Objectives To develop and validate machine learning models to distinguish between benign and malignant bone lesions and compare the performance to radiologists. Methods In 880 patients (age 33.1 ± 19.4 years, 395 women) diagnosed with malignant (n = 213, 24.2%) or benign (n = 667, 75.8%) primary bone tumors, preoperative radiographs were obtained, and the diagnosis was established using histopathology. Data was split 70%/15%/15% for training, validation, and internal testing. Additionally, 96 patients from another institution were obtained for external testing. Machine learning models were developed and validated using radiomic features and demographic information. The performance of each model was evaluated on the test sets for accuracy, area under the curve (AUC) from receiver operating characteristics, sensitivity, and specificity. For comparison, the external test set was evaluated by two radiology residents and two radiologists who specialized in musculoskeletal tumor imaging. Results The best machine learning model was based on an artificial neural network (ANN) combining both radiomic and demographic information achieving 80% and 75% accuracy at 75% and 90% sensitivity with 0.79 and 0.90 AUC on the internal and external test set, respectively. In comparison, the radiology residents achieved 71% and 65% accuracy at 61% and 35% sensitivity while the radiologists specialized in musculoskeletal tumor imaging achieved an 84% and 83% accuracy at 90% and 81% sensitivity, respectively. Conclusions An ANN combining radiomic features and demographic information showed the best performance in distinguishing between benign and malignant bone lesions. The model showed lower accuracy compared to specialized radiologists, while accuracy was higher or similar compared to residents. Key Points • The developed machine learning model could differentiate benign from malignant bone tumors using radiography with an AUC of 0.90 on the external test set. • Machine learning models that used radiomic features or demographic information alone performed worse than those that used both radiomic features and demographic information as input, highlighting the importance of building comprehensive machine learning models. • An artificial neural network that combined both radiomic and demographic information achieved the best performance and its performance was compared to radiology readers on an external test set.
Objective To qualitatively and quantitatively evaluate the 2-year magnetic resonance imaging (MRI) outcome after MPFL reconstruction at the knee and to assess MRI-based risk factors that predispose for inferior clinical and imaging outcomes. Materials and methods A total of 31 patients with MPFL reconstruction were included (22 ± 6 years, 10 female). MRI was performed preoperatively in 21/31 patients. Two-year follow-up MRI included quantitative cartilage T2 and T1rho relaxation time measurements at the ipsilateral and contralateral knee. T2relative was calculated as T2patellofemoral/T2femorotibial. Morphological evaluation was conducted via WORMS scores. Patellar instability parameters and clinical scores were obtained. Statistical analyses included descriptive statistics, t-tests, multivariate regression models, and correlation analyses. Results Two years after MPFL reconstruction, all patellae were clinically stable. Mean total WORMS scores improved significantly from baseline to follow-up (mean difference ± SEM, − 4.0 ± 1.3; P = 0.005). As compared to patients with no worsening of WORMS subscores over time (n = 5), patients with worsening of any WORMS subscore (n = 16) had lower trochlear depth, lower facetal ratio, higher tibial-tuberosity to trochlear groove (TTTG) distance, and higher postoperative lateral patellar tilt (P < 0.05). T2relative was higher at the ipsilateral knee (P = 0.010). T2relative was associated with preoperatively higher patellar tilt (P = 0.021) and higher TTTG distance (P = 0.034). TTTG distance, global T2 values, and WORMS progression correlated with clinical outcomes (P < 0.05). Conclusion MPFL reconstruction is an optimal treatment strategy to restore patellar stability. Still, progressive knee joint degeneration and patellofemoral cartilage matrix degeneration may be observed, with patellar instability MRI parameters representing particular risk factors.
Objective To evaluate whether a sandwich technique procedure for large osteochondral lesions (OCL) of the medial femur condyle reduces clinical symptoms and improves activity level as well as to assess repair tissue integration on MRI over 2 years. Design Twenty-one patients (median age: 29 years, 18-44 years) who received matrix-associated autologous chondrocyte transplantation (MACT) combined with cancellous bone grafting at the medial femur condyle in a 1-step procedure were prospectively included. Patients were evaluated before surgery (baseline) as well as 3, 6, 12, and 24 months postoperatively, including clinical evaluation, Lysholm score, Tegner Activity Rating Scale, and MRI with Magnetic Resonance Observation of Cartilage Repair Tissue (MOCART) score and a modified Whole-Organ Magnetic Resonance Imaging Score (WORMS). Results Seventeen patients were available for the 24-month (final) follow-up (4 dropouts). Lysholm significantly improved from 48 preoperatively stepwise to 95 at final follow-up ( P < 0.05). Tegner improvement from 2.5 at baseline to 4.0 at final follow-up was not significant ( P = 1.0). MOCART score improved significantly and stepwise from 65 at 3 months to 90 at 24 months ( P < 0.05). Total WORMS improved from 14.5 at surgery to 7.0 after 24 months ( P < 0.05). Body mass index and defect size at surgery correlated with total WORMS at final follow-up ( P < 0.05) but did not correlate with clinical scores or defect filling. Conclusion MACT combined with cancellous bone grafting at the medial femoral condyle reduces symptoms continuously over 2 years. A 1-step procedure may reduce perioperative morbidity. However, despite improvements, patients’ activity levels remain low, even 2 years after surgery.
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