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.

Update: The New 9th Edition TNM Classification for Lung Cancer Is Now in Use - Thoracic Research and Practice

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.

One of the patient groups adversely affected during the COVID19 pandemic is those suffering with cancer. The aim of this study was to evaluate the clinical characteristics and outcomes of lung cancer (LC) patients with COVID-19. Three thousand seven-hundred and fifty hospitalized patients with a presumptive diagnosis of COVID-19 in a tertiary referral hospital between March 2020-February 2021 were retrospectively evaluated. Among them, 36 hospitalized COVID-19 patients with a history of primary LC were included in the study. Univariate and multivariate analyses were carried out to assess the risk factors associated with severe disease. Of the 36 patients included in the study, 28 (77%) were males and 8 (23%) were females. Median age was 67 years (min-max: 53-81 years). Six patients (17%) had a diagnosis of small cell LC, whereas 30 patients (83%) had a diagnosis of non-small cell LC. The most common symptoms were fever (n= 28, 77%), coughing and myalgia (n= 21, 58%) and dyspnea (n= 18, 50%). The most common radiological finding was ground glass opacity (GGO) (n= 30), of which 13 was bilateral and 17 was unilateral in distribution. Nearly 30% (n= 11) of LC patients with COVID-19 developed severe disease, 5% (n= 2) of the 36 patients were admitted to intensive care unit and all of these patients eventually expired. LC patients with COVID-19 and patchy consolidation on computed tomography of thorax (Th CT) on admission had a higher risk of developing severe disease in univariate (HR 2.41, 95%CI: 1.4- 4.4, p= 0.04) and multivariate Cox regression analysis (HR 0.48, 95%CI: 0.24-0.97, p= 0.03). Clinical characteristics, laboratory and radiographic findings were similar in LC patients with COVID-19 when compared with the general population, LC patients have a higher mortality rate than the general population, with a 5% mortality rate in our series. Our findings suggest that LC may be a risk factor associated with the prognosis of COVID-19 patients.