Xinping Shen

Background: A timely assessment of local recurrence (LoR) risk in extremity high-grade osteosarcoma is crucial for optimizing treatment strategies and improving patient outcomes. Purpose: To explore the potential of machine-learning algorithms in predicting LoR in patients with osteosarcoma. Material and Methods: Data from patients with high-grade osteosarcoma who underwent preoperative radiograph and multiparametric magnetic resonance imaging (MRI) were collected. Machine-learning models were developed and trained on this dataset to predict LoR. The study involved selecting relevant features, training the models, and evaluating their performance using the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC). DeLong's test was utilized for comparing the AUCs. Results: The performance (AUC, sensitivity, specificity, and accuracy) of four classifiers (random forest [RF], support vector machine, logistic regression, and extreme gradient boosting) using radiograph-MRI as image inputs were stable (all Hosmer-Lemeshow index >0.05) with the fair to good prognosis efficacy. The RF classifier using radiograph-MRI features as training inputs exhibited better performance (AUC = 0.806, 0.868) than that using MRI only (AUC = 0.774, 0.771) and radiograph only (AUC = 0.613 and 0.627) in the training and testing sets ( P <0.05) while the other three classifiers showed no difference between MRI-only and radiograph-MRI models. Conclusion: This study provides valuable insights into the use of machine learning for predicting LoR in osteosarcoma patients. These findings emphasize the potential of integrating radiomics data with algorithms to improve prognostic assessments.

PURPOSE: This study aimed to identify patients with local relapse (≤ 2 years) in osteosarcoma after surgical resection and make better clinical decisions by constructing a preoperative predictive model based on radiograph and multiparametric magnetic resonance imaging (MRI). MATERIALS AND METHODS: A retrospective study of 92 consecutive patients (training set, n = 61; testing set, n = 31) with extremity high-grade osteosarcoma were enrolled. The imaging features for each patient were extracted from radiograph, multiparametric MRI (T1WI, T2WI and T1WI-CE). In order to select features, three steps including minimal-redundancy-maximum-relevance (mRMR), least absolute shrinkage and selection operator (LASSO) regression and the random forest recursive feature elimination (RF-RFE) were performed. The classification performance was evaluated with four classifiers: extreme gradient boosting (XGB), logistic regression (LR), support vector machine (SVM) and random forest (RF). The receiver-operating characteristic curve (ROC) and the area under the curve (AUC) were used to evaluate the performance of the classifiers. DeLong’s test was utilized for comparing the AUCs. RESULTS: The performance (AUC, sensitivity, specificity, and accuracy) of four classifiers (RF, SVM, LR and XGB) using radiograph-MRI as image inputs were stable (all Hosmer–Lemeshow index > 0.05) with the fair to good prognosis efficacy. The RF classifier using radiograph-MRI features as training inputs exhibited better performance (AUC = 0.806, 0.868) than that using MRI-only (AUC = 0.774, 0.771) and radiograph-only (AUC = 0.613 and 0.627) in the training and testing sets (p < 0.05) while the other three classifiers showed no difference between MRI only and radiograph-MRI models. CONCLUSION: The tumoral radiograph and multiparametric MRI radiomics model can promisingly predict local relapse in extremity high-grade osteosarcoma. Our results highlighted the potential value of the tumoral radiomic model in osteosarcoma management.

This study aimed to identify radiomic features of primary tumor and develop a model for indicating extrahepatic metastasis of hepatocellular carcinoma (HCC). Contrast-enhanced computed tomographic (CT) images of 177 HCC cases, including 26 metastatic (MET) and 151 non-metastatic (non-MET), were retrospectively collected and analyzed. For each case, 851 radiomic features, which quantify shape, intensity, texture, and heterogeneity within the segmented volume of the largest HCC tumor in arterial phase, were extracted using Pyradiomics. The dataset was randomly split into training and test sets. Synthetic Minority Oversampling Technique (SMOTE) was performed to augment the training set to 145 MET and 145 non-MET cases. The test set consists of six MET and six non-MET cases. The external validation set is comprised of 20 MET and 25 non-MET cases collected from an independent clinical unit. Logistic regression and support vector machine (SVM) models were identified based on the features selected using the stepwise forward method while the deep convolution neural network, visual geometry group 16 (VGG16), was trained using CT images directly. Grey-level size zone matrix (GLSZM) features constitute four of eight selected predictors of metastasis due to their perceptiveness to the tumor heterogeneity. The radiomic logistic regression model yielded an area under receiver operating characteristic curve (AUROC) of 0.944 on the test set and an AUROC of 0.744 on the external validation set. Logistic regression revealed no significant difference with SVM in the performance and outperformed VGG16 significantly. As extrahepatic metastasis workups, such as chest CT and bone scintigraphy, are standard but exhaustive, radiomic model facilitates a cost-effective method for stratifying HCC patients into eligibility groups of these workups.

Role of computed tomography features in the differential diagnosis of chromophobe renal cell carcinoma from oncocytoma and angiomyolipoma without visible fat Cuiping Zhou, Xiaohua Ban, Jianxun Lv, Lin Cheng, Jianmin Xu, Xinping Shen

Clinical and imaging features of 112 patients with irregular and flat bone osteosarcoma Zhendong Luo, Jing Li, Genggeng Qin, Hui Zeng, Zilong He, Derun Pan, Yulin Li, Weiguo Chen, Xinping Shen

Purpose: To establish and verify a predictive model involving multiparameter MRI and clinical manifestations for predicting synchronous lung metastases (SLM) in osteosarcoma. Materials and Methods: Seventy-eight consecutive patients with osteosarcoma (training dataset, n = 54; validation dataset, n = 24) were enrolled in our study. MRI features were extracted from the T1‐weighted image (T1WI), T2‐weighted image (T2WI), and contrast-enhanced T1-weighted image (CE-T1WI) of each patient. Least absolute shrinkage and selection operator (LASSO) regression and multifactor logistic regression were performed to select key features and build radiomics models in conjunction with logistic regression (LR) and support vector machine (SVM) classifiers. Eight individual models based on T1WI, T2WI, CE-T1WI, T1WI+T2WI, T1WI+CE-T1WI, T2WI+CE-T1WI, T1WI+T2WI+CE-T1WI, and clinical features, as well as two combined models, were built. The area under the receiver operating characteristic curve (AUC), sensitivity and specificity were employed to assess the different models. Results: Tumor size was the most significant univariate clinical indicator (1). The AUC values of the LR predictive model based on T1WI, T2WI, CE-T1WI, T1WI+T2WI, T1WI+CE-T1WI, T2WI+CE-T1WI, and T1WI+T2WI+CE-T1WI were 0.686, 0.85, 0.87, 0.879, 0.736, 0.85, and 0.914, respectively (2). The AUC values of the SVM predictive model based on T1WI, T2WI, CE-T1WI, T1WI+T2WI, T1WI +CE-T1WI, T2WI +CE-T1WI, and T1WI+T2WI+CE-T1WI were 0.629, 0.829, 0.771, 0.879, 0.643, 0.829, and 0.929, respectively (3). The AUC values of the clinical, combined 1 (clinical and LR-radiomics) and combined 2 (clinical and SVM-radiomics) predictive models were 0.779, 0.957, and 0.943, respectively. Conclusion: The combined model exhibited good performance in predicting osteosarcoma SLM and may be helpful in clinical decision-making.

Objective To determine the predictive computed tomography (CT) and clinical features for diagnosis of COVID-19 pneumonia. Methods The CT and clinical data including were analyzed using univariate analysis and multinomial logistic regression, followed by receiver operating characteristic curve analysis. Results The factors including size of ground grass opacity (GGO), GGO with reticular and/or interlobular septal thickening, vascular enlargement, “tree-in-bud” opacity, centrilobular nodules, and stuffy or runny nose were associated with the 2 groups of viral pneumonia, as determined by univariate analysis (P < 0.05). Only GGO with reticular and/or interlobular septal thickening, centrilobular nodules, and stuffy or runny nose remained independent risk factors in multinomial logistic regression analysis. Receiver operating characteristic curve analysis showed that the area under curve of the obtained logistic regression model was 0.893. Conclusion Computed tomography and clinical features including GGO with reticular and/or interlobular septal thickening, absence of centrilobular nodules, and absence of stuffy or runny nose are potential patients with COVID-19 pneumonia.