PurposeMachine learning models were developed and validated to identify lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) using clinical factors, laboratory metrics, and 2-deoxy-2[18F]fluoro-D-glucose ([18F]F-FDG) positron emission tomography (PET)/computed tomography (CT) radiomic features.MethodsOne hundred and twenty non-small cell lung cancer (NSCLC) patients (62 LUAD and 58 LUSC) were analyzed retrospectively and randomized into a training group (n = 85) and validation group (n = 35). A total of 99 feature parameters—four clinical factors, four laboratory indicators, and 91 [18F]F-FDG PET/CT radiomic features—were used for data analysis and model construction. The Boruta algorithm was used to screen the features. The retained minimum optimal feature subset was input into ten machine learning to construct a classifier for distinguishing between LUAD and LUSC. Univariate and multivariate analyses were used to identify the independent risk factors of the NSCLC subtype and constructed the Clinical model. Finally, the area under the receiver operating characteristic curve (AUC) values, sensitivity, specificity, and accuracy (ACC) was used to validate the machine learning model with the best performance effect and Clinical model in the validation group, and the DeLong test was used to compare the model performance.ResultsBoruta algorithm selected the optimal subset consisting of 13 features, including two clinical features, two laboratory indicators, and nine PEF/CT radiomic features. The Random Forest (RF) model and Support Vector Machine (SVM) model in the training group showed the best performance. Gender (P=0.018) and smoking status (P=0.011) construct the Clinical model. In the validation group, the SVM model (AUC: 0.876, ACC: 0.800) and RF model (AUC: 0.863, ACC: 0.800) performed well, while Clinical model (AUC:0.712, ACC: 0.686) performed moderately. There was no significant difference between the RF and Clinical models, but the SVM model was significantly better than the Clinical model. ConclusionsThe proposed SVM and RF models successfully identified LUAD and LUSC. The results indicate that the proposed model is an accurate and noninvasive predictive tool that can assist clinical decision-making, especially for patients who cannot have biopsies or where a biopsy fails.
Purpose: The aim of this study was to develop and validate a nomogram model to evaluate lymph node metastasis (LNM) in patients with rectal cancer (RC).Methods: A total of 162 patients with RC between 2019 and 2021 were included in the study. Patients were allocated to a training set and a validation set at a ratio of 7:3. The lymph node (LN) status was evaluated retrospectively from magnetic resonance imaging (MRI) images by two radiologists. Based on 103 radiomic features extracted from T2 weighted images (T2WI), the least absolute shrinkage and selection operator (LASSO) was used to screen and calculate the radiomic feature score (Radscore). The model was constructed using the logistics regression algorithm. The DeLong test and decision curve analysis (DCA) were used to compare the prediction performance and clinical utility of the MRI reported model, the Radscore model, and the complex model constructed by combining the MRI reported and Radscore. The nomogram model was constructed to visualize the prediction results of the best model. Model performance was evaluated in the training and validation groups, and the calibration curve and Hosmer-Lemeshow goodness of fit test were used to evaluate the calibration.Result: This study included 162 patients with RC, including 54 patients with LNM and 108 patients without LNM. All three models constructed by the logistics regression algorithm were good at identifying LNM. The DeLong test and the DCA results showed that the complex model outperformed the MRI-based model and the Radscore model in relation to their predictive performance and clinical utility. The nomogram of the complex model had an area under the curve (AUC) of 0.902 (95% confidence interval (CI): 0.848−0.957) in the training group and an AUC of 0.891 (95% CI: 0.799−0.983) in the validation group. Meanwhile, the calibration curve and the Hosmer-Lemeshow goodness-of-fit test showed good calibration.Conclusion: The nomogram model constructed based on T2WI radiomics and MRI reported had good diagnostic efficacies for LNM in patients with RC, and provided a new auxiliary method for accurate and individualized clinical management.
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