Objectives: To investigate the performance of radiomic-based quantitative analysis on CT images in predicting invasiveness of lung adenocarcinoma manifesting as pure ground-glass nodules (pGGNs). Methods: A total of 275 lung adenocarcinoma cases, with 322 pGGNs resected surgically and confirmed pathologically, from January 2015 to October 2017 were enrolled in this retrospective study. All nodules were split into training and test cohorts randomly with a ratio of 4:1 to establish models to predict between pGGN-like adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IVA). Radiomic feature extraction was performed using Pyradiomics with semi-automatically segmented tumor regions on CT scans that were contoured with an in-house plugin for 3D-Slicer. Random forest (RF) and support vector machine (SVM) were used for feature selection and predictive model building in the training cohort. Three different predictive models containing conventional, radiomic, and combined models were built on the basis of the selected clinical, radiological, and radiomic features. The predictive performance of each model was evaluated through the receiver operating characteristic curve (ROC) and the area under the curve (AUC). The predictive performance of two radiologists (A and B) and our radiomic predictive model were further investigated in the test cohort to see if radiomic predictive model could improve radiologists' performance in prediction between pGGN-like AIS/MIA and IVA. Results: Among 322 nodules, 48 (14.9%) were AIS and 102 (31.7%) were MIA with 172 (53.4%) for IVA. Age, diameter, density, and nine meaningful radiomic features were selected for model building in the training cohort. Three predictive models showed good performance in prediction between pGGN-like AIS/MIA and IVA (AUC > 0.8, P < 0.05) in both training and test cohorts. The AUC values in the test cohort were 0.824 (95% CI, 0.723–0.924), 0.833 (95% CI, 0.733–0.934), and 0.848 (95% CI, 0.750–0.946) for conventional, radiomic, and combined models, respectively. The predictive accuracy was 73.44 and 59.38% for radiologist A and radiologist B in the test cohort and was improved dramatically to 79.69 and 75.00% with the aid of our radiomic predictive model. Conclusion: The predictive models built in our study showed good predictive power with good accuracy and sensitivity, which provided a non-invasive, convenient, economic, and repeatable way for the prediction between IVA and AIS/MIA representing as pGGNs. The radiomic predictive model outperformed two radiologists in predicting pGGN-like AIS/MIA and IVA, and could significantly improve the predictive performance of the two radiologists, especially radiologist B with less experience in medical imaging diagnosis. The selected radiomic features in our research did not provide more useful information to improve the combined predictive mode...
BACKGROUNDMost melanomas identified in the stomach are metastatic; primary gastric melanoma (PGM) is extremely rare, and the relevant studies are relatively scarce. PGM may be incorrectly diagnosed as other gastric malignant tumor types.CASE SUMMARYWe describe a rare case of PGM confirmed through long-term clinical observation and pathological diagnosis. A 67-year-old woman presented to our hospital with recurrent chest tightness and chest pain. Digital gastrointestinal radiography revealed a circular shadow in the gastric cardia. Computed tomography (CT) revealed a heterogeneous tumor with uneven enhancement. Enlarged lymph nodes were noted in the lesser curvature of the stomach. On magnetic resonance imaging (MRI), T1- and T2-weighted imaging revealed hyperintensity in and hypointensity in the tumor, respectively, both of which increased substantially after uneven enhancement. Near total gastrectomy was performed, and the tumor was pathologically confirmed to be a gastric melanoma. Because no other possible primary site of malignant melanoma was suspected, a clinical diagnosis of PGM was made. The patient was followed for nearly 5 years, during which she received CT reexamination, but no recurrence or metastasis was observed.CONCLUSIONCertain imaging characteristics could be revealed in PGM. Imaging examination can be of great value in preoperative diagnosis, differential diagnosis, and follow-up of patients with PGM.
Background: To test the ability of a multiclassifier model based on radiomics features to predict benign and malignant primary pulmonary solid nodules.Methods: Computed tomography (CT) images of 342 patients with primary pulmonary solid nodules confirmed by histopathology or follow-up were retrospectively analyzed. The region of interest (ROI) of the images was delineated, and the radiomics features of the lesions were extracted. The feature weight was calculated using the relief feature selection algorithm. Based on the selected features, five classifier models were constructed: support vector machine (SVM), random forest (RF), logistic regression (LR), extreme learning machine (ELM), and K-nearest neighbor (KNN). The precision, recall rate, and area under the receiver operating characteristic curve (AUC) were used to evaluate the prediction performance of each classifier. The prediction result of each classifier was first weighted, and then all the prediction results were fused to predict the nodule type of unknown images. The prediction precision, recall rate, and AUC of the fusion classifier and single classifier were compared. Cross-validation was used to evaluate the generalization of the fusion classifier, and t-and F-tests were performed on the five classifiers and fusion classifier.Results: For each ROI, 450 features in four major categories were extracted and were analyzed using the relief feature selection algorithm. According to the weights, 25 highly repetitive and nonredundant stable features that played a major role in pulmonary nodule classification were selected. The fusion classifier's prediction performance (prediction precision =92.0%, AUC =0.915) was superior to those of SVM (prediction precision =75.3%, AUC =0.740), RF (prediction precision =89.1%, AUC =0.855), LR (prediction precision =68.4%, AUC =0.681), ELM (prediction precision =87.0%, AUC =0.830), and KNN (prediction precision =77.1%, AUC =0.702). The fusion classifier showed the best null hypothesis performance in the t-test (P=0.035) and F-test (P=0.036). Conclusions:The multiclassifier fusion model based on radiomics features had high prediction value for benign and malignant primary pulmonary solid nodules.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.