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Background: Current diagnostic methods for colorectal cancer (CRC) are colonoscopy and sigmoidoscopy, which are invasive and complex procedures with possible complications. This study aimed to determine models for CRC identification that involve minimally invasive, affordable, portable, and accurate screening variables. Methods: This was a retrospective study that used data from electronic medical records of patients with CRC and healthy individuals between July 2017 and June 2018. Laboratory data, including liver enzymes, lipid profiles, complete blood counts, and tumor biomarkers, were extracted from the electronic medical records. Five machine learning models (logistic regression, random forest, k-nearest neighbors, support vector machine [SVM], and naïve Bayes) were used to identify CRC. The performances were evaluated using the areas under the curve (AUCs), sensitivity, specificity, positive predictive values (PPV), and negative predictive values (NPV). Results: A total of 1164 electronic medical records (CRC patients: 582; healthy controls: 582) were included. The logistic regression model achieved the highest performance in identifying CRC (AUC: 0.865, sensitivity: 89.5%, specificity: 83.5%, PPV: 84.4%, NPV: 88.9%). The first four weighted features in the model were carcinoembryonic antigen (CEA), hemoglobin (HGB), lipoprotein (a) (Lp(a)), and high-density lipoprotein (HDL). A diagnostic model for CRC was established based on the four indicators, with an AUC of 0.849 (0.840-0.860) for identifying all CRC patients, and it performed best in discriminating patients with late colon cancer from healthy individuals with an AUC of 0.905 (0.889-0.929). Conclusions: The logistic regression model based on CEA, HGB, Lp(a), and HDL might be a powerful, noninvasive, and cost-effective method to identify CRC.

Few researches have been focused on the treatment delay of rural-to-urban migrants in China. Our study aimed to investigate the effect of migration duration on treatment delay among rural-to-urban migrants in tertiary hospitals. A cross-sectional study was conducted based on a sample of 727 patients and surveyed factors including sociodemographics, medical costs, migration, treatment delay, and health cost-coping strategies. Totally, 727 patients were included, of which 61 delayed their treatment and 666 had no treatment delay. Statistically significant differences were found between different migration duration groups in marital status, education, insurance, family annual income, residency, payment before treatment, reported disease, and migration duration ( P < .05). The results from multiple logistic regression showed that migration between 1 and 5 years (adjusted odds ratio [OR] = 7.24; 95% confidence interval [CI] = 1.59-32.87; P < .05) was considered the significant contributing risk factor for treatment delay after adjusting for age, sex, and other variables. To cope with their health expenditure, patients with treatment delay tended to use less savings and borrow more money than those without. Rural-to-urban migrants with 1 to 5 years of migration were the most vulnerable group of having treatment delay. Migrants were more likely to borrow money to cope with the health expenditure. Targeted services should be provided to meet different needs of migrants according to migration duration.

Gastric cancer (GC) is fifth most frequently diagnosed cancer and second leading cause of cancer in China. More than 80% of GC are diagnosed at an advanced stage due to low uptake rate of invasive screening method. The performance of methylated SFRP2 test was evaluated in 236 plasma samples, including 92 patients with GC, 16 intestinal metaplasia patients, 26 gastric fundic gland polyp patients, 13 small adenoma patients, 39 hyperplastic polyp patients, and 50 control patients. The sensitivity of plasma methylated SFRP2 was compared to serum CEA, CA72-4, CA19-9, and CA242 results in 79 patients with GC. The sensitivities for detecting GC and gastric intestinal metaplasia by methylated SFRP2 test were 60.9% and 56.3% with a specificity of 86.0%. Methylated SFRP2 test had significantly higher positive detection rate for patients with GC than gastric fundic gland polyp, small adenoma, and hyperplastic polyp patients. In 79 patients with GC, the sensitivities of CEA, CA72-4, CA19-9, and CA242 for detecting GC were 22.8%, 16.5%, 12.7%, and 11.4%. In comparison, the sensitivity of methylated SFRP2 test for detecting GC was 58.2%. Plasma methylated SFRP2 test may become a valuable tool for the noninvasive detection of GC and precursor lesions and showed higher sensitivity than serum tumor markers.

Primary ovarian pregnancy is rare in the classifications of ectopic pregnancy with an incidence of 0.5-3% of all ectopic gestations. The surgical management of ovarian pregnancy is different from the operation of salpingocyesis. Preoperative diagnosis in early pregnancy can help to guide the optimal management. We have reported the case of a 37- year-old woman who presented with a naturally conceived ovarian pregnancy. Transvaginal ultrasound was used in this patient, and made the correct diagnosis of ovarian pregnancy before surgery. We found that transvaginal ultrasound can be helpful in the diagnosis of the location of ectopic pregnancies.