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  • Title: Multi-machine Learning Model Based on Habitat Subregions for Outcome Prediction in Adenomyosis Treated by Uterine Artery Embolization.
    Author: Jin W, Wang S, Wang T, Zhang D, Wang Y, Zhang G.
    Journal: Acad Radiol; 2024 Dec; 31(12):4985-4995. PubMed ID: 38845295.
    Abstract:
    RATIONALE AND OBJECTIVES: To establish and validate a predictive multi-machine learning model for the long-term efficacy of uterine artery embolization (UAE) in the treatment of adenomyosis based on habitat subregions. MATERIALS AND METHODS: Patients who underwent UAE for adenomyosis at institution A between November 2015 and June 2018 were included in the training cohort and those at institution B between June 2017 and June 2019 were included in the test cohort. The regions of interest (ROI) were manually segmented on the T2-weighted images (T2WI). The ROIs were subsequently partitioned into habitat subregions using k-means clustering. Radiomic features were extracted from each subregion on T1WI, T2WI, apparent diffusion coefficient, and contrast-enhanced images. The least absolute shrinkage and selection operator (LASSO) was used to select the subregion radiomics features. With the improvement in patients' symptoms at 36 months post-UAE, the habitat subregion features were trained using six machine-learning classifiers. The most suitable classifier was chosen based on model performance to establish the habitat radiomics model (HRM). The efficacy of the model was validated using both the training and test cohorts. Finally, a whole-region radiomics model (WRM) and clinical model (CM) were established. The Delong test was used to compare the predictive performance of the habitat subregion model and the two other models. RESULTS: The study included 258 patients, 191 in the training cohort and 67 in the test cohort. The ROIs were divided into four habitat subregions. Radiomics features were extracted from different sequence images of the subregions. After LASSO regression, 24 habitat subregion features were included in the model. Based on the receiver operating characteristic curve analysis, the area under the curve (AUC) of the HRM was 0.921 (95% CI, 0.857-0.985, training) and 0.890 (95% CI, 0.736-1.000, test). The AUCs for the WRM were 0.805 (95% CI, 0.737-0.872, training) and 0.693 (95% CI, 0.497-0.889, test). Compared to the HRM, the difference in predictive performance was statistically significant (p = 0.008, training; p = 0.007, test). The AUCs for the CM were 0.788 (95% CI, 0.711-0.866, training) and 0.735 (95% CI, 0.566-0.903, test). Compared to the HRM, there was a statistically significant difference in the training cohort (p = 0.014) but not in the test cohort (p = 0.186). CONCLUSION: The HRM can predict the long-term efficacy of UAE in the treatment of adenomyosis. The predictive performance was superior to that of both the WRM and CM, serving as an effective tool to assist interventional physicians in clinical decision-making.
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