Osteosarcoma is the most common primary malignant tumor in the orthopaedic field. Recently, the management of osteosarcoma was evolved in many aspects and the improved results were reported in many articles. The purpose of this study is to evaluate the changes of clinical findings and management modalities in osteosarcoma since 1980. We reviewed 127 osteosarcomas managed from 1980 to 1992. The Enneking surgical stage was as follows; stage I 12 cases, stage II 98 cases and stage III 17 cases. They were subclassified into classic(97 cases), parosteal(12 cases), telangiectatic(5 cases), secondary(4 cases), periosteal(2 cases), gnathic(2 cases), high grade surface(1 cases) and undetermined(4 cases). The disease-free survival rate was evaluated for the patients of stage II classic osteosarcomas treated with amputation(27 cases) or limb-salvage operation(23 cases), and average follow up period of them was 49 months. In 27 cases of amputation adjuvant chemotherapy was administered in 23 cases. And in 23 patients treated with limb-salvage operation, neoadjuvant and adjuvant chemotherapy were done in 19 cases and only adjuvant chemotherapy in 2 cases. The clinical changes since 1980 were as follows: (1) The mean duration from symptom onset to diagnosis was decreased gradually from 6.5 months(1980) to 3.1 months(1992). (2) The proportion of curative surgery was increased from 40%(1980) to 87%(1992) in stage I and II. (3) The proportion of limb-salvage operation was rapidly increased upto 88% since 1985. The 5 year disease-free survival rate of stage II classic osteosarcoma was 36% with amputation and 67% with limb-salvage operation.
Amputation ; Chemotherapy, Adjuvant ; Diagnosis ; Disease-Free Survival ; Follow-Up Studies ; Humans ; Osteosarcoma

Fine needle aspiration biopsy cytology (FNA) for diagnosis of a variety of breast tum- ors has been proven to be a simple, safe, and cost saving diagnostic methodology with high accuracy. Cytologic specimens from 1,029 fine needle aspirations of the breast during last 3-year period were reviewed and subsequent biopsies from 107 breast lesions were reevaluated for cytohistological correlation. FNA had a sensitivity of 81.6% and a specificity of 98.3%. One out of 107 cases bio- psied revealed a false positive result (0.9%) and the case was due to misinterpretation of apocrine metaplastic cells in necrotic backgound as malignant cells. A false negative rate was 8.4% (9 of 107 cases biopsied). Six of 9 false negative cases were resulted from insufficient aspirates for diagnosis, and remaining three of 9 false negative cases revealed extensive necrosis with no or scanty viable cells on smears. The results indicate that for reducing false positive and false negative rates of FNA, an experienced cytopathologist and a proficient aspirator are of great importance.
Adenoma, Pleomorphic ; Aspirations (Psychology) ; Biopsy ; Biopsy, Fine-Needle ; Breast ; Carcinoma, Adenoid Cystic ; Carcinoma, Mucoepidermoid ; Child* ; Cost Savings ; Diagnosis ; Humans ; Necrosis ; Needles ; Sensitivity and Specificity ; Urinary Tract Infections* ; Urinary Tract* ; Vesico-Ureteral Reflux*

No abstract available.
Child* ; Heart Septal Defects, Ventricular* ; Humans

No abstract available.
Cell Line* ; Cisplatin* ; Doxorubicin* ; Osteosarcoma*

Urologic surgeries are widely performed, and the cases have increased owing to the fact that the elderly population is growing. The narrow and limited surgical space is a challenge in performing most urologic surgeries. Additionally, the elderly population is exposed to the risk of perioperative complications; therefore, a comprehensive understanding and approach are required to provide optimized anesthesia during surgery. We have searched the literature on anesthesia for urologic surgeries and summarized the anesthetic considerations for urologic surgeries.

Purpose: To develop a machine learning (ML) classifier for predicting post-induction hypotension (PIH) in non-cardiac surgeries. Materials and Methods: Preoperative data and early vital signs were obtained from 3669 cases in the VitalDB database, an opensource registry. PIH was defined as sustained mean arterial pressure (MAP) <65 mm Hg within 20 minutes since induction or from induction to incision. Six different ML algorithms were used to create binary classifiers to predict PIH. The primary outcome was the area under the receiver operating characteristic curve (AUROC) of ML classifiers. Results: A total of 2321 (63.3%) cases exhibited PIH. Among ML classifiers, the random forest regressor and extremely gradient boosting regressor showed the highest AUROC, both recording a value of 0.772. Excluding these models, the light gradient boosting machine regressor showed the second highest AUROC [0.769; 95% confidence interval (CI), 0.767–0.771], followed by the gradient boosting regressor (0.768; 95% CI, 0.763–0.772), AdaBoost regressor (0.752; 95% CI, 0.743–0.761), and automatic relevance determination regression (0.685; 95% CI, 0.669–0.701). The top three important features were mean diastolic blood pressure (DBP), minimum MAP, and minimum DBP from anesthetic induction to tracheal intubation, and these features were lower in cases with PIH (all p<0.001). Conclusion ML classifiers exhibited moderate performance in predicting PIH, and have the potential for real-time prediction.

Purpose: To develop a machine learning (ML) classifier for predicting post-induction hypotension (PIH) in non-cardiac surgeries. Materials and Methods: Preoperative data and early vital signs were obtained from 3669 cases in the VitalDB database, an opensource registry. PIH was defined as sustained mean arterial pressure (MAP) <65 mm Hg within 20 minutes since induction or from induction to incision. Six different ML algorithms were used to create binary classifiers to predict PIH. The primary outcome was the area under the receiver operating characteristic curve (AUROC) of ML classifiers. Results: A total of 2321 (63.3%) cases exhibited PIH. Among ML classifiers, the random forest regressor and extremely gradient boosting regressor showed the highest AUROC, both recording a value of 0.772. Excluding these models, the light gradient boosting machine regressor showed the second highest AUROC [0.769; 95% confidence interval (CI), 0.767–0.771], followed by the gradient boosting regressor (0.768; 95% CI, 0.763–0.772), AdaBoost regressor (0.752; 95% CI, 0.743–0.761), and automatic relevance determination regression (0.685; 95% CI, 0.669–0.701). The top three important features were mean diastolic blood pressure (DBP), minimum MAP, and minimum DBP from anesthetic induction to tracheal intubation, and these features were lower in cases with PIH (all p<0.001). Conclusion ML classifiers exhibited moderate performance in predicting PIH, and have the potential for real-time prediction.

Purpose: To develop a machine learning (ML) classifier for predicting post-induction hypotension (PIH) in non-cardiac surgeries. Materials and Methods: Preoperative data and early vital signs were obtained from 3669 cases in the VitalDB database, an opensource registry. PIH was defined as sustained mean arterial pressure (MAP) <65 mm Hg within 20 minutes since induction or from induction to incision. Six different ML algorithms were used to create binary classifiers to predict PIH. The primary outcome was the area under the receiver operating characteristic curve (AUROC) of ML classifiers. Results: A total of 2321 (63.3%) cases exhibited PIH. Among ML classifiers, the random forest regressor and extremely gradient boosting regressor showed the highest AUROC, both recording a value of 0.772. Excluding these models, the light gradient boosting machine regressor showed the second highest AUROC [0.769; 95% confidence interval (CI), 0.767–0.771], followed by the gradient boosting regressor (0.768; 95% CI, 0.763–0.772), AdaBoost regressor (0.752; 95% CI, 0.743–0.761), and automatic relevance determination regression (0.685; 95% CI, 0.669–0.701). The top three important features were mean diastolic blood pressure (DBP), minimum MAP, and minimum DBP from anesthetic induction to tracheal intubation, and these features were lower in cases with PIH (all p<0.001). Conclusion ML classifiers exhibited moderate performance in predicting PIH, and have the potential for real-time prediction.