Objective To observe the value of MRI radiomics model for predicting postoperative prognosis of moderate carpal tunnel syndrome(CTS).Methods A total of 126 patients with moderate CTS who underwent endoscopic release and fat-suppressed proton density weighted imaging(PDWI)before operation were retrospectively enrolled.The patients were divided into good prognosis group(n=80)and poor prognosis group(n=46)based on postoperative functional evaluation,also randomly divided into training set and validation set at a ratio of 7∶3.Volume of interest(VOI)of the median nerve was obtained through delineating ROI of the affected wrist on fat suppressed PDWI.Radiomics features were extracted,and those associated with postoperative prognosis of CTS were screened in training set.Clinical prediction model,radiomics model and combined model of these two were established,and the predictive efficacy of the models were evaluated and compared according to the area under the curve(AUC)of receiver operating characteristic(ROC)curve.Results Patients in poor prognosis group were older than in good prognosis group(P＜0.05).A clinical model was constructed based on age.The radiomics model was constructed based on 6 radiomics features associated with postoperative prognosis of CTS,with predictive efficacy(AUC=0.872)higher than that of clinical model(AUC=0.604,P＜0.05)but not significantly different with that of the combined model(AUC=0.905,P＞0.05).Conclusion MRI radiomics model could be used to effectively predict postoperative prognosis of moderate CTS.

Objective To explore the diagnostic value of MRI radiomics analysis in mild carpal tunnel syndrome(CTS).Methods Seventy patients with mild CTS and 86 healthy volunteers who underwent wrist MRI examination were retrospectively selected.MRI fat-suppressed proton density weighted imaging(PDWI)were imported into 3D Slicer software,and the region of interest(ROI)delineation was performed by two radiologists independently.The 830 radiomics parameters were extracted,including first-order fea-tures,shape features,texture features,and wavelet-transform features.Radiomics parameter selection was performed through observer intraclass correlation coefficient(ICC),correlation analysis,and multivariate logistic regression.Five diagnostic models were estab-lished,including logistic regression,support vector machine,naive Bayes,decision tree,and random forest.Receiver operating charac-teristic(ROC)curve was used to analyze the diagnostic efficiency of the models.Results Seven radiomics features were selected for inclusion in the diagnostic models.The logistic regression model demonstrated the best performance,with an area under the curve(AUC)of 0.91[95%confidence interval(CI)0.86-0.96],a sensitivity of 88.63%,and a specificity of 89.00%in the training group.In the test group,the AUC was 0.92(95%CI 0.85-0.97),with a sensitivity of 90.48%and a specificity of 84.62%.Conclusion MRI radiomics analysis can be used to diagnose mild CTS,and the logistic regression model demonstrates superior diagnostic per-formance.

Objective To explore the diagnostic value of MRI radiomics analysis in mild carpal tunnel syndrome(CTS).Methods Seventy patients with mild CTS and 86 healthy volunteers who underwent wrist MRI examination were retrospectively selected.MRI fat-suppressed proton density weighted imaging(PDWI)were imported into 3D Slicer software,and the region of interest(ROI)delineation was performed by two radiologists independently.The 830 radiomics parameters were extracted,including first-order fea-tures,shape features,texture features,and wavelet-transform features.Radiomics parameter selection was performed through observer intraclass correlation coefficient(ICC),correlation analysis,and multivariate logistic regression.Five diagnostic models were estab-lished,including logistic regression,support vector machine,naive Bayes,decision tree,and random forest.Receiver operating charac-teristic(ROC)curve was used to analyze the diagnostic efficiency of the models.Results Seven radiomics features were selected for inclusion in the diagnostic models.The logistic regression model demonstrated the best performance,with an area under the curve(AUC)of 0.91[95%confidence interval(CI)0.86-0.96],a sensitivity of 88.63%,and a specificity of 89.00%in the training group.In the test group,the AUC was 0.92(95%CI 0.85-0.97),with a sensitivity of 90.48%and a specificity of 84.62%.Conclusion MRI radiomics analysis can be used to diagnose mild CTS,and the logistic regression model demonstrates superior diagnostic per-formance.

Objective To observe the value of MRI radiomics model for predicting postoperative prognosis of moderate carpal tunnel syndrome(CTS).Methods A total of 126 patients with moderate CTS who underwent endoscopic release and fat-suppressed proton density weighted imaging(PDWI)before operation were retrospectively enrolled.The patients were divided into good prognosis group(n=80)and poor prognosis group(n=46)based on postoperative functional evaluation,also randomly divided into training set and validation set at a ratio of 7∶3.Volume of interest(VOI)of the median nerve was obtained through delineating ROI of the affected wrist on fat suppressed PDWI.Radiomics features were extracted,and those associated with postoperative prognosis of CTS were screened in training set.Clinical prediction model,radiomics model and combined model of these two were established,and the predictive efficacy of the models were evaluated and compared according to the area under the curve(AUC)of receiver operating characteristic(ROC)curve.Results Patients in poor prognosis group were older than in good prognosis group(P＜0.05).A clinical model was constructed based on age.The radiomics model was constructed based on 6 radiomics features associated with postoperative prognosis of CTS,with predictive efficacy(AUC=0.872)higher than that of clinical model(AUC=0.604,P＜0.05)but not significantly different with that of the combined model(AUC=0.905,P＞0.05).Conclusion MRI radiomics model could be used to effectively predict postoperative prognosis of moderate CTS.