ABSTRACT. The cause of postoperative failure after the treatment of femoral intertrochanteric fracture with proximal femoral nail antirotation (PFNA) was analyzed, and the reoperative methods were examined. Nine failures of 308 femoral intertrochanteric fracture patients with PFNA were treated with femoral prosthesis total hip replacement (THR) and reoperative internal fixation. All nine patients were analyzed to determine the cause of failure. The causes of failed internal fixation in the intertrochanteric-fractured patients included perforation of the helical blade into the hip joint in three cases, cuttingout of the helical blade exit outside in two cases, and hip varus as a result of cutting-out the helical blade in two cases. Seven patients with failed internal fixation were treated with THR. Two patients who had femoral shaft fractures at the end of the nail were treated with longer PFNA. Faulty operative procedures, unsatisfactory reductions, serious osteoporosis, and incorrect positioning of the helical blade were the most important factors responsible for the failed internal fixation. Satisfactory results were achieved with THR and refixation relative to the causes of the failed internal fixation.
PurposeTo develop an appropriate machine learning model for predicting anaplastic lymphoma kinase (ALK) rearrangement status in non-small cell lung cancer (NSCLC) patients using computed tomography (CT) images and clinical features.Method and materialsThis study included 193 patients with NSCLC (154 in the training cohort, 39 in the validation cohort), 68 of whom tested positive for ALK rearrangements and 125 of whom tested negative. From the nonenhanced CT scans, 157 radiomic characteristics were extracted, and 8 clinical features were collected. Five machine learning (ML) models were assessed to find the best classification model for predicting ALK rearrangement status. A radiomic signature was developed using the least absolute shrinkage and selection operator (LASSO) algorithm. The predictive performance of the models based on radiomic features, clinical features, and their combination was assessed by receiver operating characteristic (ROC) curves.ResultsThe support vector machine (SVM) model had the highest AUC of 0.914 for classification. The clinical features model had an AUC=0.805 (95% CI 0.731–0.877) and an AUC=0.735 (95% CI 0.566–0.863) in the training and validation cohorts, respectively. The CT image-based ML model had an AUC=0.953 (95% CI 0.913–1.0) in the training cohort and an AUC=0.890 (95% CI 0.778–0.971) in the validation cohort. For predicting ALK rearrangement status, the ML model based on CT images and clinical features performed better than the model based on only clinical information or CT images, with an AUC of 0.965 (95% CI 0.826–0.882) in the primary cohort and an AUC of 0.914 (95% CI 0.804–0.893) in the validation cohort.ConclusionOur findings revealed that ALK rearrangement status could be accurately predicted using an ML-based classification model based on CT images and clinical data.
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