Objective To investigate the diagnostic value of a machine learning model based on magnetic resonance imaging (MRI), multimodal ultrasound, and clinical biochemical indicators for mesosalpinx cysts. Methods A retrospective analysis was conducted on MRI and ultrasound image features of 178 patients with suspected mesosalpinx cysts admitted to Gansu Provincial Maternity and Child-Caring Hospital between April 2021 and April 2024. Based on surgical pathology, patients were classified into a mesosalpinx cyst group (n=120) and a non-mesosalpinx cyst group (n=58). Variables were selected using LASSO regression and the XGBoost algorithm. Independent predictors were identified by multivariate logistic regression, and the model's diagnostic performance was assessed with receiver operating characteristic (ROC) curves. Results LASSO regression selected seven parameters most relevant to mesosalpinx cyst diagnosis: lesion margin, lesion shape, lesion size, T1-weighted intracystic hypointensity, T2-weighted intracystic hyperintensity, white blood cell (WBC) count, and high-density lipoprotein cholesterol. After intersecting variables from both algorithms via a Venn diagram, five key parameters were retained for logistic analysis: lesion margin, lesion size, T1-weighted intracystic hypointensity, T2-weighted intracystic hyperintensity, and WBC count. Multivariate logistic regression confirmed all variables as independent predictors (P<0.05). ROC curve analysis demonstrated the model's diagnostic performance with an area under the curve of 0.91, sensitivity of 89.66%, and specificity of 75.83%. The predictive model correctly identified 113 patients, with 12 misdiagnoses and 7 missed diagnoses, achieving higher accuracy compared to single diagnostic methods. Conclusion The machine learning model incorporating MRI and multimodal ultrasound image features shows high diagnostic efficacy for mesosalpinx cysts.
Key words
Magnetic resonance /
Multimodal ultrasound /
mesosalpinx cyst /
LASSO /
XGBoost
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