Scholarlink

Purpose <p>To evaluate the application of multi-parametric MRI (MP-MRI) combined with radiomics in diagnosing and grading endometrial fibrosis (EF).</p> Methods <p>A total of 74 patients with severe endometrial fibrosis (SEF), 41 patients with mild to moderate fibrosis (MMEF) confirmed by hysteroscopy, and 40 healthy women of reproductive age were prospectively enrolled. The enrolled data were randomly stratified and divided into a train set (108 cases: 28 healthy women, 29 with MMEF, and 51 with SEF) and a test set (47 cases: 12 healthy women, 12 MMEF and 23 SEF) at a ratio of 7:3. All participants underwent T2 and DWI sequence scans. By freely delineating the volume of interest (VOI) of the endometrium in three subgroups, radiomic features were extracted and selected. Two feature selection methods and four machine learning (ML) classifiers were combined in pairs to establish five prediction models [model<sub>1</sub> (T2 + ADC + clinical data), model<sub>2</sub> (T2 + ADC), model<sub>3</sub> (T2), model<sub>4</sub> (ADC), and model<sub>5</sub> (clinical data)], resulting in a total of 40 classification models. The predictive performance of all models was evaluated using the area under the curve (AUC), F1-score, and accuracy (ACC).</p> Results <p>The “UFS-LR” model, which combined unsupervised feature selection (UFS) with the logistic regression (LR) classifier, performed the best, with an average AUC of 0.92 on the test set. Among the five models constructed via UFS-LR, model<sub>1</sub> exhibited the best performance, with average AUC, F1-score, and ACC values of 0.92, 0.80, and 0.81, respectively. T2-related features were the most significant in distinguishing fibrosis levels, with T2_wavelet-LLL_gldm_DependenceVariance being the most important characteristic among them.</p> Conclusion <p>MP-MRI radiomics analysis using ML has excellent performance in grading EF. This approach is non-invasive and has the potential to reduce the reliance on hysteroscopy.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Multi-parametric MRI combined with radiomics for the diagnosis and grading of endometrial fibrosis

  • 期刊论文

  • 发表日期：

    2025年1月22日

查看全文
  • Huanhuan Wang,
  • Li Zhu,
  • Hui Zhu,
  • Jie Meng,
  • Huanhuan Liang,
  • Danyan Li,
  • Yali Hu,
  • Zhengyang Zhou

摘要

Purpose

To evaluate the application of multi-parametric MRI (MP-MRI) combined with radiomics in diagnosing and grading endometrial fibrosis (EF).

Methods

A total of 74 patients with severe endometrial fibrosis (SEF), 41 patients with mild to moderate fibrosis (MMEF) confirmed by hysteroscopy, and 40 healthy women of reproductive age were prospectively enrolled. The enrolled data were randomly stratified and divided into a train set (108 cases: 28 healthy women, 29 with MMEF, and 51 with SEF) and a test set (47 cases: 12 healthy women, 12 MMEF and 23 SEF) at a ratio of 7:3. All participants underwent T2 and DWI sequence scans. By freely delineating the volume of interest (VOI) of the endometrium in three subgroups, radiomic features were extracted and selected. Two feature selection methods and four machine learning (ML) classifiers were combined in pairs to establish five prediction models [model1 (T2 + ADC + clinical data), model2 (T2 + ADC), model3 (T2), model4 (ADC), and model5 (clinical data)], resulting in a total of 40 classification models. The predictive performance of all models was evaluated using the area under the curve (AUC), F1-score, and accuracy (ACC).

Results

The “UFS-LR” model, which combined unsupervised feature selection (UFS) with the logistic regression (LR) classifier, performed the best, with an average AUC of 0.92 on the test set. Among the five models constructed via UFS-LR, model1 exhibited the best performance, with average AUC, F1-score, and ACC values of 0.92, 0.80, and 0.81, respectively. T2-related features were the most significant in distinguishing fibrosis levels, with T2_wavelet-LLL_gldm_DependenceVariance being the most important characteristic among them.

Conclusion

MP-MRI radiomics analysis using ML has excellent performance in grading EF. This approach is non-invasive and has the potential to reduce the reliance on hysteroscopy.