To evaluate the potential of computed tomography (CT) radiomics, based on high-resolution large matrix target reconstruction images, in predicting the invasiveness of lung adenocarcinoma in pure ground-glass nodules (pGGNs) with a diameter ≤ 1.5 cm. The clinical and imaging data of 297 patients with pGGNs, confirmed by pathology, were collected between March 2021 and June 2024. Pathological diagnoses included atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC). The patients were divided into non-invasive (AAH and AIS) and invasive (MIA and IAC) groups based on pathology. Radiomics features were extracted using ITK-SNAP software, and a predictive model was built using Python 3.9.7, with feature selection based on least absolute shrinkage and selection operator regression. Receiver operating characteristic analysis, area under the curve (AUC), sensitivity, specificity and clinical decision curve analysis were used to assess model performance. Multivariate logistic regression revealed that the maximum lesion diameter, median CT value and solid component ratio were significant predictors of invasiveness (P < 0.05). The CT radiomics model achieved AUC values of 0.861 (95

This study aims to develop a non-invasive model for preoperatively predicting the pathological risk classification of thymic anterior mediastinal cysts and thymic epithelial tumors using CT-based radiomics and deep learning. Accurate risk stratification before surgery can support personalized treatment planning and improve clinical outcomes. A retrospective analysis was conducted on 144 patients with pathologically confirmed thymic anterior mediastinal cysts or thymic epithelial tumors who underwent preoperative thin-slice chest CT between January 2014 and December 2023. Regions of interest were manually segmented, and 1834 handcrafted radiomics features—including geometric, intensity, and texture features—were extracted using Pyradiomics. Deep learning features were derived from a ResNet50 network with transfer learning and cosine annealing learning rate adjustment. Radiomics and deep features were fused into a deep learning radiomics (DLR) feature set. Feature selection was performed before model training. The models were evaluated in training (n = 101) and test (n = 43) cohorts. The radiomics model achieved an AUC of 0.876 in the training cohort and 0.800 in the test cohort. The deep learning model yielded AUCs of 0.838 and 0.831, respectively. The combined DLR model showed superior performance, with an AUC of 0.964 in the training cohort and 0.820 in the test cohort, outperforming unimodal models in classification accuracy and robustness. In this study, a model for predicting pathological risk classification of thymic anterior mediastinal cysts and thymic epithelial tumors was developed by combining radiomics and deep learning, and its superior prediction was confirmed in verification. The results show that the model is capable of preoperatively assessing the pathological risk classification of patients, which provides strong support for the need of individual treatment strategies.

Abstract Objective To develop a multimodal predictive model, Radiomics Integrated TLSs System (RAITS), based on preoperative CT radiomic features for the identification of TLSs in stage I lung adenocarcinoma patients and to evaluate its potential in prognosis stratification and guiding personalized treatment. Methods The most recent preoperative chest CT thin-slice scans and postoperative hematoxylin and eosin-stained pathology sections of patients diagnosed with stage I LUAD were retrospectively collected. Tumor segmentation was achieved using an automatic virtual adversarial training segmentation algorithm based on a three-dimensional U-shape convolutional neural network (3D U-Net). Radiomic features were extracted from the tumor and peritumoral areas, with extensions of 2 mm, 4 mm, 6 mm, and 8 mm, respectively, and deep learning image features were extracted through a convolutional neural network. Subsequently, the RAITS was constructed. The performance of RAITS was then evaluated in both the train and validation cohorts. Results RAITS demonstrated superior AUC, sensitivity, and specificity in both the training and external validation cohorts, outperforming traditional unimodal models. In the validation cohort, RAITS achieved an AUC of 0.78 (95% CI, 0.69–0.88) and showed higher net benefits across most threshold ranges. RAITS exhibited strong discriminative ability in risk stratification, with p < 0.01 in the training cohort and p = 0.02 in the validation cohort, consistent with the actual predictive performance of TLSs, where TLS-positive patients had significantly higher recurrence-free survival (RFS) compared to TLS-negative patients (p = 0.04 in the training cohort, p = 0.02 in the validation cohort). Conclusion As a multimodal predictive model based on preoperative CT radiomic features, RAITS demonstrated excellent performance in identifying TLSs in stage I LUAD and holds potential value in clinical decision-making.

Background:The reproducibility of radiomic features is essential to lung cancer detection. This study aimed to investigate the reproducibility of radiomic features of pulmonary nodules between low-dose computed tomography (LDCT) and conventional-dose computed tomography (CDCT).Methods:A total of 105 patients with 119 pulmonary nodules [39 ground-glass nodules (GGNs) and 80 solid nodules] who underwent LDCT and CDCT were retrospectively studied between September 2019 and November 2020. Pulmonary nodules were manually segmented and 1,125 radiomic features (shape, first-order intensity, texture, wavelet, and Laplacian of the Gaussian features) were extracted from both LDCT and CDCT images. The concordance correlation coefficient (CCC) was used to evaluate the reproducibility of these radiomic features.Results:Of the 1,125 radiomic features considered, 35.5% (399 of 1,125) and 41.5% (467 of 1,125) were reproducible (CCC ≥0.85) for GGNs and solid nodules, respectively. The intensity, texture, and wavelet features of solid nodules were more reproducible than those of GGNs. The mean CCC values for intensity and texture features of solid nodules were of 0.85 and above, whereas the mean values for those of GGNs were of less than 0.85. After Gaussian kernel (σ =2) preprocessing, the CCC of intensity and texture features of GGNs improved from 0.77 to 0.90, and 84.9% (79 of 93) of the radiomic features were reproducible (mean CCC increase from 0.84±0.13 to 0.92±0.08 for intensity features, and from 0.75±0.15 to 0.89±0.11 for texture features). Wavelet features had the lowest CCCs for both GGNs and solid nodules.Conclusions:The majority of the radiomic feature classes of solid pulmonary nodules have a high level of reproducibility between LDCT and CDCT. However, LDCT should not be used as an alternative to CDCT in the radiomic study of GGNs.

Introduction To evaluate the value of artificial intelligence (AI)-assisted software in the diagnosis of lung nodules using a combination of low-dose computed tomography (LDCT) and high-resolution computed tomography (HRCT). Method A total of 113 patients with pulmonary nodules were screened using LDCT. For nodules with the largest diameters, an HRCT local-target scanning program (combined scanning scheme) and a conventional-dose CT scanning scheme were also performed. Lung nodules were subjectively assessed for image signs and compared by size and malignancy rate measured by AI-assisted software. The nodules were divided into improved visibility and identical visibility groups based on differences in the number of signs identified through the two schemes. Results The nodule volume and malignancy probability for subsolid nodules significantly differed between the improved and identical visibility groups. For the combined scanning protocol, we observed significant between-group differences in subsolid nodule malignancy rates. Conclusion Under the operation and decision of AI, the combined scanning scheme may be beneficial for screening high-risk populations.

This study is to investigate the predictive value of insufficient contrast medium filling (ICMF) in patients with acute pulmonary embolism (PE).A total of 108 PE patients were enrolled and divided into group A and group B according to the presence of ICMF. PE index and ventricul araxial lengths were measured. Heart cavity volumes were examined and right ventricle (RV) to left ventricle (LV) diameter ratio (RV/LV(d)) and volume ratio (RV/LV(V)) and right atrium (RA) to left atrium (LA) volume ratio (RA/LA(V)) were calculated and compared. Group A was further divided into A1 and A2 based upon the pulmonary vein filling degree and each index was compared.There were no significant differences between group A and B in general condition. PE index of group A was higher than that of group B. LA and LV in group A were smaller than that of group B, whereas RA in group A was larger than that of group B. RV/LV(d), RV/LV(V), and RA/LA(V) in group A were significantly larger than that of group B. Embolism index of group A2 was higher than that of groupA1, but without statistical significant difference. LA in group A2 was smaller than that of group A1, whereas RA, RV/LV(d), and RV/LV(V) were larger than that of group A1, all with significant differences.PE increased with serious ICMF in pulmonary veins could be used as an indicator for risk stratification in patients with acute PE.