UNASSIGNED: This study aims to develop an artificial intelligence model utilizing clinical blood markers, ultrasound data, and breast biopsy pathological information to predict the distant metastasis in breast cancer patients.
UNASSIGNED: Data from two medical centers were utilized, Clinical blood markers, ultrasound data, and breast biopsy pathological information were separately extracted and selected. Feature dimensionality reduction was performed using Spearman correlation and LASSO regression. Predictive models were constructed using LR and LightGBM machine learning algorithms and validated on internal and external validation sets. Feature correlation analysis was conducted for both models.
UNASSIGNED: The LR model achieved AUC values of 0.892, 0.816, and 0.817 for the training, internal validation, and external validation cohorts, respectively. The LightGBM model achieved AUC values of 0.971, 0.861, and 0.890 for the same cohorts, respectively. Clinical decision curve analysis showed a superior net benefit of the LightGBM model over the LR model in predicting distant metastasis in breast cancer. Key features identified included creatine kinase isoenzyme (CK-MB) and alpha-hydroxybutyrate dehydrogenase.
UNASSIGNED: This study developed an artificial intelligence model using clinical blood markers, ultrasound data, and pathological information to identify distant metastasis in breast cancer patients. The LightGBM model demonstrated superior predictive accuracy and clinical applicability, suggesting it as a promising tool for early diagnosis of distant metastasis in breast cancer.

BACKGROUND: High risk human papillomavirus (HPV) infection is the major cause of cervical cancer. Several epidemiological studies have performed HPV screening in Chinese women, but no report was for Shanghai suburb women.
OBJECTIVE: To understand the prevalence of HPV infection and risk factors in Shanghai suburbs.
METHODS: Between March 2011 and May 2011, 10,000 female volunteers lived in Fengxian District of Shanghai were recruited for the detection of 21 HPV types using PCR and fast flow hybridization of gene chip array. For the 508 HPV-positive patients, we performed the liquid-based ThinPrep cytology test (TCT) and histological examination for the diagnosis of local cervical lesions. The questionnaire surveyed demographic and behavioral indicators for the evaluation of risk factors of HPV infection.
RESULTS: We found that the HPV-positive rate was 12.6%. The five top HPV types were as follows (in descending order): HPV52, 16, 58, 18 and 33. Moreover, HPV-positive rates were higher in women with older age, lower educational level, younger age of the first sexual intercourse, multiple sexual partners, no usage of condom for contraception, multiple deliveries, vaginal delivery, menopause, vaginal inflammation, cervical erosion and no regular cervical cytological examination. We also found that an HPV genotyping in combination with TCT and histological examination could improve early diagnosis for local cervical lesions.
CONCLUSIONS: HPV infection was associated with age, sexual behavior and chronic inflammation of the cervix and vagina. We recommend popularizing HPV genotyping in women with high risk factors for the early diagnosis and prevention of cervical cancer.