Knowledge of Thai women in cervical cancer etiology and screening.

Knowledge about cervical cancer screening and Human papilloma virus (HPV) influence on their awareness to the cervical cancer screening program. Most previous studies found inadequate knowledge and attitude among healthy women affect the low rate of screening. This study aimed to assess knowledge of cervical cancer screening and HPV in women who had abnormal cervical cancer screening in Bangkok. Thai women, aged ≥ 18 years old, who had abnormal cervical cancer screening and scheduled to colposcopy clinics of 10 participating hospitals were invited to participate in this cross-sectional study. The participants were asked to complete a self-answer questionnaire (Thai language). The questionnaire composed of 3 parts: (I) demographic data, (II) knowledge about cervical cancer screening and (III) knowledge about HPV. Among 499 women who answered the questionnaires, 2 had missing demographic data. The mean age of the participants was 39.28 ± 11.36 years. 70% of them had experience of cervical cancer screening, with 22.7% had previous abnormal cytologic results. Out of 14 questions, the mean score of knowledge about cervical cancer screening was 10.04 ± 2.37. Only 26.9% had good knowledge about cervical cancer screening. Nearly 96% of woman did not know that screening should be done. After excluding 110 women who had never known about HPV, 25.2% had good knowledge about HPV. From multivariable analysis, only younger age (≤ 40 years) was associated with good knowledge of cervical cancer screening and HPV. In the conclusion, only 26.9% of women in this study had good knowledge regarding cervical cancer screening. Likewise, 20.1% of women who had ever heard about HPV has good knowledge about HPV. Providing information about cervical cancer screening and HPV should improve the women's knowledge and better adherence to the screening procedure.

Predictive analytical model for ectopic pregnancy diagnosis: Statistics vs. machine learning.

Objective: Ectopic pregnancy (EP) is well known for its critical maternal outcome. Early detection could make the difference between life and death in pregnancy. Our aim was to make a prompt diagnosis before the rupture occur. Thus, the predictive analytical models using both conventional statistics and machine learning (ML) methods were studied. Materials and methods: A retrospective cohort study was conducted on 407 pregnancies with unknown location (PULs): 306 PULs for internal validation and 101 PULs for external validation, randomized with a nested cross-validation technique. Using a set of 22 study features based on clinical factors, serum marker and ultrasound findings from electronic medical records, analyzing with neural networks (NNs), decision tree (DT), support vector machines (SVMs), and a statistical logistic regression (LR). Diagnostic performances were compared with the area under the curve (ROC-AUC), including sensitivity and specificity for decisional use. Results: Comparing model performance (internal validation) to predict EP, LR ranked first, with a mean ROC-AUC ± SD of 0.879 ± 0.010. In testing data (external validation), NNs ranked first, followed closely by LR, SVMs, and DT with average ROC-AUC ± SD of 0.898 ± 0.027, 0.896 ± 0.034, 0.882 ± 0.029, and 0.856 ± 0.033, respectively. For clinical aid, we report sensitivity of mean ± SD in LR: 90.20% ± 3.49%; SVM: 89.79% ± 3.66%; DT: 89.22% ± 4.53%; and NNs: 86.92% ± 3.24%, consecutively. However, specificity ± SD was ranked by NNs, followed by SVMs, LR, and DT, which were 82.02 ± 8.34%, 80.37 ± 5.15%, 79.65% ± 6.01%, and 78.97% ± 4.07%, respectively. Conclusion: Both statistics and the ML model could achieve satisfactory predictions for EP. In model learning, the highest ranked model was LR, showing that EP prediction might possess linear or causal data pattern. However, in new testing data, NNs could overcome statistics. This highlights the potency of ML in solving complicated problems with various patterns, while overcoming generalization error of data.

Predictive Analytic Model for Diagnosis of Ectopic Pregnancy.

Objective: Ectopic pregnancy (EP) is a serious condition. Delayed diagnosis could lead to life-threatening outcomes. The study aimed to develop a diagnostic predictive model for EP to approach suspected cases with prompt intervention before the rupture occurred. Methods: A retrospective cross-sectional study enrolled 347 pregnant women presenting first-trimester complications (abdominal pain or vaginal bleeding) with diagnosis suspected of pregnancy of unknown location, who were eligible and underwent chart review. The data including clinical risk factors, signs and symptoms, serum human chorionic gonadotropin (hCG), and ultrasound findings were analyzed. The statistical predictive score was developed by performing logistic regression analysis. The testing data of 30 patients were performed to test the validation of predictive scoring. Results: From a total of 22 factors, logistic regression method-derived scoring model was based on five potent factors (history of pelvic inflammatory disease, current use of emergency pills, cervical motion tenderness, serum hCG ≥1,000 mIU/ml, and ultrasound finding of adnexal mass) using a cutoff score ≥3. This predictive index score was able to determine ectopic pregnancy with an accuracy of 77.8% [95% confidence interval (CI) = 73.1-82.1], specificity of 91.0% (95% CI = 62.1-72.0), sensitivity of 67.0% (95% CI = 88.0-94.0), and area under the curve of 0.906 (95% CI = 0.875-0.937). In the validation group, no patient with negative result of this score had an EP. Conclusion: Statistical predictive score was derived with high accuracy and applicable performance for EP diagnosis. This score could be used to support clinical decision making in routine practice for management of EP.

Prognosis of hepatoid yolk sac tumor in women: what's up, Doc?

Ovarian yolk sac tumors are highly malignant germ cell tumors that commonly occur in young women. The hepatoid yolk sac tumor is a variant form of yolk sac tumor in which there has been extensive tumor differentiation to early liver tissue. Hepatoid differentiation is traditionally considered to signify a poor prognosis. This review focuses on diagnostic criteria and establishes the optimal treatment for patients with hepatoid yolk sac tumor. Immunohistochemical stains are useful for distinguishing hepatoid yolk sac tumor from the other hepatoid-appearing tumors. With a multidisciplinary treatment approach using platinum-based regimens, the outcome is similar to those of any yolk sac tumor.

The treatment outcome of adenocarcinoma of uterine cervix at Phramongkutklao Hospital.

OBJECTIVE: To determine the survival rate of patients with adenocarcinoma of the cervix after completing treatment at Phramongkutklao Hospital. MATERIAL AND METHOD: Retrospective review of medical records of 229 patients with adenocarcinoma of the cervix who had completed treatment at Phramongkutklao Hospital between October 1991 to September 2006. RESULTS: Overall 2, 5 and 10-year survival for patients with adenocarcinoma of the cervix was 78.9%, 70.1% and 67.0%, respectively. The 5-year survival rates for stages I, II, III and IV were 94.6%, 76.1%, 49.2% and 0, respectively. Five-year survival of patients with locally advanced adenocarcinoma of the cervix treated with concurrent chemoradiation was comparable to that of patients treated with radiation alone (64.0 vs. 62.4%). Survival of group treated by radiation plus surgery was not significantly different to the group recieved radiation alone. There have been no serious complications from the treatments. CONCLUSION: Survival of patients with adenocarcinoma of the cervix shows a direct correlation with stage. The survival for each treatment modality was comparable. Adjuvant hysterectomy after radiation in adenocarcinoma of the cervix stage IIB and IIIB did not improve long-term survival.