splenectomy ; Tuberculosis, Splenic/*pathology

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.