{"title":"卵巢癌的关联和因果关系--构建预测模型。","authors":"Jing Liu, Tingting Hu, Yulan Guan, Jinguo Zhai","doi":"10.2147/IJWH.S462883","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>To explore the risk and protective factors for developing ovarian cancer and construct a risk prediction model.</p><p><strong>Methods: </strong>Information related to patients diagnosed with ovarian cancer on the electronic medical record data platform of three tertiary hospitals in Guangdong Province from May 2018 to September 2023 was collected as the case group. Patients with non-ovarian cancer who attended the clinic during the same period were included in the control group. Logistic regression analysis was used to screen the independent variables and explore the factors associated with the development of ovarian cancer. An ovarian cancer risk prediction model was constructed using a decision tree C4.5 algorithm. The ROC and calibration curves were plotted, and the model was validated.</p><p><strong>Results: </strong>Logistic regression analysis identified independent risk and protective factors for ovarian cancer. The sample size was divided into training and test sets in a ratio of 7:3 for model construction and validation. The AUC of the training and test sets of the decision tree model were 0.961 (95% CI:0.944-0.978) and 0.902 (95% CI:0.840-0.964), respectively, and the optimal cut-off values and their coordinates were 0.532 (0.091, 0.957), and 0.474 (0.159, 0.842) respectively. The accuracies of the training and test sets were 93.3% and 84.2%, respectively, and their sensitivities were 95.7% and 84.2%, respectively.</p><p><strong>Conclusion: </strong>The constructed ovarian cancer risk prediction model has good predictive ability, which is conducive to improving the efficiency of early warning of ovarian cancer in high-risk groups.</p>","PeriodicalId":14356,"journal":{"name":"International Journal of Women's Health","volume":null,"pages":null},"PeriodicalIF":2.5000,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11193432/pdf/","citationCount":"0","resultStr":"{\"title\":\"The Associations and Causal Relationships of Ovarian Cancer - Construction of a Prediction Model.\",\"authors\":\"Jing Liu, Tingting Hu, Yulan Guan, Jinguo Zhai\",\"doi\":\"10.2147/IJWH.S462883\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>To explore the risk and protective factors for developing ovarian cancer and construct a risk prediction model.</p><p><strong>Methods: </strong>Information related to patients diagnosed with ovarian cancer on the electronic medical record data platform of three tertiary hospitals in Guangdong Province from May 2018 to September 2023 was collected as the case group. Patients with non-ovarian cancer who attended the clinic during the same period were included in the control group. Logistic regression analysis was used to screen the independent variables and explore the factors associated with the development of ovarian cancer. An ovarian cancer risk prediction model was constructed using a decision tree C4.5 algorithm. The ROC and calibration curves were plotted, and the model was validated.</p><p><strong>Results: </strong>Logistic regression analysis identified independent risk and protective factors for ovarian cancer. The sample size was divided into training and test sets in a ratio of 7:3 for model construction and validation. The AUC of the training and test sets of the decision tree model were 0.961 (95% CI:0.944-0.978) and 0.902 (95% CI:0.840-0.964), respectively, and the optimal cut-off values and their coordinates were 0.532 (0.091, 0.957), and 0.474 (0.159, 0.842) respectively. The accuracies of the training and test sets were 93.3% and 84.2%, respectively, and their sensitivities were 95.7% and 84.2%, respectively.</p><p><strong>Conclusion: </strong>The constructed ovarian cancer risk prediction model has good predictive ability, which is conducive to improving the efficiency of early warning of ovarian cancer in high-risk groups.</p>\",\"PeriodicalId\":14356,\"journal\":{\"name\":\"International Journal of Women's Health\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2024-06-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11193432/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Women's Health\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.2147/IJWH.S462883\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"OBSTETRICS & GYNECOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Women's Health","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2147/IJWH.S462883","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"OBSTETRICS & GYNECOLOGY","Score":null,"Total":0}
The Associations and Causal Relationships of Ovarian Cancer - Construction of a Prediction Model.
Purpose: To explore the risk and protective factors for developing ovarian cancer and construct a risk prediction model.
Methods: Information related to patients diagnosed with ovarian cancer on the electronic medical record data platform of three tertiary hospitals in Guangdong Province from May 2018 to September 2023 was collected as the case group. Patients with non-ovarian cancer who attended the clinic during the same period were included in the control group. Logistic regression analysis was used to screen the independent variables and explore the factors associated with the development of ovarian cancer. An ovarian cancer risk prediction model was constructed using a decision tree C4.5 algorithm. The ROC and calibration curves were plotted, and the model was validated.
Results: Logistic regression analysis identified independent risk and protective factors for ovarian cancer. The sample size was divided into training and test sets in a ratio of 7:3 for model construction and validation. The AUC of the training and test sets of the decision tree model were 0.961 (95% CI:0.944-0.978) and 0.902 (95% CI:0.840-0.964), respectively, and the optimal cut-off values and their coordinates were 0.532 (0.091, 0.957), and 0.474 (0.159, 0.842) respectively. The accuracies of the training and test sets were 93.3% and 84.2%, respectively, and their sensitivities were 95.7% and 84.2%, respectively.
Conclusion: The constructed ovarian cancer risk prediction model has good predictive ability, which is conducive to improving the efficiency of early warning of ovarian cancer in high-risk groups.
期刊介绍:
International Journal of Women''s Health is an international, peer-reviewed, open access, online journal. Publishing original research, reports, editorials, reviews and commentaries on all aspects of women''s healthcare including gynecology, obstetrics, and breast cancer. Subject areas include: Chronic conditions including cancers of various organs specific and not specific to women Migraine, headaches, arthritis, osteoporosis Endocrine and autoimmune syndromes - asthma, multiple sclerosis, lupus, diabetes Sexual and reproductive health including fertility patterns and emerging technologies to address infertility Infectious disease with chronic sequelae including HIV/AIDS, HPV, PID, and other STDs Psychological and psychosocial conditions - depression across the life span, substance abuse, domestic violence Health maintenance among aging females - factors affecting the quality of life including physical, social and mental issues Avenues for health promotion and disease prevention across the life span Male vs female incidence comparisons for conditions that affect both genders.