ABSTRACT
Objective: To evaluate the value of a nomogram combined MRI Diffusion Weighted Imaging (DWI) and clinical features to predict the treatment response of Neoadjuvant Chemotherapy (NAC) in patients with osteosarcoma. Methods: A retrospective analysis was conducted on 209 osteosarcoma patients admitted into two bone cancer treatment centers (133 males, 76females; mean age 16.31 ± 11.42 years) from January 2016 to January 2022. Patients were classified as pathological good responders (pGRs) if postoperative histopathological examination revealed ≥90% tumor necrosis, and non-pGRs if <90%. Their clinical features were subjected to univariate and multivariate analysis, and features with statistically significance were utilized to construct a clinical signature using machine learning algorithms. Apparent diffusion coefficient (ADC) values pre-NAC (ADC 0) and post two chemotherapy cycles (ADC 1) were recorded. Regions of interest (ROIs) were delineated from pre-treatment DWI images (b=1000 s/mm²) for radiomic features extraction. Variance thresholding, SelectKBest, and LASSO regression were used to select features with strong relevance, and three machine learning models (Logistic Regression, RandomForest and XGBoost) were used to construct radiomics signatures for predicting treatment response. Finally, the clinical and radiomics signatures were integrated to establish a comprehensive nomogram model. Predictive performance was assessed using ROC curve analysis, with model clinical utility appraised through AUC and decision curve analysis (DCA). Results: Of the 209patients, 51 (24.4%) were pGRs, while 158 (75.6%) were non-pGRs. No significant ADC1 difference was observed between groups (P>0.05), but pGRs had a higher ADC 0 (P<0.01). ROC analysis indicated an AUC of 0.681 (95% CI: 0.482-0.862) for ADC 0 at the threshold of ≥1.37×10-3 mm²/s, achieving 74.7% sensitivity and 75.7% specificity. The clinical and radiomics models reached AUCs of 0.669 (95% CI: 0.401-0.826) and 0.768 (95% CI: 0.681-0.922) respectively in the test set. The combined nomogram displayed superior discrimination with an AUC of 0.848 (95% CI: 0.668-0.951) and 75.8% accuracy. The DCA suggested the clinical utility of the nomogram. Conclusion: The nomogram based on combined radiomics and clinical features outperformed standalone clinical or radiomics model, offering enhanced accuracy in evaluating NAC response in osteosarcoma. It held significant promise for clinical applications.
ABSTRACT
OBJECTIVE: To evaluate the potential of synovial membrane volume measurement by MRI in monitoring the effect of radiation synovectomy on patients of Hemophilic Arthropathy (HA). METHODS: We studied 63 diseased joints of 42 HA patients who received hospitalized services at the Hemophilia Diagnosis and Treatment Center of Henan Provincial People's Hospital from May 2011 to January 2015. Unenhanced and enhanced MR scanning of each diseased joint was performed simultaneously. The volumes of synovial membrane of 21 joints from 16 patients before and after being treated with 32P radiation synovectomy (PRS) were measured and compared using image post-processing software and workstation. Two sample matching t test was conducted to analyze the synovial membrane volumes of the same joint measured by unenhanced and enhanced MR, as well as change of MR enhancement ratio after treatments. RESULTS: The synovial membrane volumes measured by unenhanced versus enhanced MR scanning showed no statistical significance. Significant reduction (tâ=â7.831, pâ<â0.001) of the synovial membrane volume after treatment (2479.45±46.48âmm3 versus 2983.30±42.87âmm3 before treatment) was observed. MR enhancement ratio of synovial membrane decreased after treatment (0.92±0.06 after vs 1.17±0.07 before treatment) with statistical significance. CONCLUSION: The synovial membrane volume and MR enhancement ratio can be used to monitor patient response to PRS treatment.
