首页|McGill University Reports Findings in Endometrial Cancer (Machine learning for prediction of concurrent endometrial carcinoma in patients diagnosed with endometrial intraepithelial neoplasia)
McGill University Reports Findings in Endometrial Cancer (Machine learning for prediction of concurrent endometrial carcinoma in patients diagnosed with endometrial intraepithelial neoplasia)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
New research on Oncology - Endometrial Cancer is the subject of a report. According to news reporting originating from Quebec, Canada, by NewsRx correspondents, research stated, “To identify predictive clinico-pathologic factors for concurrent endometrial carcinoma (EC) among patients with endometrial intraepithelial neoplasia (EIN) using machine learning. a retrospective analysis of 160 patients with a biopsy proven EIN.” Our news editors obtained a quote from the research from McGill University, “We analyzed the performance of multiple machine learning models (n = 48) with different parameters to predict the diagnosis of postoperative EC. The prediction variables included: parity, gestations, sampling method, endometrial thickness, age, body mass index, diabetes, hypertension, serum CA-125, preoperative histology and preoperative hormonal therapy. Python ‘sklearn’ library was used to train and test the models. The model performance was evaluated by sensitivity, specificity, PPV, NPV and AUC. Five iterations of internal crossvalidation were performed, and the mean values were used to compare between the models. Of the 160 women with a preoperative diagnosis of EIN, 37.5% (60) had a post-op diagnosis of EC. In univariable analysis, there were no significant predictors of EIN. For the five best machine learning models, all the models had a high specificity (71%-88%) and a low sensitivity (23%-51%). Logistic regression model had the highest specificity 88%, XG Boost had the highest sensitivity 51%, and the highest positive predictive value 62% and negative predictive value 73%. The highest area under the curve was achieved by the random forest model 0.646. Even using the most elaborate AI algorithms, it is not possible currently to predict concurrent EC in women with a preoperative diagnosis of EIN.”
QuebecCanadaNorth and Central AmericaCancerCarcinomasCyborgsDiagnostics and ScreeningEmerging TechnologiesEndometrial CancerGynecologyHealth and MedicineMachine LearningNeoplasiaOncologySurgeryWomen’s Health
NETL
NSTL
