An Applicable Machine Learning Model Based on Preoperative Examinations Predicts Histology, Stage, and Grade for Endometrial Cancer

Frontiers in Oncology

Zhiqiang Zhang¹  Chongdong Liu¹  Huiqiao Gao¹  Shuzhen Wang¹  Jinfeng Li¹  Meizhu Xiao¹  Zhenyu Zhang¹  Andrea Romano²  Bert Delvoux²  Sofia Xanthoulea²  Zhixiang Wang³  Zhen Zhang³  Alberto Traverso³  Andre Dekker³  Yuan Su⁴  Ying Feng⁴  Linxue Qian⁴ 

[1] Department of Obstetrics and Gynecology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China;Department of Obstetrics and Gynecology, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands;Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, Netherlands;Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing, China;

关键词: machine learning;    endometrial carcinoma;    diagnosis;    prediction;    random forest;    preoperatively;   

DOI  :  10.3389/fonc.2022.904597

来源: DOAJ

【 摘 要 】

PurposeTo build a machine learning model to predict histology (type I and type II), stage, and grade preoperatively for endometrial carcinoma to quickly give a diagnosis and assist in improving the accuracy of the diagnosis, which can help patients receive timely, appropriate, and effective treatment.Materials and MethodsThis study used a retrospective database of preoperative examinations (tumor markers, imaging, diagnostic curettage, etc.) in patients with endometrial carcinoma. Three algorithms (random forest, logistic regression, and deep neural network) were used to build models. The AUC and accuracy were calculated. Furthermore, the performance of machine learning models, doctors’ prediction, and doctors with the assistance of models were compared.ResultsA total of 329 patients were included in this study with 16 features (age, BMI, stage, grade, histology, etc.). A random forest algorithm had the highest AUC and Accuracy. For histology prediction, AUC and accuracy was 0.69 (95% CI=0.67-0.70) and 0.81 (95%CI=0.79-0.82). For stage they were 0.66 (95% CI=0.64-0.69) and 0.63 (95% CI=0.61-0.65) and for differentiation grade 0.64 (95% CI=0.63-0.65) and 0.43 (95% CI=0.41-0.44). The average accuracy of doctors for histology, stage, and grade was 0.86 (with AI) and 0.79 (without AI), 0.64 and 0.53, 0.5 and 0.45, respectively. The accuracy of doctors’ prediction with AI was higher than that of Random Forest alone and doctors’ prediction without AI.ConclusionA random forest model can predict histology, stage, and grade of endometrial cancer preoperatively and can help doctors in obtaining a better diagnosis and predictive results.

【 授权许可】

Unknown