To investigate the predictive value of combined radiomic features derived from chest CT scans with clinical characteristics for epidermal growth factor receptor (EGFR) gene mutations in non-small cell lung cancer (NSCLC).

A multi-center case-control study was conducted on the clinical data and CT images of 1070 NSCLC patients from the radiology departments of the 3 medical institutions between January 2013 and October 2023. The 719 NSCLC patients from the First Affiliated Hospital of Army Medical University were randomly divided into a training set and an internal validation set in a ratio of 7∶3; The 173 patients in the Eastern Theatre General Hospital and the 178 patients in Army Medical Centre of PLA were assigned into the external validation set 1 and 2, respectively. Least absolute shrinkage and selection operator (LASSO) regression was employed to identify the optimal radiomic features, which were subsequently used to construct a radiomics model. Univariate and multivariate logistic regression analyses were applied to identify clinical features associated with EGFR mutation, thereby developing a clinical model. The radiomic and clinical features were subsequently combined to develop a comprehensive model. All the 3 classification models were built using random forest (RF) machine learning. The area under curve (AUC), accuracy, sensitivity and specificity were utilized to evaluate the predictive performance of the models. Calibration curve was plotted to assess the goodness of fit of the comprehensive model, while decision curve analysis was performed to assess the clinical utility of the model.

The AUC value of the radiomics model was 0.7624 (95%CI: 0.6924~0.8251), 0.7454 (95%CI: 0.6711~0.8143), and 0.7247 (95%CI: 0.6397~0.8016), respectively, in the internal validation set, external validation set 1, and external validation set 2; The AUC value of the clinical prediction model was 0.6917 (95%CI: 0.6279~0.7576), 0.6525 (95%CI: 0.5767~0.7291), and 0.7792 (95%CI: 0.7125~0.8473), respectively in the above sets in turn; The comprehensive model constructed based on clinical features and radiomic features showed the best predictive efficacy, with an AUC value of 0.8180 (95%CI: 0.7577~0.8743), 0.7824 (95%CI: 0.7031~ 0.8482), and 0.7966 (95%CI: 0.7181~0.8686), respectively in the above sets. Calibration curve analysis indicated that the comprehensive model had a good fit, while decision curve analysis revealed that the model provided a favorable net benefit.

Our comprehensive model constructed based on chest CT radiomic features and clinical characteristics shows superior predictive performance for EGFR gene mutations in NSCLC across multiple center datasets, which may be helpful for clinical decision-making for treatment strategies.

To investigate the change in choroid plexus (CP) volume in Parkinson's disease (PD) patients with different cognitive states and its correlation with structural volumes of other brain regions.

A cross-sectional study was conducted on 48 PD patients admitted in Department of Neurosurgery of the First Affiliated Hospital of Army Medical University between May 2023 and April 2024, and on 35 healthy controls (HC) recruited through a physical exam center. According to the results of Mini-Mental State Examination (MMSE), the patients were divided into PD with cognitive impairment (PD-CI) group (n=27) and PD with normal cognitive function (PD-NC) group (n=21). 3. 0T magnetic resonance imaging (MRI) was performed using MPRAGE sequences, and CP volume and volumes of other brain regions were obtained using FreeSurfer 6. 0 software. The CP volume was adjusted by calculating the ratio of its volume to estimated total intracranial volume (eTIV). After controlling for confounders, partial correlation analysis was used to assess the relationship between the CPV/eTIV ratio and the volumes of other brain regions as well as cognitive scale scores. Additionally, multiple linear regression analysis was performed to further explore the relationship between CPV and cognitive function in the PD-CI group.

Compared to the HC group, the CPV in the PD-CI group was significantly larger (P=0. 029). In the PD-CI group, the CPV/eTIV ratio showed significant positive correlations with the volume of the lateral ventricles (r=0. 689, P=0. 001), the volume of the third ventricle (r=0. 592, P=0. 006), the volume of cerebrospinal fluid (CSF) (r=0. 508, P=0. 022), and white matter hyperintensities (WMH) (r=0. 486, P=0. 030), but was negatively correlated with the volume of the caudate nucleus (r=-0. 530, P=0. 016), the volume of the thalamus (r=-0. 477, P=0. 033), and the MMSE scores (r=-0. 483, P=0. 031). But in the PD-NC group, the CPV/eTIV ratio was only positively correlated with CSF volume (r=0. 571, P=0. 021). Multiple linear regression analysis indicated that the CPV/eTIV ratio and MMSE scores remained significantly negatively correlated in the PD-CI group (β=-0. 388, P=0. 046).

Cognitive impairment in PD patients may be closely associated with the change in CP volume, suggesting that the volume can serve as a potential imaging marker in assessment of cognitive impairment in PD patients.

To investigate age- and sex-related changes in choroid plexus (CP) volume in healthy adults, as well as its association with the volumes of other brain regions, and explore the relationship of CP volume changes with degenerative brain alterations.

A total of 320 healthy subjects aged between 18 and 85 years were prospectively recruited in Physical Examination Center of First Affiliated Hospital to Army Medical University during August 2023 and February 2024. These participants were randomly divided into 7.0T and 3.0T groups, with 160 people in each group. After all of them underwent sagittal three-dimensional structural MPRAGE scans of magnetic resonance imaging (MRI) at 3.0T or 7.0T, FreeSurfer 6.0 segmentation software was employed to obtain the volumes of CP and other brain regions automatically. Spearman analysis was applied to analyze the correlation of CP volume with age. Independent sample t-test analysis was applied to analyze the differences in CP volume between genders. Partial correlation analysis was performed to analyze the correlation between CP volume and the volumes of other brain regions.

A total of 311 subjects were included in the study. The results from both 3.0T and 7.0T MRI showed that CP volume was positively correlated with age (3.0T: r=0.462, P < 0.001; 7.0T: r=0.539, P < 0.001). The males had significantly larger CP volume than the females (3.0T: 1.4±0.47 vs 1.08±0.39 mL, P < 0.001; 7.0T: 2.43±0.68 vs 1.98±0.38 mL, P < 0.001). In addition, 3.0T MRI revealed there was a significant positive correlation of CP volume with the volumes of white matter hyperintensities (WMH) and cerebrospinal fluid (P < 0.001), as well as a negative correlation with the volumes of gray matter, white matter, hippocampus and thalamus (P < 0.05).

CP volume is increased with ageing, with gender differences, independent of field strength and resolution. CP volume is correlated with WMH, hippocampus and other brain regions, suggesting that increment in CP volume is involved in age-related degenerative changes in the brain. Changes in CP volume might be regarded as a new imaging marker for the neurodegenerative changes.

To develop a CT radiomics-based prediction model for prognosis of pancreatic ductal adenocarcinoma (PDAC) in order to provide evidence for individualized treatment decisions.

A retrospective study was carried on 118 PDAC patients admitted in the First Affiliated Hospital of Army Medical University between January 2020 and December 2023. They were assigned into a training group (n=83) and a validation group (n=35) at a 7∶3 ratio. ITK-SNAP software was used to perform 3-D segmentation on the preoperatively enhanced arterial phase CT images, and radiomic features were extracted using pyradiomics. High-reproducibility features were selected through ICC analysis (>0.85), and core features were determined using LASSO regression to construct the Rad-score. Cox regression analysis was employed to develop both a radiomics model and a model integrating radiomic and clinical features for predicting overall survival in PDAC patients. Receiver operating characteristic (ROC) curves and calibration curves were plotted to evaluate the prognostic models for survival prediction.

From 1453 extracted radiomic features, 7 core features were finally selected to construct the Rad-score. The radiomics prediction model based on the Rad-score achieved an AUC value of 0.796 (95%CI: 0.702~0.890) and 0.744 (95%CI: 0.589~0.899) for 1-year survival prediction in the training and validation groups, respectively. The integrated model combining 2 types of features together demonstrated improved performance with an AUC value of 0.906 (95%CI: 0.842~0.970) and 0.872 (95%CI: 0.753~0.992) in the 2 groups. Calibration curve analysis indicated good prediction accuracy for both models.

Both the CT radiomics-based model and the integrated model incorporating clinical features demonstrate good predictive performance for survival outcomes.