Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

OBJECTIVE: Choice of hysterectomy and adjuvant treatment for International Federation of Gynecology and Obstetrics (FIGO) 2009 stage II endometrioid endometrial cancer (EEC) is still controversial. Aims of this study were to evaluate survival benefits and adverse effects of different hysterectomies with or without adjuvant radiotherapy (RT), and to identify prognostic factors. METHODS: The patients at 14 member hospitals of the Taiwanese Gynecologic Oncology Group from 1992 to 2013 were retrospectively investigated. Patients were divided into simple hysterectomy (SH) alone, SH with RT, radical hysterectomy (RH) alone, and RH with RT groups. Endpoints were recurrence-free survival (RFS), overall survival (OS), disease-specific survival (DSS), adverse effects and prognostic factors for survival. RESULTS: Total of 246 patients were enrolled. The 5-year RFS, OS, DSS and recurrence rates for the entire cohort were 89.5%, 94.3%, 96.2% and 10.2%, respectively. Patients receiving RH had more adverse effects including blood loss (p < 0.001), recurrent urinary tract infections (p = 0.013), and leg lymphedema (p = 0.038). Age over 50-year (HR = 9.2; 95% confidence interval [CI], 1.2–70.9) and grade 3 histology (HR = 7.28; 95% CI, 1.45–36.6) were independent predictors of OS. Grade 3 histology was an independent predictor of RFS (HR = 5.13; 95% CI, 1.38–19.1) and DSS (HR = 5.97; 95% CI, 1.06–58.7). Patients receiving adjuvant RT had lower locoregional recurrence (p = 0.046), but no impact on survival. CONCLUSION: Different treatment modalities yield similar survival outcomes. Patients receiving SH with RT had lower locoregional recurrent with acceptable morbidity. Age and tumor grading remained significant predictors for survival among patients with FIGO 2009 stage II EEC.
Cohort Studies* ; Endometrial Neoplasms* ; European Union ; Female ; Gynecology ; Humans ; Hysterectomy ; Leg ; Lymphedema ; Neoplasm Grading ; Obstetrics ; Radiotherapy ; Radiotherapy, Adjuvant ; Recurrence ; Retrospective Studies* ; Urinary Tract Infections ; Uterine Neoplasms

Objective To investigate the clinical effects of tibiotalocalcaneal arthrodesis (TTCA) using a retrograde intramedullary nail with a valgus curve.Methods At our department,22 patients underwent TTCA using a retrograde intramedullary nail with a valgus curve from June 2009 to January 2014 and were available for complete follow-up.They were 12 men and 10 women,aged from 46 to 79 years (average,62.2 years).There were 3 cases of primary ankle osteoarthritis,9 ones of traumatic arthritis,one of ankle arthritis secondary to severe talar avascular necrosis,3 ones of progressive subtalar arthritis following failed ankle replacement,5 ones of progressive subtalar arthritis following failed ankle arthrodesis,and one of arthritis secondary to equinovarus.The outcome measurements included the American Foot and Ankle Society (AOFAS) ankle-hindfoot scale,EQ-5DTM functional score,radiologic assessment and clinical examination.Results The mean follow-up was 21.3 months (range,from 14 to 38 months).A plantigrade foot and bony union were achieved in all the patients after a mean time of 3.9 months (range,from 2.4 to 6.2 months).Postoperative radiologic results showed a good hindfoot alignment in all the patients.Superficial infection occurred in one patient and loosening of the distal screw in another who asked for removal.The mean postoperative EQ-5DTM functional score and AOFAS ankle-hindfoot score were 69.3 (range,from 20 to 90) and 69.9 (range,from 45 to 85),respectively.Conclusion TTCA using a retrograde curved intramedullary nail may lead to solid fusion and good hindfoot alignment.

We investigated the development of an injectable, biodegradable hydrogel composite of poly(trimethylene carbonate)-F127-poly(trimethylene carbonate)(PTMC11-F127-PTMC11 )loaded with bone morphogenetic protein-2 (BMP-2) derived peptide P24 for ectopic bone formation in vivo and evaluated its release kinetics in vitro. Then we evaluated P24 peptide release kinetics from different concentration of PTMC11-F127-PTMC11 hydrogel in vitro using bicinchoninic acid (BCA)assay. P24/ PTMC11-F127-PTMC11 hydrogel was implanted into each rat's erector muscle of spine and ectopic bone formation of the implanted gel in vivo was detected by hematoxylin and eosin stain (HE). PTMC11-F127-PTMC11 hydrogel with concentration more than 20 percent showed sustained slow release for one month after the initial burst release. Bone trabeculae surround the P24/ PTMC11-F127-PTMC11 hydrogel was shown at the end of six weeks by hematoxylin and eosin stain. These results indicated that encapsulated bone morphogenetic protein (BMP-2) derived peptide P24 remained viable in vivo, thus suggesting the potential of PTMC11-F127-PT- MC11 composite hydrogels as part of a novel strategy for localized delivery of bioactive molecules.
Animals ; Biocompatible Materials ; chemistry ; Bone Morphogenetic Proteins ; pharmacology ; Bone and Bones ; drug effects ; Dioxanes ; chemistry ; Drug Delivery Systems ; Hydrogels ; chemistry ; Osteogenesis ; drug effects ; Peptides ; Prostheses and Implants ; Rats

Objective To assess the efficacy and safety of fluvastatin sodium extended-release tablets (fluvastatin XL) 80 mg once daily compared to fluvastatin sodium immediate-release capsules (fluvastatin IR) 40 mg twice daily in Chinese hyperlipidemic patients with moderate or high cardiovascular risk.Methods In this multi-center,randomized,double-blind,double-dummy,active-controlled,parallel-group study,after 6-week open-label treatment with fluvastatin IR 40 mg once daily,patients who did not reach their lipid goals were randomized to 12-week double-blind treatment with fluvastatin XL 80 mg once daily or fluvastatin IR 40 mg twice daily.Results (1) There were 218 patients enrolled in each group.At the study endpoint,no statistical difference was found in the mean percent change from baseline for LDL-C with-8.69％ [from (3.504 ±0.060) mmol/L to (3.153 ±0.065) mmol/L] in the fluvastatin XL group and-7.89％ [from (3.491 ±0.050) mmol/L to (3.181 ±0.060) mmol/L] in the fluvastatin IR group (P ＞ 0.05).The 95％ CI for difference between the two groups in adjusted mean percent change from baseline was (-4.70％-3.09％),which was within the pre-specified non-inferiority margin.In the fluvastatin XL group,the proportion of patients with moderate cardiovascular(CV) risk and high CV risk achieving their LDL-C treatment goals at endpoint was 50.0％ and 31.5％ respectively,while the proportion was 42.5％ and 24.5％ respectively in the fluvastatin IR group.No significant difference was found between the two groups in the proportion of patients who reached their lipid goals and the changes from baseline with other lipid parameters.(2)Similar safety profiles were observed in the two treatment groups,with 21.1％ adverse event (AE) (8.3％ study-drug related AE) in the fluvastatin XL group and 17.0％ AE (6.3％ study-drug related AE) in the fluvastatin IR group.Conclusion The efficacy of fluvastatin XL 80 mg once daily is comparable to fluvastatin IR 40 mg twice daily in Chinese hyperlipidemic patients with moderate or high cardiovascular risk and both treatments are safe and well-tolerated.

Objective To prepare P24/PTMC11-F127-PTMC11 hydrogel,to study the in vitro release profile and to observe ectopic bone formation in p24 peptide incorporated PTMC11-F127-PTMC11 hydrogel.Methods Corresponding weight powder of p24 peptide was infunded into tubes of PTMC11-F127-PTMC11 solution with concentrations of 16％,20％ and 25％.Release profiles of P24 peptide in different concentration PTMC11-F127-PTMC11 hydrogel were measured in vitro by BCA assay.P24/PTMC11-F127-PTMC11 hydrogel was implanted into each rat's erector muscle of spine,and the implanted gel was detected by hematoxylin and eosin stain (HE).Results PTMC11-F127-PTMC11 hydrogel showed sustained slow release for the whole process after the initial burst release.With the increase of concentration in PTMC11-F127-PTMC.hydrogel,the initial burst release was reduced significantly.Ectopic bone formation was observed by computed tomography in p24 peptide incorporated PTMC11-F127-PTMC11 hydrogel after four weeks.Bone trabeculae surround the P24/PTMC11-F127-PTMC11 hydrogel was observed at forth week by hematoxylin and eosin stain.The bone trabeculae became thicker from sixth week.Conclusion Delayed release of peptide from the hydrogel was mainly controlled by disintegration of hydrogel and a satisfactory release profile was observed.These results suggest that the p24-loaded PTMC11-F127-PTMC11 hydrogel remmns active of p24 at the implanted site,continuously induce differentiation of osteoblast precursor cells into osteoblasts,and activate osteoblasts to promote ectopic calcification.