Objectives: To evaluate the effectiveness of radiomic features on classifying histological subtypes of central lung cancer in contrast-enhanced CT (CECT) images. Materials and Methods: A total of 200 patients with radiologically defined central lung cancer were recruited. All patients underwent dual-phase chest CECT, and the histological subtypes (adenocarcinoma (ADC), squamous cell carcinoma (SCC), small cell lung cancer (SCLC)) were confirmed by histopathological samples. 107 features were used in five machine learning classifiers to perform the predictive analysis among three subtypes. Models were trained and validated in two conditions: using radiomic features alone, and combining clinical features with radiomic features. The performance of the classification models was evaluated by the area under the receiver operating characteristic curve (AUC). Results: The highest AUCs in classifying ADC vs. SCC, ADC vs. SCLC, and SCC vs. SCLC were 0.879, 0.836, 0.783, respectively by using only radiomic features in a feedforward neural network. Conclusion: Our study indicates that radiomic features based on the CECT images might be a promising tool for noninvasive prediction of histological subtypes in central lung cancer and the neural network classifier might be well-suited to this task.

Purpose: Systemic lupus erythematosus (SLE) frequently affects the central nervous system, likely via neuroimmune interfaces; how these interfaces respond longitudinally to standard therapy remains unclear. This study aimed to clarify these therapeutic dynamics. Methods: This retrospective longitudinal study included 63 women with SLE who had brain MRI at baseline and after standard therapy. Enlarged perivascular spaces (ePVS), choroid plexus (CP), and gray matter (GM) volumes were segmented from 3D T1-weighted images; the diffusion tensor imaging along the perivascular space (DTI-ALPS) index was computed from DTI scans. Paired t-tests compared baseline and post-therapy values. Patients were grouped into neuropsychiatric SLE (NPSLE) and non-NPSLE groups, and longitudinal deltas compared. Spearman correlations assessed associations of MRI changes with clinical measures. Results: After standard therapy, all 63 patients showed clinical improvement (decreased SLEDAI-2K, increased MoCA scores). Left DTI-ALPS index increased (T = -2.475, P _FDR = 0.048), and bilateral CP volumes decreased (left: T = 2.432, P _FDR = 0.048; right: T = 3.330, P _FDR = 0.006). ePVS volumes remained unchanged. No significant correlations emerged between MRI changes and clinical scores, and improvement did not differ between NPSLE and non-NPSLE subgroups. Conclusions: Longitudinal MRI identified measurable alterations in Neuroimmune Interfaces in women with SLE, particularly the DTI-ALPS index and CP volume. These changes correlate with clinical improvement and serve as supplementary imaging biomarkers for assessing disease activity, independent of neuropsychiatric involvement.