Monogram and Heat Map on Magnetic Resonance Imaging to Evaluate the Recommendation for Myomectomy in Patients with Infertility: A Pilot Study.

Abstract

Uterine myomas can cause infertility. Studies are attempting to determine the indications for myomectomy. However, the multiplicity and localization of myomas complicate this issue. We aimed to develop a visualization tool to aid patients with infertility in their decision-making for myomectomy. We included 191 women with uterine myoma attending an outpatient infertility clinic, of whom 124 patients underwent myomectomy. Of these, 65 (52.4%) patients became pregnant within 17.6 months after surgery, and 54 (83.1%) of them had a live birth. A logistic regression model predicting the pregnancy rate (area under the curve, 0.82; 95% confidence interval, 0.74-0.89; validation value, 74.6%) was generated using the leave-one-out cross-validation method. This model incorporated five factors: age, maximum level of infertility intervention following myomectomy, presence of submucosal myoma, maximum diameter of the myoma, and type of myomas (multiple or single). We successfully visualized the degree of involvement of each factor in the pregnancy rate by developing a nomogram based on this model. We expanded the data from the preoperative magnetic resonance images and applied machine learning using a convolutional neural network. The classification accuracy was 71.4% for sensitivity and 77.7% for specificity. Heatmap images, generated using gradient-weighted class activation mapping to show the classification results of this model, could distinguish between myomas that required enucleation and those that did not. Although a larger sample size is needed to further validate our findings, this innovative pilot study demonstrates the potential of machine learning to refine assessment criteria and improve patient decision-making.