Objective To evaluate the surgical method and the results of endoscopic decompression and anterior transposition of the ulnar nerve for treatment of cubital tunnel syndrome. Methods Between May 2008 and August 2009, 13 cases of cubital tunnel syndrome were treated with endoscopic decompression and anterior transposition of the ulnar nerve. There were 4 males and 9 females with an average age of 47.5 years (range, 32-60 years). The injury was caused by fractures of the humeral medial condyle in 1 case, by long working in elbow flexion position with no obvious injury in 10 cases, and subluxafion of ulnar nerve in 2 cases. The locations were the left side in 6 cases and the right side in 7 cases. The disease duration was 4-30 months. The time from onset to operation was 3-20 months (mean, 8.5 months). Ten patients compl icated by intrinsic muscle atrophy. Results The operation was successfully performed in 13 cases, and the operation time was 45-60 minutes. All the wounds gained primary heal ing. All patients were followed up 12-18 months (mean, 14 months). The numbness of ring finger, l ittle finger, and the ulnar side of hand were decreased obviously on the first day after operation. The examination of electromyogram showed that the ulnar nerve conduction increased at 2 weeks, the ampl itude was improved, and recruitment of the intrinsic muscles of hand enhanced. In 10 cases compl icated by intrinsic muscle atrophy, myodynamia was recovered to the normal in 7 cases and was mostly recovered in 3 cases at 3 months after operation. The symptom of cubital tunnel syndrome disappeared and gained a normal function at 12 months after operation. According to the assessment of Chinese Medical Association and Lascar et al. grading criteria, the cl inical results were excellent in 10 cases and good in 3; the excellent and good rate was 100%. Patients recovered to work 12-16 days (mean, 14 days) after operation. No recurrence occurred during followup. Conclusion The surgical method of endoscope and microscope assisted three small incisions for treatment cubital tunnel syndrome has less invasion with small incision and complete decompression. Patients can recover to work early. It is a convenient and efficient procedure for treating cubital tunnel syndrome.
ObjectiveTo investigate the value of a predictive model for sentinel lymph node (SLN) metastasis after neoadjuvant therapy (NAT) based on the radiomic features from multi-modality MRI in combination with clinicopathologic data. MethodsThe clinical data and MRI images of breast cancer patients (initially diagnosed with cN0, all underwent NAT and surgical treatment) from two hospitals (Affiliated Hospital of Southwest Medical University and Suining Central Hospital) from January 2018 to September 2024, were retrospectively collected. The radiomic features from the multi-modality images, including T2-weighted short tau inversion recovery (T2STIR), diffusion-weighted imaging (DWI), dynamic contrast-enhanced (DCE), were extracted and selected. The predictive models for SLN metastasis after NAT were constructed using four algorithms: LightGBM, XGBoost, support vector machine (SVM), and logistic regression (LR), in combination with clinicopathologic data. The models were evaluated for performance and interpretability using receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis, and Shapley additive explanation (SHAP) analysis. ResultsA total of 236 breast cancer patients were enrolled in this study. Among them, 216 patients from the Southwest Medical University were subdivided in an 8:2 ratio into the training set (173) and internal validation set (43), while 20 patients from the Suining Central Hospital served as the external validation set. Among the clinical and pathological features, lymphovascular invasion (LVI) (P<0.001), perineural invasion (PNI) (P=0.002), and Ki-67(P<0.001) were identified as the risk factors for SLN metastasis after NAT. The predictive models utilizing multi-modality MRI and clinicopathologic data yielded area under the ROC curve (AUC) values for the internal and external validation sets of 0.750 [95%CI=(0.395, 1.000)]/0.625 [95%CI=(0.321, 0.926)] for LightGBM, 0.878 [95%CI=(0.707, 1.000)]/0.778 [95%CI=(0.525, 0.986)] for XGBoost, 0.641 [95%CI=(0.488, 0.795)]/0.681 [95%CI=(0.345, 1.000)] for SVM, and 0.667 [95%CI=(0.357, 0.945)]/0.583 [95%CI=(0.196, 0.969)] for LR. XGBoost demonstrated the best predictive performance. Further SHAP analysis revealed that LVI, the minimum value of first-order features from T2STIR-MRI, and platelet count were the key features influencing the predictions of the models. ConclusionThe XGBoost prediction model based on radiomic features derived from multiparametric MRI (T2STIR, DWI, and DCE) combined with clinicopathological data was able to predict SLN metastasis after NAT in breast cancer patients.