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 evaluate the therapeutic role of lymphadenectomy in patients surgically treated for clinically early-stage epithelial ovarian cancer (EOC). Methods: This retrospective, multicenter study included patients with clinically earlystage EOC based on preoperative abdominal-pelvic computed tomography or magnetic resonance imaging findings between 2007 and 2021. Oncologic outcomes and perioperative complications were compared between the lymphadenectomy and non-lymphadenectomy groups. Independent prognostic factors were determined using Cox regression analysis.Disease-free survival (DFS) was the primary outcome. Overall survival (OS) and perioperative outcomes were the secondary outcomes. Results: In total, 586 patients (lymphadenectomy group, n=453 [77.3%]; nonlymphadenectomy groups, n=133 [22.7%]) were eligible. After surgical staging, upstaging was identified based on the presence of lymph node metastasis in 14 (3.1%) of 453 patients.No significant difference was found in the 5-year DFS (88.9% vs. 83.4%, p=0.203) and 5-year OS (97.2% vs. 97.7%, p=0.895) between the two groups. Using multivariable analysis, lymphadenectomy was not significantly associated with DFS or OS. However, using subgroup analysis, the lymphadenectomy group with serous histology had higher 5-year DFS rates than did the non-lymphadenectomy group (86.5% vs. 74.4%, p=0.048; adjusted hazard ratio=0.281; 95% confidence interval=0.107–0.735; p=0.010). The lymphadenectomy group had longer operating time (p<0.001), higher estimated blood loss (p<0.001), and higher perioperative complication rate (p=0.004) than did the non-lymphadenectomy group. Conclusion In patients with clinically early-stage EOC with serous histology, lymphadenectomy was associated with survival benefits. Considering its potential harm,

Objective: This study aimed to evaluate the therapeutic role of lymphadenectomy in patients surgically treated for clinically early-stage epithelial ovarian cancer (EOC). Methods: This retrospective, multicenter study included patients with clinically earlystage EOC based on preoperative abdominal-pelvic computed tomography or magnetic resonance imaging findings between 2007 and 2021. Oncologic outcomes and perioperative complications were compared between the lymphadenectomy and non-lymphadenectomy groups. Independent prognostic factors were determined using Cox regression analysis.Disease-free survival (DFS) was the primary outcome. Overall survival (OS) and perioperative outcomes were the secondary outcomes. Results: In total, 586 patients (lymphadenectomy group, n=453 [77.3%]; nonlymphadenectomy groups, n=133 [22.7%]) were eligible. After surgical staging, upstaging was identified based on the presence of lymph node metastasis in 14 (3.1%) of 453 patients.No significant difference was found in the 5-year DFS (88.9% vs. 83.4%, p=0.203) and 5-year OS (97.2% vs. 97.7%, p=0.895) between the two groups. Using multivariable analysis, lymphadenectomy was not significantly associated with DFS or OS. However, using subgroup analysis, the lymphadenectomy group with serous histology had higher 5-year DFS rates than did the non-lymphadenectomy group (86.5% vs. 74.4%, p=0.048; adjusted hazard ratio=0.281; 95% confidence interval=0.107–0.735; p=0.010). The lymphadenectomy group had longer operating time (p<0.001), higher estimated blood loss (p<0.001), and higher perioperative complication rate (p=0.004) than did the non-lymphadenectomy group. Conclusion In patients with clinically early-stage EOC with serous histology, lymphadenectomy was associated with survival benefits. Considering its potential harm,

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 evaluate the therapeutic role of lymphadenectomy in patients surgically treated for clinically early-stage epithelial ovarian cancer (EOC). Methods: This retrospective, multicenter study included patients with clinically earlystage EOC based on preoperative abdominal-pelvic computed tomography or magnetic resonance imaging findings between 2007 and 2021. Oncologic outcomes and perioperative complications were compared between the lymphadenectomy and non-lymphadenectomy groups. Independent prognostic factors were determined using Cox regression analysis.Disease-free survival (DFS) was the primary outcome. Overall survival (OS) and perioperative outcomes were the secondary outcomes. Results: In total, 586 patients (lymphadenectomy group, n=453 [77.3%]; nonlymphadenectomy groups, n=133 [22.7%]) were eligible. After surgical staging, upstaging was identified based on the presence of lymph node metastasis in 14 (3.1%) of 453 patients.No significant difference was found in the 5-year DFS (88.9% vs. 83.4%, p=0.203) and 5-year OS (97.2% vs. 97.7%, p=0.895) between the two groups. Using multivariable analysis, lymphadenectomy was not significantly associated with DFS or OS. However, using subgroup analysis, the lymphadenectomy group with serous histology had higher 5-year DFS rates than did the non-lymphadenectomy group (86.5% vs. 74.4%, p=0.048; adjusted hazard ratio=0.281; 95% confidence interval=0.107–0.735; p=0.010). The lymphadenectomy group had longer operating time (p<0.001), higher estimated blood loss (p<0.001), and higher perioperative complication rate (p=0.004) than did the non-lymphadenectomy group. Conclusion In patients with clinically early-stage EOC with serous histology, lymphadenectomy was associated with survival benefits. Considering its potential harm,

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: This study aimed to investigate the impact of a nurse shift improvement pilot project on nurses' shift adjustment, work-life balance, and turnover intention, as well as nurses' experiences of participating in the pilot project. Methods: An explanatory sequential mixed-methods study was performed. Survey data was collected from a total of 1,051 nurses both in participant and non-participant groups. For the qualitative study, focus group interviews were conducted with 22 nurses who participated in the shift improvement pilot, including 12 staff nurses, 7 nurse managers, and 3 substitute nurses. Results: There were significant positive effects on the key variables of nurses’ shift adjustment, work-life balance, and turnover intention in the group that participated in the nurse shift improvement pilot project. Based on the qualitative study, participants in the shift improvement pilot project indicated that it enabled nurses to recover their circadian rhythm, and achieve a better work-life balance, and they wanted to continue working as a nurse, and hoped expanded application of the pilot project. Conclusion In order for expanded implementation of the pilot project and continued improvement in the shift system, we recommend establishing compensation standard, systematizing the use of substitute nurses, developing guidelines for shift schedules, and expanding the shift improvement pilot project to other departments such as the emergency room.

Background: To evaluate the chemosensitivity to doxorubicin (DOX) in two primary cells derived from a tumor of FVB/N-Trp53tm1Hw1 knockout (KO) mice with TALEN-mediated Trp53 mutant gene, we evaluated the cell survivability, cell cycle distribution, apoptotic cell numbers and apoptotic protein expression in solid tumor cells and ascetic tumor cells treated with DOX. Results: The primary tumor cells showed a significant (P < 0.05) defect for UV-induced upregulation of the Trp53 pro-tein, and consisted of different ratios of leukocytes, fibroblasts, epithelial cells and mesenchymal cells. The ­ IC50 level to DOX was lower in both primary cells ­(IC50 = 0.12 μM and 0.20 μM) as compared to the CT26 cells ­(IC50 = 0.32  μM), although the solid tumor was more sensitive. Also, the number of cells arrested at the G0/G1 stage was significantly decreased (24.7–23.1% in primary tumor cells treated with DOX, P < 0.05) while arrest at the G2 stage was enhanced to 296.8–254.3% in DOX-treated primary tumor cells compared with DOX-treated CT26 cells. Furthermore, apoptotic cells of early and late stage were greatly increased in the two primary cell-lines treated with DOX when compared to same conditions for CT26 cells. However, the Bax/Bcl-2 expression level was maintained constant in the primary tumor and CT26 cells. Conclusions To the best of our knowledge, these results are the first to successfully detect an alteration in chemosensitivity to DOX in solid tumor cells and ascetic tumor cells derived from tumor of FVB/N-Trp53tm1Hw1 mice TALENmediated Trp53 mutant gene.