Objectives: To compare the diagnostic performance of a newly developed artificial intelligence (AI) algorithm derived from the fusion of convolution neural networks (CNN) versus human observers in the estimation of malignancy risk in pulmonary nodules. Methods: The study population consists of 158 nodules from 158 patients. All nodules (81 benign and 77 malignant) were determined to be malignant or benign by a radiologist based on pathologic assessment and/or follow-up imaging. Two radiologists and an AI platform analyzed the nodules based on the Lung-RADS classification. The two observers also noted the size, location, and morphologic features of the nodules. An intraclass correlation coefficient was calculated for both observers and the AI; ROC curve analysis was performed to determine diagnostic performances. Results: Nodule size, presence of spiculation, and presence of fat were significantly different between the malignant and benign nodules (p < 0.001, for all three). Eighteen (11.3%) nodules were not detected and analyzed by the AI. Observer 1, observer 2, and the AI had an AUC of 0.917 ± 0.023, 0.870 ± 0.033, and 0.790 ± 0.037 in the ROC analysis of malignity probability, respectively. The observers were in almost perfect agreement for localization, nodule size, and lung-RADS classification [κ (95% CI)=0.984 (0.961–1.000), 0.978 (0.970–0.984), and 0.924 (0.878–0.970), respectively]. Conclusion: The performance of the fusion AI algorithm in estimating the risk of malignancy was slightly lower than the performance of the observers. Fusion AI algorithms might be applied in an assisting role, especially for inexperienced radiologists. Advances in knowledge: In this study, we proposed a fusion model using four state-of-art object detectors for lung nodule detection and discrimination. The use of fusion of deep learning neural networks might be used in a supportive role for radiologists when interpreting lung nodule discrimination.

Background: Detecting molecular drivers is crucial in the management of non-small-cell lung cancer (NSCLC). This study aimed to evaluate the use of pretreatment chest computed tomography (CT) radiomics features for predicting epidermal growth factor receptor (EGFR) mutation status in NSCLC. Materials and Methods: CT images were used to develop a radiomics-based model for predicting EGFR mutation status. Two different groups were formed from the dataset, namely groups for training (n = 380) and testing (n = 86). Feature selection was performed using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm on a radiomics platform. Machine learning methods were then applied to construct the radiomics models. Receiver operating characteristic curve analysis was conducted to assess the performance of the radiomics signature across different datasets and methods. Results: The frequency of EGFR mutation was 13.5% (58/430). A total of 1409 quantitative imaging features were extracted from CT images using the Radcloud platform. Among the six radiomics-based classifiers (k-Nearest Neighbor, Support Vector Machine (SVM), eXtreme Gradient Boosting, Random Forest, Logistic Regression, and Decision Tree), SVM demonstrated the highest area under the curve values in both the testing and training groups, reaching 0.87 and 0.98, respectively. Our model, which incorporated both clinical and radiomics data, successfully predicted EGFR mutation status with an accuracy rate of 86.9%. Conclusion: Our findings highlight the potential of radiomics features as a non-invasive predictive imaging biomarker for EGFR mutation status, which could enhance personalized treatment in NSCLC. Radiomics emerges as a valuable tool for identifying driver mutations, although further studies are necessary to validate its clinical utility in NSCLC.

Objectives: The aim of this study is to investigate and delineate the reliability and efficacy of the new Pipeline Vantage (PV). The flow diverter device was recently introduced presenting the innovative shield technology. Methods: This retrospective study included 21 patients with 22 unruptured intracranial aneurysms, all of whom were treated with the PV device at our institution between January 2022 and January 2023. Patient demographics, aneurysm morphology and location, periprocedural strategies, treatment approaches, and occlusion status at follow-up (up to one year) were retrospectively analyzed. Results: Nineteen of the aneurysms (86.36%) were treated solely with the PV device and three (13.63%) were treated in combination with coil embolization. Two patients (9.52%) were expired, one related to a subarachnoid hemorrhage occurred during treatment and the second one after a successful treatment on follow-up, due to the existence of concomitant disease, not related to the aneurysm itself. The mean time of follow-up on the treated aneurysms was 7.4 months. In 81.81% (18 aneurysms) of the cases presented, a complete aneurysm obliteration was achieved on the last follow-up imaging. Conclusions: In this initial experience, the PV flow diverter demonstrated favorable short- and mid-term outcomes in the treatment of intracranial aneurysms. While the overall complication profile was acceptable, the occurrence of a periprocedural mortality reflects the inherent procedural risks of neurointervention, even under meticulous technique. However, short-term results should be balanced with caution and supported by robust research to demonstrate the long-term safety and efficacy of the device. Advances in knowledge: PV the successor of the novel Pipeline device showed promising short-term results.

Short-Term Renal Function Outcomes after Renal Artery Stenting in Atherosclerotic Renal Artery Stenosis - Journal of Ankara University Faculty of Medicine