Background: Lung is the most common primary site of brain metastases (BMs). For different pathological types of BMs have some similar characteristics, it is still a challenge to confirm the origin based on their characteristics directly. BMs of small cell lung cancer (SCLC) have favorable therapeutic expectations due to their high sensitivity to radiotherapy. This study sought to identify unique characteristics of BMs in SCLC, aiming to assist in clinical decision‐making.Methods: Patients diagnosed with BMs of lung cancer who received radiotherapy from January 2017 to January 2022 were reviewed (N = 284). Definitive diagnosis of BMs of SCLC was reached for 36 patients. All patients underwent head examination using magnetic resonance imaging. The number, size, location, and signal characteristics of lesions were analyzed.Results: There were 7 and 29 patients with single focus and non‐single focus, respectively. Ten patients had diffuse lesions, and the remaining 26 patients had a total of 90 lesions. These lesions were divided into three groups according to size: 3 cm (43.33%, 53.34%, and 3.33%, respectively). Sixty‐six lesions were located in the supratentorial area, primarily including cortical and subcortical lesions (55.56%) and deep brain lesions (20%). Moreover, 22 lesions were located in the infratentorial area. According to diffusion‐weighted imaging and T1‐weighted contrast enhancement, the imaging characteristics were classified into six patterns. Hyperintensity in diffusion‐weighted imaging and homogeneous enhancement was the most common pattern of BMs in SCLC (46.67%), while partial lesions showed hyperintensity in diffusion‐weighted imaging without enhancement (7.78%).Conclusions: The manifestations of BMs in SCLC were multiple lesions (diameter: 1–3 cm), hyperintensity in diffusion‐weighted imaging, and homogeneous enhancement. Interestingly, hyperintensity in diffusion‐weighted imaging without enhancement was also one of the characteristics.

Background: Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease. Current gold standard criteria, pulmonary function tests (PFTs) may result in underdiagnosis of potential COPD patients. Therefore, we hypothesize that the combination of high-resolution computed tomography (HRCT) and clinical basic characteristics will enable the identification of more COPD patients. Methods: A total of 284 patients with respiratory symptoms who were current or former smokers were included in the study, and were further divided into 5 groups of GOLD grade I–IV and non-COPD according to PFTs. All patients underwent inspiratory HRCT scanning and low attenuation area (LAA) was measured. Then they were divided into seven visual subtypes according to the Fleischner Society classification system. Non-parametric tests were used for exploring differences in basic characteristics and PFTs between different groups of enrolled patients and visual subtypes. Binary logistic regression was to find the influencing factors that affected the patients’ outcome (non-COPD vs GOLD I-IV). The area under the receiver operating characteristic curve (AUC-ROC) was to explore the diagnostic efficacy of LAA, visual subtypes, and combined basic characteristics related to COPD for COPD diagnosis. Finally, based on the cut-off values of ROC analysis, exploring HRCT features in patients who do not meet the diagnostic criteria but clinically suspected COPD. Results: With the worsening severity of COPD, the visual subtypes gradually progressed (p < 0.01). There was a significant difference in LAA between GOLD II–IV and non-COPD (p < 0.0001). The diagnostic efficacy of LAA, visual subtypes, and LAA combined with visual subtypes for COPD were 0.742, 0.682 and 0.730 respectively. The diagnostic efficacy increased to 0.923–0.943 when basic characteristics were added (all p < 0.001). Based on the cut-off value of ROC analysis, LAA greater than 5.6, worsening of visual subtypes, combined with positive basic characteristics can help identify some potential COPD patients. Conclusion: The heterogeneous phenotype of COPD requires a combination of multiple evaluation methods. The diagnostic efficacy of combining LAA, visual subtypes, and basic characteristics achieves good consistency with current diagnostic criteria.

Background & Aims Enhanced computed tomography (CT) is the primary method for focal liver lesion diagnosis. We aimed to use automated machine learning (AutoML) algorithms to differentiate between benign and malignant focal liver lesions on the basis of radiomics from unenhanced CT images. Methods We enrolled 260 patients from 2 medical centers who underwent CT examinations between January 2017 and March 2023. This included 60 cases of hepatic malignancies, 93 cases of hepatic hemangiomas, 48 cases of hepatic abscesses, and 84 cases of hepatic cysts. The Pyradiomics method was used to extract radiomics features from unenhanced CT images. By using the mljar-supervised (MLJAR) AutoML framework, clinical, radiomics, and fusion models combining clinical and radiomics features were established. Results In the training and validation sets, the area under the curve (AUC) values for the clinical, radiomics, and fusion models exceeded 0.900. In the external testing set, the respective AUC values for the clinical, radiomics, and fusion models were as follows: 0.88, 1.00, and 1.00 for hepatic cysts; 0.81, 0.90, and 0.97 for hepatic hemangiomas; 0.89, 0.98, and 0.92 for hepatic abscesses; and 0.23, 0.80, and 0.93 for hepatic malignancies. The diagnostic accuracy rates for hepatic cysts, hemangiomas, malignancies, and abscesses by radiologists in the external testing cohort were 0.96, 0.60, 0.79, and 0.66, respectively. Conclusion The fusion model based on noninvasive radiomics and clinical features of unenhanced CT images has high clinical value for distinguishing focal hepatic lesions.

Background: Mild cognitive impairment (MCI) is a transitional stage between subjective cognitive decline and Alzheimer’s dementia, representing a critical window for intervention. We characterize the small-world properties of brain networks in MCI to identify sensitive biomarkers for early detection and assessment. Methods: Thirty-one patients diagnosed with MCI were recruited as the experimental group, while 30 healthy elderly individuals served as the normal control (NC) group. Based on diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI), small-world properties of the brain networks were observed using graph theory analysis. Global and nodal properties were computed to assess differences in brain network topology. Results: Both structural and functional brain networks in the MCI and NC groups exhibited small-world properties (σ>1), and significant differences were noted in nodal properties such as nodal efficiency, nodal degree centrality, and nodal shortest path length (P<0.05). Importantly, these nodal properties in brain regions were significantly correlated with Montreal Cognitive Assessment (MoCA) scores in patients with MCI (P<0.05). Conclusions: Patients with MCI exhibit small-world properties in their brain networks, suggesting preserved efficiency of information transfer. Node property metrics in regions such as the posterior cingulate cortex, prefrontal cortex, and occipital lobe are promising biomarkers for early detection of MCI.