Establishment and Verification of Logistic Regression Model for Qualitative Diagnosis of Ovarian Cancer Based on MRI and Ultrasound Signs

Objective. To explore the establishment and verification of logistic regression model for qualitative diagnosis of ovarian cancer based on MRI and ultrasonic signs. Method. 207 patients with ovarian tumors in our hospital from April 2018 to April 2021 were selected, of which 138 were used as the training group for model creation and 69 as the validation group for model evaluation. The differences of MRI and ultrasound signs in patients with ovarian cancer and benign ovarian tumor in the training group were analyzed. The risk factors were screened by multifactor unconditional logistic regression analysis, and the regression equation was established. The self-verification was carried out by subject working characteristics (ROC), and the external verification was carried out by K-fold cross verification. Result. There was no significant difference in age, body mass index, menstruation, dysmenorrhea, times of pregnancy, cumulative menstrual years, and marital status between the two groups ( P > 0.05 ). After logistic regression analysis, the diagnostic model of ovarian cancer was established: logit   P = − 1.153 + MRI   signs : morphology × 1.459 + boundary × 1.549 + reinforcement × 1.492 + tumor   components × 1.553 + ultrasonic   signs : morphology × 1.594 + mainly   real × 1.417 + separated   form × 1.294 + large   nipple × 1.271 + blood   supply × 1.364 ; self-verification: AUC of the model is 0.883, diagnostic sensitivity is 93.94%, and specificity is 80.95%; K-fold cross validation: the training accuracy was 0.904 ± 0.009 and the prediction accuracy was 0.881 ± 0.049 . Conclusion. Irregular shape, unclear boundary, obvious enhancement in MRI signs, cystic or solid tumor components and irregular shape, solid-dominated shape, thick septate shape, large nipple, and abundant blood supply in ultrasound signs are independent risk factors for ovarian cancer. After verification, the diagnostic model has good accuracy and stability, which provides basis for clinical decision-making.