STUDY DESIGN: Retrospective case series. PURPOSE: To evaluate surgical outcomes and effectiveness of an autogenic rib graft for upper cervical fixation in pediatric patients. OVERVIEW OF LITERATURE: Autogenic bone grafts have long been considered the ‘gold standard’ bone source for posterior cervical fusion in pediatric patients. However, there are some unsolved problems associated with donor-site morbidity and amount of bone grafting. METHODS: We studied five consecutive pediatric patients who underwent atlantoaxial fixation or occipitocervical fixation (OCF) using an autogenic rib graft with at least 2 years of follow-up (mean age, 9.8 years; mean follow-up period, 73.0 months). Two patients underwent OCF without screw-rod constructs and three patients with screw-rod constructs. Autogenic rib grafts were used in all patients. We evaluated the surgical outcomes including radiographic parameter, bony union, and perioperative complications. RESULTS: The atlantoaxial interval (ADI) was corrected from 11.6 to 6.0 mm, and the C1–2 angle was corrected −14.8° to 7.8°. The C2–7 angle was reduced from 31° to 9° spontaneously. Two patients with OCF required revision surgery due to loss of correction. Patients did not experience any complication associated with the donor sites (rib bone grafts). Six months postoperation X-rays clearly showed regeneration of the rib at the donor sites. Bony fusion was achieved in all patients; however, bony fusion occurred more slowly in patients without screw-rod constructs compared with patients with screw-rod constructs. Bone regeneration of the rib was observed in all patients with no complications at the donor site. CONCLUSIONS: Autogenic rib grafts have advantages of potential bone regeneration, high fusion rate, and low donor-site morbidity. In addition, a screw-rod construct provides better bony fusion in pediatric patients with OCF and atlantoaxial fixation.

Objective: Ovarian clear cell carcinoma (OCCC) is a subtype of epithelial ovarian carcinoma with poor prognosis. However, no effective biomarkers have been established for predicting unfavorable events, including recurrence and poor prognoses. Serum microRNAs (miRNAs) have been increasingly reported to be useful in predicting a patient’s condition and have been recognized as a potentially less-invasive source for liquid biopsy in cancer. Therefore, this study aimed to evaluate serum miRNA profiles from patients with OCCC and to establish biomarker for predicting the prognoses. Methods: The GSE106817, which included preoperative serum miRNA profiles of patients with ovarian tumors, was used, and clinical information was investigated. In all, 66 patients with OCCC were included, excluding those with other histological subtypes or insufficient prognostic information. Moreover, miRNA profiles of OCCC tissues were also examined. Results: The median follow-up period was 64.3 (8.0–153.3) months. Based on multivariable Cox regression analyses and the expression of miRNAs in OCCC tissues, miR-150-3p, miR-3195, and miR-7704 were selected as miRNA candidates associated with both progression-free survival (PFS) and overall survival (OS). Then, the prognostic index was calculated based on expression values of 3 serum miRNAs. Kaplan-Meier survival analysis indicated that the prognostic index was significantly predictive of PFS and OS (p=0.004 and p=0.012, respectively). Conclusion Preoperative serum miRNA profiles of miR-150-3p, miR-3195, and miR-7704 can be used to potentially predict the prognosis of patients with OCCC.

Methods: We retrospectively reviewed 31 ASD patients who underwent multilevel LIF combined with PCO (LIF group, n=14) or multilevel PLIF (PLIF group, n=17) and with a minimum 2-year follow-up. In the comparison between LIF and PLIF groups, their mean age at surgery was 69.4 vs. 61.8 years while the mean follow-up period was 29.2 vs. 59.3 months. We evaluated the transition of pelvic incidence–lumbar lordosis (PI–LL) and disc angle (DA) in the LIF group, in fulcrum backward bending (FBB), after LIF and after posterior spinal fusion (PSF) with PCO. The spinopelvic radiographic parameters were compared between LIF and PLIF groups. Results: Compared with the PLIF group, the LIF group had less blood loss and comparable surgical outcomes with respect to radiographic data, health-related quality of life scores and surgical time. In the LIF group, the mean DA and PI–LL were unchanged after LIF (DA, 5.8°; PI–LL, 15°) compared with the values using FBB (DA, 4.3°; PI–LL, 15°) and improved significantly after PSF with PCO (DA, 8.1°; PI–LL, 0°). Conclusions In the surgical treatment of ASD, multilevel LIF is less invasive than multilevel PLIF and combination of LIF and PCO would be necessary for optimal sagittal correction in patients with rigid deformity.

Objective: Magnetic resonance imaging (MRI) is efficient for the diagnosis of preoperative uterine sarcoma; however, misdiagnoses may occur. In this study, we developed a new artificial intelligence (AI) system to overcome the limitations of requiring specialists to manually process datasets and a large amount of computer resources. Methods: The AI system comprises a tumor image filter, which extracts MRI slices containing tumors, and sarcoma evaluator, which diagnoses uterine sarcomas. We used 15 types of MRI patient sequences to train deep neural network (DNN) models used by tumor filter and sarcoma evaluator with 8 cross-validation sets. We implemented tumor filter and sarcoma evaluator using ensemble prediction technique with 9 DNN models. Ten tumor filters and sarcoma evaluator sets were developed to evaluate fluctuation accuracy. Finally, AutoDiag-AI was used to evaluate the new validation dataset, including 8 cases of sarcomas and 24 leiomyomas. Results: Tumor image filter and sarcoma evaluator accuracies were 92.68% and 90.50%, respectively. AutoDiag-AI with the original dataset accuracy was 89.32%, with 90.47% sensitivity and 88.95% specificity, whereas AutoDiag-AI with the new validation dataset accuracy was 92.44%, with 92.25% sensitivity and 92.50% specificity. Conclusion Our newly established AI system automatically extracts tumor sites from MRI images and diagnoses them as uterine sarcomas without human intervention. Its accuracy is comparable to that of a radiologist. With further validation, the system could be applied for diagnosis of other diseases. Further improvement of the system's accuracy may enable its clinical application in the future.

Objective: Magnetic resonance imaging (MRI) is efficient for the diagnosis of preoperative uterine sarcoma; however, misdiagnoses may occur. In this study, we developed a new artificial intelligence (AI) system to overcome the limitations of requiring specialists to manually process datasets and a large amount of computer resources. Methods: The AI system comprises a tumor image filter, which extracts MRI slices containing tumors, and sarcoma evaluator, which diagnoses uterine sarcomas. We used 15 types of MRI patient sequences to train deep neural network (DNN) models used by tumor filter and sarcoma evaluator with 8 cross-validation sets. We implemented tumor filter and sarcoma evaluator using ensemble prediction technique with 9 DNN models. Ten tumor filters and sarcoma evaluator sets were developed to evaluate fluctuation accuracy. Finally, AutoDiag-AI was used to evaluate the new validation dataset, including 8 cases of sarcomas and 24 leiomyomas. Results: Tumor image filter and sarcoma evaluator accuracies were 92.68% and 90.50%, respectively. AutoDiag-AI with the original dataset accuracy was 89.32%, with 90.47% sensitivity and 88.95% specificity, whereas AutoDiag-AI with the new validation dataset accuracy was 92.44%, with 92.25% sensitivity and 92.50% specificity. Conclusion Our newly established AI system automatically extracts tumor sites from MRI images and diagnoses them as uterine sarcomas without human intervention. Its accuracy is comparable to that of a radiologist. With further validation, the system could be applied for diagnosis of other diseases. Further improvement of the system's accuracy may enable its clinical application in the future.

Objective: Magnetic resonance imaging (MRI) is efficient for the diagnosis of preoperative uterine sarcoma; however, misdiagnoses may occur. In this study, we developed a new artificial intelligence (AI) system to overcome the limitations of requiring specialists to manually process datasets and a large amount of computer resources. Methods: The AI system comprises a tumor image filter, which extracts MRI slices containing tumors, and sarcoma evaluator, which diagnoses uterine sarcomas. We used 15 types of MRI patient sequences to train deep neural network (DNN) models used by tumor filter and sarcoma evaluator with 8 cross-validation sets. We implemented tumor filter and sarcoma evaluator using ensemble prediction technique with 9 DNN models. Ten tumor filters and sarcoma evaluator sets were developed to evaluate fluctuation accuracy. Finally, AutoDiag-AI was used to evaluate the new validation dataset, including 8 cases of sarcomas and 24 leiomyomas. Results: Tumor image filter and sarcoma evaluator accuracies were 92.68% and 90.50%, respectively. AutoDiag-AI with the original dataset accuracy was 89.32%, with 90.47% sensitivity and 88.95% specificity, whereas AutoDiag-AI with the new validation dataset accuracy was 92.44%, with 92.25% sensitivity and 92.50% specificity. Conclusion Our newly established AI system automatically extracts tumor sites from MRI images and diagnoses them as uterine sarcomas without human intervention. Its accuracy is comparable to that of a radiologist. With further validation, the system could be applied for diagnosis of other diseases. Further improvement of the system's accuracy may enable its clinical application in the future.

Multiple endocrine neoplasia type 1 (MEN1) is an inherited autosomal dominant disease presenting with pancreatic neuroendocrine tumors (pNETs), parathyroid tumors, or pituitary tumors. Using the PubMed database, we reviewed the literature on information regarding the proper diagnosis and treatment of MEN1-associated pNET. Many cases of MEN1-associated pNET are functioning pNETs. Gastrinomas and insulinomas tend to occur frequently in the duodenum and pancreas, respectively. In addition to diagnostic imaging, the selective arterial secretagogue injection test (SASI test) is useful for localizing functioning pNET. The standard treatment is surgical resection. However, in the case of a functioning pNET, the tumor should first be accurately located using the SASI test before an appropriate surgical method is selected. In cases of a MEN1-associated non-functioning pNET that exceeds 2 cm in diameter, the incidence of distant metastasis is significantly increased, and surgery is recommended. In cases of unresectable pNET, a somatostatin analog has been shown to demonstrate antitumor effects and is considered to be a promising treatment. In addition, molecular-targeted drugs have recently been found to be effective in phase III clinical trials.
Diagnostic Imaging ; Duodenum ; Gastrinoma ; Incidence ; Insulinoma ; Multiple Endocrine Neoplasia ; Multiple Endocrine Neoplasia Type 1 ; Neoplasm Metastasis ; Neuroectodermal Tumors, Primitive ; Neuroendocrine Tumors ; Pancreas ; Pituitary Neoplasms ; Somatostatin

Chronic expanding haematoma (CEH) is a rare type of haematoma that enlarges slowly and continuously without coagulation. It can occur following surgery because of shear stress-induced bleeding in the scar tissue between the subcutaneous fat and fascia. We present three cases of large chronic CEH that were successfully treated with triamcinolone injections. Three female patients developed large chronic CEH at 9 months, 5 years, and 6 years, respectively, after latissimus dorsi flap harvesting for breast reconstruction. Although the condition did not improve after multiple sessions of haematoma aspiration in the first two patients, it resolved following a single 40-mg triamcinolone injection along with appropriate compression dressing for several weeks. In the third patient, triamcinolone was injected immediately after the initial aspiration of the haematoma, and the condition improved considerably. There were no side effects in any of the patients. To the best of our knowledge, this is the first report of successful treatment of large CEH using triamcinolone. Therefore, we suggest that triamcinolone injections be considered for the treatment of CEH.
Bandages ; Cicatrix ; Fascia ; Female ; Free Tissue Flaps ; Hemorrhage ; Humans ; Mammaplasty ; Seroma ; Subcutaneous Fat ; Superficial Back Muscles* ; Triamcinolone*

We started a prophylactic rehabilitation class from fiscal year 2016 and 1 year later we identified 4 problems, namely, a small number of participants (mean, 4.6), a small male population (ratio of 1 male to 9 females), no follow up because only 1 session was given per area, and a low home exercise rate (42.5%). For the class in fiscal year 2017, discussions were held with the comprehensive regional support center. Accordingly, relevant information was disseminated via public relations magazines, neighborhood circulars, and the local government to address the problems with the number of participants and sex ratio, and a series of 4 sessions were held per area to address the problems with the lack of follow-up and continuation of home exercise. Objective assessments (grip strength, one-leg standing, and knee extension strength) were included in the physical assessment. Through these measures, based on questionnaire results, we found that there was an increase in the number of participants and that more participants continued the exercise at home after the class was increased.