BACKGROUND: Lung cancer is one of the most common malignant tumors worldwide and a major cause of cancer-related deaths. Early-stage lung cancer is often manifested as pulmonary nodules, and accurate assessment of the malignancy risk is crucial for prolonging survival and avoiding overtreatment. This study aims to construct a model based on image feature parameters automatically extracted by artificial intelligence (AI) to evaluate its effectiveness in predicting the malignancy of part-solid nodule (PSN). METHODS: This retrospective study analyzed 229 PSN from 222 patients who underwent pulmonary nodule resection at Lanzhou University Second Hospital between October 2020 and February 2025. According to pathological results, 45 cases of benign lesions and precursor glandular lesion were categorized into the non-malignant group, and 184 cases of pulmonary malignancies were categorized into the malignant group. All patients underwent preoperative chest computed tomography (CT), and AI software was used to extract imaging feature parameters. Univariate analysis was used to screen significant variables; variance inflation factor (VIF) was calculated to exclude highly collinear variables, and LASSO regression was further applied to identify key features. Multivariate Logistic regression was used to determine independent risk factors. Based on the selected variables, five models were constructed: Logistic regression, random forest, XGBoost, LightGBM, and support vector machine (SVM). Receiver operating characteristic (ROC) curves were used to assess the performance of the models. RESULTS: The independent risk factors for the malignancy of PSN include roughness (ngtdm), dependence variance (gldm), and short run low gray-level emphasis (glrlm). Logistic regression achieved area under the curves ( AUCs) of 0.86 and 0.89 in the training and testing sets, respectively, showing good performance. XGBoost had AUCs of 0.78 and 0.77, respectively, demonstrating relatively balanced performance, but with lower accuracy. SVM showed an AUC of 0.93 in the training set, which decreased to 0.80 in the testing set, indicating overfitting. LightGBM performed excellently in the training set with an AUC of 0.94, but its performance declined in the testing set, with an AUC of 0.88. In contrast, random forest demonstrated stable performance in both the training and testing sets, with AUCs of 0.89 and 0.91, respectively, exhibiting high stability and excellent generalizability. CONCLUSIONS The random forest model constructed based on independent risk factors demonstrated the best performance in predicting the malignancy of PSN and could provide effective auxiliary predictions for clinicians, supporting individualized treatment decisions.
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Humans ; Male ; Female ; Lung Neoplasms/pathology* ; Middle Aged ; Retrospective Studies ; Artificial Intelligence ; Aged ; Tomography, X-Ray Computed ; Adult ; Solitary Pulmonary Nodule/diagnostic imaging* ; ROC Curve

Objective To investigate the effect of percutaneous balloon compression on the ocular surface in the treatment of trigeminal neuralgia (TN). Methods A total of 30 patients (60 eyes) diagnosed with TN who underwent parallel balloon compression surgery from May 2023 to June 2024 were included in the study. Corneal sensation,Schirmer Ⅰ test results,tear film break-up time (TBUT),and corneal fluorescein staining scores were evaluated in both eyes one day before and one day after the surgery. Results The tear secretion values and TBUT in both eyes of TN patients were lower than normal. Preoperatively,there were no statistically significant differences in various ocular examinations between the two eyes (P=0.144,P=0.072,P=0.069,P=1.000). One day postop-eratively,the corneal sensitivity,tear secretion value,and TBUT of the affected eye were significantly lower com-pared to the contralateral eye (P<0.01,P<0.01,P<0.01),while the fluorescein staining score showed a slight increase but was not statistically significant (P=0.157). Compared to preoperative levels,the postoperative corneal sensitivity,tear secretion value,and TBUT of the affected side were significantly reduced (P<0.01,P<0.01,P<0.01),while the fluorescein staining score showed a slight increase but remained non-significant (P=0.157). Conclusions TN patients patients exhibit reduced bilateral tear secretion and compromised tear film stability. Following balloon compression surgery,the corneal sensitivity of the affected eye diminishes,leading to a further decline in tear secretion and tear film stability. Consequently,it is imperative that TN patients receive ophthalmic intervention,treatment,and regular follow-up,irrespective of whether they undergo surgery.

Objective To evaluate the predictive ability and clinical application value of artificial intelligence (AI) systems in the benign and malignant differentiation and pathological type of pulmonary nodules, and to summarize clinical application experience. Methods A retrospective analysis was conducted on the clinical data of patients with pulmonary nodules admitted to the Department of Thoracic Surgery, Second Hospital of Lanzhou University, from February 2016 to February 2025. Firstly, pulmonary nodules were divided into benign and non-benign groups, and the discriminative abilities of AI systems and clinicians were compared. Subsequently, lung nodules reported as precursor glandular lesions (PGL), microinvasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) in postoperative pathological results were analyzed, comparing the efficacy of AI systems and clinicians in predicting the pathological type of pulmonary nodules. Results In the analysis of benign/non-benign pulmonary nodules, clinical data from a total of 638 patients with pulmonary nodules were included, of which there were 257 males (10 patients and 1 patient of double and triple primary lesions, respectively) and 381 females (18 patients and 1 patient of double and triple primary lesions, respectively), with a median age of 55.0 (47.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis of the two groups of variables showed that, except for nodule location, the differences in the remaining variables were statistically significant (P<0.05). Multivariate logistic regression analysis showed that age, nodule type (subsolid pulmonary nodule), average density, spicule sign, and vascular convergence sign were independent influencing factors for non-benign pulmonary nodules, among which age, nodule type (subsolid pulmonary nodule), spicule sign, and vascular convergence sign were positively correlated with non-benign pulmonary nodules, while average density was negatively correlated with the occurrence of non-benign pulmonary nodules. The area under the receiver operating characteristic curve (AUC) of the malignancy risk value given by the AI system in predicting non-benign pulmonary nodules was 0.811, slightly lower than the 0.898 predicted by clinicians. In the PGL/MIA/IAC analysis, clinical data from a total of 411 patients with pulmonary nodules were included, of which there were 149 males (8 patients of double primary lesions) and 262 females (17 patients of double primary lesions), with a median age of 56.0 (50.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis results showed that, except for gender, nodule location, and vascular convergence sign, the differences in the remaining variables among the three groups of PGL, MIA, and IAC patients were statistically significant (P<0.05). Multinomial multivariate logistic regression analysis showed that the differences between the parameters in the PGL group and the MIA group were not statistically significant (P>0.05), and the maximum diameter and average density of the nodules were statistically different between the PGL and IAC groups (P<0.05), and were positively correlated with the occurrence of IAC as independent risk factors. The average AUC value, accuracy, recall rate, and F1 score of the AI system in predicting lung nodule pathological type were 0.807, 74.3%, 73.2%, and 68.5%, respectively, all better than the clinical physicians’ prediction of lung nodule pathological type indicators (0.782, 70.9%, 66.2%, and 63.7% respectively). The AUC value of the AI system in predicting IAC was 0.853, and the sensitivity, specificity, and optimal cutoff value were 0.643, 0.943, and 50.0%, respectively. Conclusion This AI system has demonstrated high clinical value in predicting the benign and malignant nature and pathological type of lung nodules, especially in predicting lung nodule pathological type, its ability has surpassed that of clinical physicians. With the optimization of algorithms and the adequate integration of multimodal data, it can better assist clinical physicians in formulating individualized diagnostic and treatment plans for patients with lung nodules.

Objective To investigate the effect of percutaneous balloon compression on the ocular surface in the treatment of trigeminal neuralgia (TN). Methods A total of 30 patients (60 eyes) diagnosed with TN who underwent parallel balloon compression surgery from May 2023 to June 2024 were included in the study. Corneal sensation,Schirmer Ⅰ test results,tear film break-up time (TBUT),and corneal fluorescein staining scores were evaluated in both eyes one day before and one day after the surgery. Results The tear secretion values and TBUT in both eyes of TN patients were lower than normal. Preoperatively,there were no statistically significant differences in various ocular examinations between the two eyes (P=0.144,P=0.072,P=0.069,P=1.000). One day postop-eratively,the corneal sensitivity,tear secretion value,and TBUT of the affected eye were significantly lower com-pared to the contralateral eye (P<0.01,P<0.01,P<0.01),while the fluorescein staining score showed a slight increase but was not statistically significant (P=0.157). Compared to preoperative levels,the postoperative corneal sensitivity,tear secretion value,and TBUT of the affected side were significantly reduced (P<0.01,P<0.01,P<0.01),while the fluorescein staining score showed a slight increase but remained non-significant (P=0.157). Conclusions TN patients patients exhibit reduced bilateral tear secretion and compromised tear film stability. Following balloon compression surgery,the corneal sensitivity of the affected eye diminishes,leading to a further decline in tear secretion and tear film stability. Consequently,it is imperative that TN patients receive ophthalmic intervention,treatment,and regular follow-up,irrespective of whether they undergo surgery.

BACKGROUND: Lung cancer is the cancer with the highest mortality at home and abroad at present. The detection of lung nodules is a key step to reducing the mortality of lung cancer. Artificial intelligence-assisted diagnosis system presents as the state of the art in the area of nodule detection, differentiation between benign and malignant and diagnosis of invasive subtypes, however, a validation with clinical data is necessary for further application. Therefore, the aim of this study is to evaluate the effectiveness of artificial intelligence-assisted diagnosis system in predicting the invasive subtypes of early‑stage lung adenocarcinoma appearing as pulmonary nodules. METHODS: Clinical data of 223 patients with early-stage lung adenocarcinoma appearing as pulmonary nodules admitted to the Lanzhou University Second Hospital from January 1st, 2016 to December 31th, 2021 were retrospectively analyzed, which were divided into invasive adenocarcinoma group (n=170) and non-invasive adenocarcinoma group (n=53), and the non-invasive adenocarcinoma group was subdivided into minimally invasive adenocarcinoma group (n=31) and preinvasive lesions group (n=22). The malignant probability and imaging characteristics of each group were compared to analyze their predictive ability for the invasive subtypes of early-stage lung adenocarcinoma. The concordance between qualitative diagnostic results of artificial intelligence-assisted diagnosis of the invasive subtypes of early-stage lung adenocarcinoma and postoperative pathology was then analyzed. RESULTS: In different invasive subtypes of early-stage lung adenocarcinoma, the mean CT value of pulmonary nodules (P<0.001), diameter (P<0.001), volume (P<0.001), malignant probability (P<0.001), pleural retraction sign (P<0.001), lobulation (P<0.001), spiculation (P<0.001) were significantly different. At the same time, it was also found that with the increased invasiveness of different invasive subtypes of early-stage lung adenocarcinoma, the proportion of dominant signs of each group gradually increased. On the issue of binary classification, the sensitivity, specificity, and area under the curve (AUC) values of the artificial intelligence-assisted diagnosis system for the qualitative diagnosis of invasive subtypes of early-stage lung adenocarcinoma were 81.76%, 92.45% and 0.871 respectively. On the issue of three classification, the accuracy, recall rate, F1 score, and AUC values of the artificial intelligence-assisted diagnosis system for the qualitative diagnosis of invasive subtypes of early-stage lung adenocarcinoma were 83.86%, 85.03%, 76.46% and 0.879 respectively. CONCLUSIONS Artificial intelligence-assisted diagnosis system could predict the invasive subtypes of early‑stage lung adenocarcinoma appearing as pulmonary nodules, and has a certain predictive value. With the optimization of algorithms and the improvement of data, it may provide guidance for individualized treatment of patients.
Adenocarcinoma/pathology* ; Adenocarcinoma of Lung/pathology* ; Artificial Intelligence ; Humans ; Lung Neoplasms/pathology* ; Multiple Pulmonary Nodules ; Neoplasm Invasiveness ; Retrospective Studies

Background and purpose:For stageⅠ non-small cell lung cancer (NSCLC), video-assisted thoracic segmentectomy is given much attention to by thoracic surgeon because of the less tissue damages. However, video-assisted thoracic lobectomy is still considered as the standard treatment in the world. Therefore, this study was to evaluate the clinical effect after video-assisted thoracic segmentectomy and lobectomy in patients with stageⅠ NSCLC in order to provide reference for clinical application.Methods:The comparative studies on video-assisted thoracic segmentectomy and lobectomy treating stage I NSCLC were retrieved from PubMed, Web of Science, EMBASE, the Cochrane Library, CNKI, CBM, VIP, and Wanfang Data. All data were acquired until July 2015. Literature screening according to data extraction and quality assessment was completed by two reviewers independently. Meta-analysis was conducted by RevMan 5.3 software which was offered by Cochrane network.Results:A total of 11 articles involving 1 677 patients were ifnally included. The results of meta-analysis indicated that: for stageⅠ NSCLC, compared with video-assisted thoracic lobectomy, the effect of video-assisted thoracic segmentectomy was alike in total mortality (OR=0.77, 95%CI: 0.48 to 1.21,P=0.25), 5-year mortality (OR=0.77, 95%CI: 0.52 to 1.14,P=0.19) and systemic complications (OR=0.76, 95%CI: 0.53 to 1.09,P=0.13), but could reduce blood loss [difference in means (MD)=-41.16, 95%CI: -59.46 to -22.86,P<0.000 1], chest tube duration (MD=-0.29, 95%CI: -0.49 to -0.09,P=0.005) and the length of hospital stay (MD=-0.74, 95%CI: -1.44 to -0.05,P=0.04).Conclusion:Compared with video-assisted thoracic lobectomy, video-assisted thoracic segmentectomy can signiifcantly reduce blood loss, chest tube duration and length of hospital stay. However, the two kinds of operation methods achieved the same effects on the total mortality, 5-year mortality and systemic complications. Thoracoscopic segmentectomy may be an alternative to thoracic lobectomy.