Sarcoidosis is an inflammatory condition affecting multiple systems in the body, distinguished by the presence of noncaseating granulomas. It is believed that specific exposures to external antigens in individuals with genetic predisposition lead to the development of these granulomas. When diagnosing sarcoidosis, tuberculosis (TB) is a potential alternative explanation for the symptoms. Our case describes a rare coexistence of cutaneous sarcoidosis and TB pleurisy in a 75-year-old male. He was diagnosed with cutaneous sarcoidosis on his face. During the investigation for possible involvement of other organs, pleural effusion was discovered, and it was determined to be caused by mycobacterial infection. The patient received a 6-month course of anti-TB drugs to treat the TB pleurisy, while a topical calcineurin inhibitor was applied to the cutaneous sarcoidosis. This case serves as a reminder to dermatologists that the coexistence of TB with sarcoidosis is possible, not just as a differential diagnosis.

Sarcoidosis is an inflammatory condition affecting multiple systems in the body, distinguished by the presence of noncaseating granulomas. It is believed that specific exposures to external antigens in individuals with genetic predisposition lead to the development of these granulomas. When diagnosing sarcoidosis, tuberculosis (TB) is a potential alternative explanation for the symptoms. Our case describes a rare coexistence of cutaneous sarcoidosis and TB pleurisy in a 75-year-old male. He was diagnosed with cutaneous sarcoidosis on his face. During the investigation for possible involvement of other organs, pleural effusion was discovered, and it was determined to be caused by mycobacterial infection. The patient received a 6-month course of anti-TB drugs to treat the TB pleurisy, while a topical calcineurin inhibitor was applied to the cutaneous sarcoidosis. This case serves as a reminder to dermatologists that the coexistence of TB with sarcoidosis is possible, not just as a differential diagnosis.

Sarcoidosis is an inflammatory condition affecting multiple systems in the body, distinguished by the presence of noncaseating granulomas. It is believed that specific exposures to external antigens in individuals with genetic predisposition lead to the development of these granulomas. When diagnosing sarcoidosis, tuberculosis (TB) is a potential alternative explanation for the symptoms. Our case describes a rare coexistence of cutaneous sarcoidosis and TB pleurisy in a 75-year-old male. He was diagnosed with cutaneous sarcoidosis on his face. During the investigation for possible involvement of other organs, pleural effusion was discovered, and it was determined to be caused by mycobacterial infection. The patient received a 6-month course of anti-TB drugs to treat the TB pleurisy, while a topical calcineurin inhibitor was applied to the cutaneous sarcoidosis. This case serves as a reminder to dermatologists that the coexistence of TB with sarcoidosis is possible, not just as a differential diagnosis.

Sarcoidosis is an inflammatory condition affecting multiple systems in the body, distinguished by the presence of noncaseating granulomas. It is believed that specific exposures to external antigens in individuals with genetic predisposition lead to the development of these granulomas. When diagnosing sarcoidosis, tuberculosis (TB) is a potential alternative explanation for the symptoms. Our case describes a rare coexistence of cutaneous sarcoidosis and TB pleurisy in a 75-year-old male. He was diagnosed with cutaneous sarcoidosis on his face. During the investigation for possible involvement of other organs, pleural effusion was discovered, and it was determined to be caused by mycobacterial infection. The patient received a 6-month course of anti-TB drugs to treat the TB pleurisy, while a topical calcineurin inhibitor was applied to the cutaneous sarcoidosis. This case serves as a reminder to dermatologists that the coexistence of TB with sarcoidosis is possible, not just as a differential diagnosis.

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.

Purpose: In men with metastatic castration-resistant prostate cancer (mCRPC), new bone lesions are sometimes not properly categorized through a confirmatory bone scan, and clinical significance of the test itself remains unclear. This study aimed to demonstrate the performance rate of confirmatory bone scans in a real-world setting and their prognostic impact in enzalutamide-treated mCRPC. Materials and Methods: Patients who received oral enzalutamide for mCRPC during 2014-2017 at 14 tertiary centers in Korea were included. Patients lacking imaging assessment data or insufficient drug exposure were excluded. The primary outcome was overall survival (OS). Secondary outcomes included performance rate of confirmatory bone scans in a real-world setting. Kaplan-Meier analysis and multivariate Cox regression analysis were performed. Results: Overall, 520 patients with mCRPC were enrolled (240 [26.2%] chemotherapy-naïve and 280 [53.2%] after chemotherapy). Among 352 responders, 92 patients (26.1%) showed new bone lesions in their early bone scan. Confirmatory bone scan was performed in 41 patients (44.6%), and it was associated with prolonged OS in the entire population (median, 30.9 vs. 19.7 months; p < 0.001), as well as in the chemotherapy-naïve (median, 47.2 vs. 20.5 months; p=0.011) and post-chemotherapy sub-groups (median, 25.5 vs. 18.0 months; p=0.006). Multivariate Cox regression showed that confirmatory bone scan performance was an independent prognostic factor for OS (hazard ratio 0.35, 95% confidence interval, 0.18 to 0.69; p=0.002). Conclusion Confirmatory bone scan performance was associated with prolonged OS. Thus, the premature discontinuation of enzalutamide without confirmatory bone scans should be discouraged.